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\begin{document}
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\title{Threshold cointegration: overview and implementation in R}


\author{Matthieu Stigler\\
\date{January 10, 2010}
\textsf{\small Matthieu.Stigler at gmail.com}}

\maketitle
\begin{center}
(Revision 5: April 2013)
\par\end{center}
\begin{abstract}
Purpose of this paper is twofold. It is first to offer a rough overview
on the field of threshold cointegration, from the seminal paper of
\citet{BalkeFomby1997} to the recent developments. Simultaneously,
it is to describe the implementation of the main functionalities for
the modeling in the open-source package \tsDyn. It provides hence
a unique way to get an introduction on the threshold cointegration
field allowing in the same time to conduct its own analysis. 

Introduced by \citet{EngleGranger1987}, the concept of cointegration
became a indispensable step in the analysis of non stationary time
series. The underlying idea is that even if two variables (or more)
are non-stationary, there can exist a combination of them which is
stationary. This definition leads to interesting interpretations as the variables can then be interpreted to have a stable relationship (a long-run equilibrium), can be represented in an vector error-correction model, and share a common stochastic trend. 

However, implicit in the definition is the idea that every small deviation
from the long-run equilibrium will lead instantaneously to error correction mechanisms.
Threshold cointegration extends the linear cointegration case by allowing
the adjustment to occur only after the deviation exceed some critical
threshold, thus taking into account possibly transaction costs or
stickiness of the prices. Furthermore, it allows to capture asymmetries
in the adjustment, where positive or negative deviations won't be
corrected in the same manner. 
\end{abstract}
\begingroup \hypersetup{linkcolor=black}
\tableofcontents{}\pagebreak{}
\endgroup

\section{Introduction: linear cointegration}


\paragraph{On stationarity}

When dealing with time series, a main concern for statistical analysis
is stationarity, the usual inference being based on that assumption.
In its weak version, stationarity is defined as the finiteness and
time-invariance of the expectation, variance and auto-covariance of
a series. However, there are considerable theoretical \citep{Samuelson1965}
and empirical \citep{NelsonPlosser1982} arguments
in favor of non-stationarity of economic series, especially for the
difference-stationary type. A difference-stationary (or \emph{integrated})
series is defined as a series that is non-stationary, but whose difference
is stationary, as the random walk is. Great care should be taken
when analyzing such series as they follow a different asymptotic behavior
and particularly, regression among integrated series leads to a so-called
spurious regression, i.e. inflation of regression indicators (t-tests,
$R^{2}$) which lead to the false conclusion of statistical dependence
between the series (Granger and Newbold \citeyear{GrangerNewbold1974},
Philipps \citeyear{Phillips1986}). An obvious remedy is to use differenced
series, for which usual asymptotics apply. This approach has become
the standard in the VAR framework popularized by \citet{Sims1980}. 


\paragraph{Cointegration}

Granger introduced in 1982 the concept of cointegration, which means
integrated series for which a linear combination exists that is stationary.
This can be interpreted economically as the presence of a long-run
equilibrium, the relationship between the variables being stable.
The concept gained a significant interest with the so-called Granger
representation theorem, which states that cointegrated variables have
a vector error correction model (VECM) representation, that can be
seen as a VAR model including a variable representing the deviations
from the long-run equilibrium. Equation\ref{eq:VECM2} shows a VECM
for two variables including a constant, the error-correction term
and a lag.

\begin{equation}
\begin{bmatrix}\Delta X_{t}\\
\Delta Y_{t}\end{bmatrix}=\begin{bmatrix}c_{1}\\
c_{2}\end{bmatrix}+\begin{bmatrix}a_{1}\\
a_{2}\end{bmatrix}ECT_{-1}+\begin{bmatrix}b_{11} & b_{12}\\
b_{21} & b_{22}\end{bmatrix}\begin{bmatrix}\Delta X_{t-1}\\
\Delta X_{t-1}\end{bmatrix}+\begin{bmatrix}\varepsilon_{t}^{X}\\
\varepsilon_{t}^{Y}\end{bmatrix}\qquad ECT_{-1}=(1,-\beta)\begin{bmatrix}X_{t-1}\\
Y_{t-1}\end{bmatrix}\label{eq:VECM2}\end{equation}


This VECM representation is particularly interesting as it allows
to estimate how the variables adjust deviations towards the long-run
equilibrium, to test for Granger-causality as well as to determine
the impacts of shocks to the variables using impulse response functions.
In a system with more than $k>2$ variables, there may exist $k-1$
cointegrating relationships, hence the vector A of adjustment speed
parameters (also called loading matrix) and the vector B of cointegrating
values become matrices%
\footnote{of dimension $r\times k$, with $r$ the number of cointegrating values
and $k$ the number of variables%
}. The matrix of their product, corresponding to the parameters of
the lagged vector, is singular with rank equal to the number of cointegrating
relationships.

Two methods captured the main attention and are of popular use now.
The first one was advocated by Engle and Granger (hereafter E-G),
who propose a two-steps approach, estimating the cointegrating values
in the long-run representation and then plugging those estimates one
the VECM representation%
\footnote{This two-step approach has been justified afterwards by the fact that
the estimator in the first step is super-consistent, i.e. converging
to its true value at rate $n$ instead of usual rate $\sqrt{n}$ \citep{Stock1987}}. 
The related testing procedure taking absence of cointegration as
a null hypothesis consists in determining whether the residuals from
the first step are stationary or not. Rejection of the stationarity
is then interpreted as the rejection of the null hypothesis of cointegration.
When the cointegrating vector is known, usual unit root tests can
be applied, whereas in case it is unknown, different critical values
need to be used. Philipps and Ouliaris \citeyearpar{PhillipsOuliaris1990}
developed a test that is invariant to the normalization available
in the software R in package \pkg{urca} (\citealt{PfaffBook2008}). 


\paragraph{Estimation and testing}

A major drawback of the E-G approach is that is allows to estimate
and test only for one cointegrating relationship. When the cointegrating
vectors are known, estimation of multiple cointegration relationship
is trivial as the estimation is simply OLS regression for the VECM
and testing can be done using classical Wald tests (Horvath and Watson
\citeyear{HorvathWatson1995}). When these vectors are unknown, the
reduced-rank approach adopted by Johansen (1998, 1995) is able to
estimate the potential cointegrating vectors and to test for their
significance, allowing to determine the number of cointegration relationships.
This is available in R with the \code{ca.jo()} function from package \pkg{vars}
(\citealt{Pfaff2008vars}). 


\section{The extension to threshold cointegration}

\citet{BalkeFomby1997} note that in the concept
of cointegration there is the implicit assumption that the adjustment
of the deviations towards the long-run equilibrium is made instantaneously
at each period. There are nevertheless serious arguments in economic
theory to invalidate this assumption of linearity. Among them, the
presence of transaction costs is maybe the most noteworthy, as it
implies that adjustment will occur only once deviations are higher
than the transactions costs, and hence adjustment should not happen
instantaneously and at each time. Financial theory predicts that even
in highly liquid markets a so-called band of no arbitrage may exist
where deviations are too small for the arbitrage to be profitable.
In the domain of macroeconomics, policies are often organized around
targets, where intervention is activated only once the deviations
from the target are significant, the most famous example being the
monetary policy management during the Bretton Woods agreement where
central banks pegged their exchange rate and allowed a +/- 1 \% band.


A second range of arguments that were raised in favour of nonlinear
adjustment concerns the assumption of symmetry. In the linear cointegration
context, increases or decreases of the deviations are deemed to be
corrected in the same way. Again, several theoretical arguments may
contest this assumption, such as the presence of menu costs (
\citealp{Levy}, \citealp{Dutta}),  market power (\citealp{DamaniaYang1998}, \citealp{Ward}) or simply
small country vs rest of the world effects. 

Balke and Fomby (1997) introduced the concept of threshold cointegration,
which allows to take into consideration the two main criticisms (though
BF were concerned only with the first one) raised against linear cointegration.
In their framework, the adjustment does not need to occur instantaneously
but only once the deviations exceed some critical threshold, allowing
thus the presence of an inaction or no-arbitrage band. They base their
adjustment process on the self-exciting threshold autoregressive model
(SETAR%
\footnote{But they call this model TAR, which is a more general form presented
later.%
}) introduced by Chan (1983) and discussed extensively in Tong (\citeyear{Tong1990}).
In the SETAR model, the autoregressive coefficients take different
values depending on whether the previous value is above or under a
certain threshold value, thus exhibiting regime switching dynamics.
Hence, the linear adjustment process: 

\begin{equation}
\varepsilon_{t}=\rho\varepsilon_{t-1}+u_{t}\label{eq:AR}\end{equation}


is extended as:

\begin{equation}
\varepsilon_{t}=\left\{ \begin{array}{lll}
\rho_{L}\varepsilon_{t-1}+u_{t} & \mbox{if} & \varepsilon_{t-1}\leq\theta_{L}\\
\rho_{M}\varepsilon_{t-1}+u_{t} & \mbox{if} & \theta_{L}\leq\varepsilon_{t-1}\leq\theta_{H}\\
\rho_{H}\varepsilon_{t-1}+u_{t} & \mbox{if} & \theta_{H}\leq\varepsilon_{t-1}\end{array}\right.\label{eq:TAR}\end{equation}
 

This is actually a piecewise linear model where three different AR(1)
processes are estimated depending on the state of the variable at
time $t-1$. Autoregressive parameters are denoted with subscript
$L$, $M$ and $H$ standing for Low, Middle and High regime, and
they differ whether the variable was below the lower threshold $\theta_{L}$,
between the lower $\theta_{L}$ and upper $\theta_{H}$ threshold,
or above the higher $\theta_{H}$. This leads to some further remarks:
\begin{itemize}
\item The threshold effect is present when $\rho_{H}\neq\rho_{M}$and $\rho_{L}\neq\rho_{M}$
and as long as $0<P(\epsilon<\theta_{b})<1\quad\text{where }b=L\text{ or }H$
\item The SETAR model nest the AR when $\rho_{H}=\rho_{M}=\rho_{L}$. 
\item The model can easily by extended by adding lags in each regime, as
well as intercepts. 
\item Several restricted models have been proposed, the main restriction
being that the outer regimes are symmetric ($\theta_{L}=\theta_{M}$
along with $\rho_{H}=\rho_{L}$).
\end{itemize}
While the work of Balke and Fomby (1997) focused on the long-run relationship
representation, extension to a threshold VECM (TVECM) has been made
by several authors, the threshold effect being applied the anticipation
by the agents of interventionary policy only to the error-correction
term (Granger and Lee \citeyear{GrangerLee1989}, Seo \citeyear{Seo2006})
or to the lags and the intercept as well (Hansen and Seo \citeyear{HanSeo2002},
Lo and Zivot \citeyear{LoZivot2001} ). 

\begin{equation}
\begin{bmatrix}\Delta X_{t}\\
\Delta Y_{t}\end{bmatrix}=\begin{bmatrix}c^{X}\\
c^{Y}\end{bmatrix}+\left\{ \begin{array}{l}
\begin{bmatrix}a_{XL}\\
a_{YL}\end{bmatrix}ECT_{L,t-1}\\
\begin{bmatrix}a_{XM}\\
a_{YM}\end{bmatrix}ECT_{M,t-1}\\
\begin{bmatrix}a_{XH}\\
a_{YH}\end{bmatrix}ECT_{H,t-1}\end{array}\right.+\begin{bmatrix}b_{11} & b_{12}\\
b_{21} & b_{22}\end{bmatrix}\begin{bmatrix}\Delta X_{t-1}\\
\Delta Y_{t-1}\end{bmatrix}+\begin{bmatrix}\varepsilon_{t}^{X}\\
\varepsilon_{t}^{Y}\end{bmatrix}\label{eq:TVECM}\end{equation}


In this model, the error-correction term is split into three regimes,
lower (L), middle (M) and high (H) depending on whether it is below,
between or above the two thresholds $\theta_{L}$ and $\theta_{H}$,
as in \ref{eq:TAR}.

Note that speaking of threshold cointegration in the case of the TVECM
was rather a conjecture as no formal representation theorem had been
demonstrated in the threshold case. Nevertheless, the intuition of
these authors was justified as Krishnakumar and Netto (2009) derived
recently such a theorem, under the assumption that the loading matrix
(the matrix of coefficients a in \ref{eq:TVECM}) is diagonal. An
interesting conclusion of their theorem is that the threshold effect
is present on the lags also only if the residuals from the LR follow
a multivariate SETAR process with one lag in each regime. When the
residuals follow a multivariate SETAR process with p >1 lags, the
lags in the TVECM do also have regime-specific components.

Several other model specifications have been used in the literature.
Gonzalo and Pittarakis\citeyearpar{GonzaloPitarakis2006a} and Krischnakumar
and Netto (2009) use as transition variable not the deviations itself,
but an external variable (that is, a TAR model, as we will see later).
This has important implications as the stationarity conditions are
different, estimation is much easier, and testing is more restrictive%
\footnote{Current tests work only with a stationary external variable. %
}. In that sense, it should be clearly differentiated from threshold
cointegration as introduced by BF and I will denote it by cointegration
with threshold effects. In my opinion, this approach is less attractive
as it lets unanswered the question why an influencing variable is
not included in the VECM. 

All previous studies were based on the definition of threshold cointegration
as a case where the variables are {}``linear'' and the combination
is linear, whereas the adjustment process exhibits threshold effects.
Gonzalo and Pittarakis (\citeyear{GonzaloPitarakis2006}) take an
opposite direction where the cointegrating relationship exhibits threshold
effects whereas the adjustment is linear%
\footnote{For an analogous case in the structural break literature, see Gregory
and Hansen \citeyearpar{GreHan}%
}. That is, $y_{t}$ is I(1) and $x_{t}$ follows a SETAR process,
but there exists a linear combination of them which is stationary.
Note that in that case, the notion of integratedness is undefined
and there is no corresponding VECM representation (Gonzalo and Pittarakis
\citeyear{GonzaloPitarakis2006a}). 


\paragraph{Empirical applications}

Since the seminal work of Balke and Fomby, threshold cointegration
has become widely applied in various contexts. The law of one price
(LOP) and the associated purchasing power parity (PPP) hypothesis
represent maybe the field where the greatest number of studies has
been conducted (see for a review on LOP Lo and Zivot, for the PPP,
Gouveia and Rodrigues \citeyear{GouveiaRodrigues2004}, Heimonen \citeyear{Heimonen2006},
Bec et al. 2004). Numerous studies on price transmission of agricultural
products or other commodities (principally oil) use the threshold
cointegration framework. In the field of the term interest theory,
threshold cointegration methods have been developed and applied by
Enders and Granger (1998), Enders and Siklos (2001), Bec et al. (2008),
Krishnakumar and Netto (2009). Other fields include the Fisher effect
(Million \citeyear{Million2004}), financial integration based on
comparing local and the US stock markets (Jawadi et al. \citeyear{JawadiMillionEtAl2009}),
exchange rate pass-through (\citealt{Al-AbriGoodwin2009}). To my
knowledge, however, the use of threshold cointegration remained within
the economical literature and no study has been done on other domains. 


\section{The TAR model: probabilistic structure}

The Balke and Fomby approach was based on the use of the SETAR model
developed by Chan and Tong associated to cointegration. The SETAR
is actually a particular case of the more general TAR model that can
be written as:

\begin{equation}
y_{t}=\left\{ \begin{array}{lll}
\mu_{1}+\rho_{1,1}y_{t-1}+\ldots+\rho_{1,p1}y_{t-p1}+\varepsilon_{t} & \mbox{if} & x_{t-d}\geq\theta_{m-1}\\
\mu_{2}+\rho_{2,1}y_{t-1}+\ldots+\rho_{2,p2}y_{t-p2}+\varepsilon_{t} & \mbox{if} & \theta_{m-1}\geq x_{t-d}\geq\theta_{m-2}\\
\ldots & \mbox{if} & \theta_{...}\geq x_{t-d}\geq\theta_{...}\\
\mu_{m}+\rho_{m,1}y_{t-1}+\ldots+\rho_{m,pm}y_{t-pm}+\varepsilon_{t} & \mbox{if} & \theta_{1}\geq x_{t-d}\end{array}\right.\label{eq:genTAR}\end{equation}


This model has several parameters:
\begin{itemize}
\item $m$: the number of regimes
\item $\mu_{1}\ldots\mu_{m}$: the intercepts in each regime
\item $p_{j,1}\ldots p_{j,m-1}$: the number of lags in regime $j$
\item $\theta_{1}\ldots\theta_{m-1}$: the thresholds
\item $d$: the delay of the transition variable
\item $x_{t-d}$: the transition variable
\end{itemize}
When used in the framework of cointegration, BF used a simplified
form of \ref{eq:genTAR} by taking lagged values as the transition
variable (i.e. setting $x_{t-d}=y_{t-d}$), which leads to she called
self-exciting threshold autoregressive model (SETAR), that they nevertheless
called simply TAR. They furthermore set the delay value to $d=1$,
as it then corresponds to the delay of the error-correction term.
Note that some authors don't take as transition variable the deviations
from equilibrium but rather an external variable%
\footnote{Or the differences of a single variable included in the process, as
in Krischnakumar and Netto (2009).%
}, i.e. they use a TAR model. 

The theoretically unlimited number of regimes is usually restricted
to 2 or 3 in empirical studies. Hence, the simplified model takes
the following form:

\begin{equation}
y_{t}=\left\{ \begin{array}{lll}
\mu_{L}+\rho_{L,1}y_{t-1}+\ldots+\rho_{L,pL}y_{t-pL}+\varepsilon_{t} & \mbox{if} & y_{t-1}\leq\theta_{L}\\
\mu_{M}+\rho_{M,1}y_{t-1}+\ldots+\rho_{M,pM}y_{t-pM}+\varepsilon_{t} & \mbox{if} & \theta_{L}\leq y_{t-1}\leq\theta_{H}\\
\mu_{H}+\rho_{H,1}y_{t-1}+\ldots+\rho_{H,pH}y_{t-pH}+\varepsilon_{t} & \mbox{if} & \theta_{H}\leq y_{t-1}\end{array}\right.\label{eq:SETAR}\end{equation}


Sufficient and necessary conditions for stationarity of model \ref{eq:SETAR}
in case of i.i.d $\varepsilon_{t}$ were derived by Chan et al. (\citeyear{ChanPetruccelliEtAl1985})
in the case when only one lag is present in each regime%
\footnote{Chan et al. actually prove this for the general case with m regimes.
In that case $\rho_{L}$ is to be replaced by $\rho_{1}$ and $\rho_{H}$
by $\rho_{m-1}$%
}. The whole process is stationary if and only if one of the following
conditions holds:
\begin{enumerate}
\item $\rho^{L}<1,\rho^{H}<1,\qquad\text{ and }\rho^{L}\rho^{H}<1$
\item $\rho_{L}<1,\rho_{H}=1,\qquad\text{ and }\mu_{H}<0$
\item $\rho_{H}<1,\rho_{L}=1,\qquad\text{ and }\mu_{L}>0$
\item $\rho_{H}=\rho_{L}=1,\quad\qquad\text{ and }\mu_{H}<0<\mu_{L}$
\item $\rho_{H}\rho_{L}=1,\rho_{L}<0,\quad\text{ and }\mu+\rho_{H}\mu_{L}$
\end{enumerate}
Interestingly, the values of the coefficients in the inner regime
do not appear in these conditions. Thus, a unit root in the inner
regime won't affect the stationarity of the whole process. Note also
that the condition for the AR(1) process $|\rho|<1$ is relaxed as
the autoregressive coefficients have only to be strictly inferior
to 1.

Condition (1) corresponds to a case where the stationarity of the
whole process is due to the stationarity of the outer regimes. It
corresponds to the previously described case where adjustment occurs
only after some threshold has been reached. This is the case being
mostly referred and investigated in the threshold cointegration literature.
That condition has been shown to hold (de Jong \citeyear{Jong2009})
under the weaker condition of weakly dependent innovations. 

Conditions (2) and (3) are less restrictive as they allow the presence
of a unit root in an outer regime. The process is though stationary
provided the drift in the unit root regime pushes towards the stationary
regime. 

Conditions (4) is still less restrictive as then the outer regimes
can have both unit roots, but the fact that the drift parameters are
of opposed signs ensures that the process will revert to its mean.
In one sense, a process driven by condition (4) could correspond to
a model of adjustment, as once the inaction band is overtaken, strong
mean reversion occurs. See the discussion in section \ref{sub:Types-of-adjustment}.

Condition (5) does not correspond in our mind to any clear and intuitive
process and is not discussed. 


\paragraph{Higher order lag polynomial}

Sufficient and necessary conditions for a SETAR process with more
than one lag are still not known. Sufficient conditions have been
derived, but those correspond only to condition (1) of the model with
one lag. Hence, one may conjecture that weaker conditions allowing
for unit roots in regimes such as (2) to (5) may hold. Chan and Tong
(\citeyear{ChanTong1985}) established the sufficient condition that
$max_{a\leq i\leq m}\sum_{j=1}^{p}|\rho_{ij}|<1$, Kapetanios and
Shin (\citeyear{KapetaniosShin2006}) require stationarity%
\footnote{The roots of the lags polynomial having all roots outside the unit
circle.%
} of the outer regimes, whereas Bec et al. (\citeyear{BecSalemEtAl2004})
establish weaker conditions, which have a less intuitive interpretation. 


\section{Estimation and inference}


\subsection{Estimation and inference in the long-run relationship representation}


\subsubsection{The one threshold case}

Estimation is discussed first in the long-run relationship for the
threshold and slope parameters, with first one and then two thresholds.
Estimation of the number of lags is then discussed. Second estimation
of the threshold with given cointegrating values is done for 1 and
2 thresholds, and then extended to the case where the cointegrating
vector has to be estimated.

Notice that model \ref{eq:SETAR} can be written in a usual regression
form as:

\begin{equation}
y_{t}=I_{L}\left(\mu_{L}+\rho_{L,1}y_{t-1}+\ldots+\rho_{L,pL}y_{t-pM}\right)+I_{M}\left(\mu_{M}+\rho_{M,1}y_{t-1}+\ldots+\rho_{M,pM}y_{t-pM}\right)+I_{H}\left(\mu_{H}+\rho_{H,1}y_{t-1}+\ldots+\rho_{H,pH}y_{t-pH}\right)+\varepsilon_{t}\label{eq:SETAR-reg}\end{equation}


where the $I_{a}$ are dummy functions that take either 0 or 1 depending
on if $y_{t-1}\in a\qquad{where}a=L,M\text{ or }H$:

$I_{a}=\begin{cases}
1 & \text{ if }y_{t-1}\in a\\
0 & \text{ else }\end{cases}$

Estimation of the slope parameters $\beta$ =($\mu_{a}$, $\rho_{a,i}$)
is straightforward in case of a known threshold: it is simply OLS.
Note that as the dummy variables are mutually exclusive%
\footnote{i.e. an observation is only in one regime at a time%
}, the subsets regressors are orthogonal and estimation can also be
done independently on the subsets.

Estimation of the threshold parameter is not obvious as the dummy
variable is a discontinuous function. Hence, to obtain an estimator
minimizing the sum of squares or maximizing the log-likelihood, an
analytical form can't be derived, nor can usual optimisation algorithms
be used, as the objective function is highly erratic. 

A solution is obtained through concentration of the objective function.
As the slope estimators given a threshold are OLS, one can reduce
the problem by concentrating out the minimization problem through
$\beta(\theta)$ and the corresponding sum of squares $SSR(\theta)$.
The objective function becomes:

\begin{equation}
\hat{\theta}=\arg\min_{\theta}SSR(\theta)\label{eq:argmin}\end{equation}


Minimization of \ref{eq:argmin} is done through a grid search: the
values of the variable are sorted, a certain percentage of the first
and last values is excluded to ensure a minimal number of observations
in each regime, the SSR is estimated for each selected value and the
one that minimize the SSR is taken as the estimator. This method has
received different name in the literature such as \emph{concentrated
LS}, \emph{conditional LS}. 

This is implemented in package \tsDyn through the function \code{selectSETAR}.
The range of value to search inside is specified by the argument \Rarg{trim
} specifying the percentage of extreme values to exclude and the argument
\Rarg{th}, which allows to search among fewer observations, search
inside an interval or around a value.

\begin{Scode}{lib, echo=FALSE,results=hide}
library(tsDyn)
\end{Scode}

\begin{Scode}{grid1, eval=TRUE}
library(tsDyn)
data(lynx)
grid<-selectSETAR(lynx, m=1, thDelay=0, trim=0.15, criterion="SSR") 
print(grid)
\end{Scode}

%
\begin{figure}
\caption{Graphical output of the grid search for one threshold}
\label{Flo:plotgrid1}

\begin{Scode}{plotgrid1,fig=TRUE}
plot(grid)
\end{Scode}
\end{figure}


Figure \ref{Flo:plotgrid1} shows the output of the grid search and
illustrate the erratic behavior of the objective function. As pointed
out by Enders (\citeyear{Enders2004}), a strong threshold effect
will result in a sharp U-shaped grid search. 

Once the threshold has been estimated, it can be plugged into the
\code{setar()} function. One can thus obtain the slope estimates
and their asymptotic p-values%
\footnote{based on student distribution%
} :

\begin{Scode}
set<-setar(lynx, m=1, thDelay=0, th=grid$th)
summary(set)
\end{Scode}

It produces an object of class \Rarg{setar} with specific methods
such as \code{print()}, \code{summary(), plot()} and \code{toLatex()}
and inherits from the class \code{nlar} with general methods for
\code{AIC()}, \code{BIC()}, \code{coef()}, \code{deviance()},
\code{fitted()}, \code{logLik()}, residuals(), \code{MAPE()},
\code{mse()}, \code{predict()}. 

Note those two steps could have been done directly using \code{setar()}
and without specification of the \Rarg{th} argument. 


\subsubsection{The two threshold case}

Procedure for two thresholds can be conducted in the same way, and
searching on all combinations of $\theta_{L}$, $\theta_{H}$ to minimize
$SSR(\theta_{L},\theta_{H})$. This is however a $n^{2}$dimensional
search and may rapidly become cumbersome. A computational shortcut
was suggested in BF (\citeyear{BalkeFomby1997}). The idea is to estimate
the threshold in a sequential way: the search is done first in a model
with only one threshold. The second threshold is then estimated taking
the first as fixed. A few iterations can be conducted, reestimating
the first threshold conditional on the second one and viz. Gonzalo
and Pittarakis (\citeyear{GonzaloPitarakis2002}) showed that this
algorithm is efficient as the estimator in the first step in the mis-specified
model is nevertheless consistent for one of the thresholds. This is
a substantial shortcut as it reduces the number of computations from
$n^{2}$ to $2\times n$ or $k\times n$ when some iterations are
done, practice showing that after 2 or 3 iterations a maximum is reached. 

Estimation of the second threshold is done in package \tsDyn
by setting the parameter \Rarg{nthresh} to 2:

\begin{Scode}{grid2, eval=TRUE}
selectSETAR(lynx, m=1, thDelay=0, trim=0.15, criterion="SSR", nthresh=2) 
\end{Scode}

Previous discussion was based on the pure TAR model. When this is
applied in the domain of threshold cointegration, the cointegrating
vector needs to be estimated. This doesn't seem to be a problem as
practically all studies apply a two-step approach, estimating first
the cointegrating vector and then estimating the threshold parameters
of the residuals from the first step. This could be justified as the
estimator of the first step is super-consistent%
\footnote{Super-consistency refers to the fact that the estimator converge to
its true value at rate $n$ instead of usual rate $\sqrt{n}$.%
}. There are to my knowledge nevertheless no proof nor empirical simulations
showing that this sequential approach leads indeed to global optimization
over the parameters. 


\subsubsection{Distribution of the estimator}

Properties of the concentrated LS estimator described above were obtained
by Chan (\citeyear{Chan1993}). He established that the estimator
of the threshold, $\hat{\theta}$, was super-convergent, whereas the
estimator of the slope coefficients, $\hat{\beta}$, was convergent.
He furthermore found that the distribution of $\hat{\theta}$ is a
compound Poisson process with nuisance parameters, which can't be
computed easily. Superconvergence of $\hat{\theta}$ allows asymptotically
to take the estimated value as given and conduct usual inference on
the $\hat{\beta}$. Indeed, the distribution of $\hat{\beta}$ is
the usual gaussian law and is independent asymptotically of the $\hat{\theta}$.
Those results apply when all the coefficients values differ in each
regime, the distribution of the whole process being discontinuous.
In a certain case when only a few variables have regime specific value,
the so-called continuous case, Chan and Tsay (\citeyear{ChanTsay1998})
established that the threshold estimator converges at the usual rate
and is normally distributed, whereas the asymptotic Independence does
not hold. However, the continuous model does not seem to have received
much use in empirical applications%
\footnote{Gonzalo and Wolf (\citeyear{GonzaloWolf2005}) discuss a test to differentiate
between continuous and discontinuous model.%
}. There remains an uncertainty for me nevertheless if some studies
use actually a continuous model but describing it as a discontinuous
model. 

Note that while in both continuous and discontinuous models the results
are known in the one threshold case, there is to my knowledge no study
investigating the two thresholds model. 


\paragraph{Inference on the threshold parameters}

A few studies have concentrated on methods to do inference on the
threshold parameter. Hansen (\citeyear{Hansen2000}) makes the assumption
that the threshold effect vanishes asymptotically, which enables him
to derive the distribution of the threshold parameter and to provide
critical values for the likelihood ratio test of $\theta=\theta_{0}$.
Confidence intervals can then be obtained by inverting the log-likelihood
ratio: the bound are the values for which the test is rejected. %
\footnote{As the objective function is erratic, there may be intervals inside
which the test was rejected for some values and not rejected for others,
see graph page 588 in Hansen (\citeyear{Hansen2000}).%
}

Gonzalo and Wolf (\citeyear{GonzaloWolf2005}) use a subsampling procedure
to obtain confidence intervals for the threshold. Their method has
the advantage of providing a test to discriminate between continuous
and discontinuous models. Seo and Linton (\citeyear{SeoLinton2007})
modify the objective function by replacing the indicator function
by a smoothing function%
\footnote{This can be a distribution function as it needs to be bounded between
0 and 1.%
}. This so called smoothed least square estimator has a smaller rate
of convergence but is normally distributed and still independent of
the slope parameter estimator. They furthermore establish the validity
of a regressor-based bootstrap to obtain small-sample refinements.
None of those methods is currently implemented in package \tsDyn, but
the inclusion of Hansen method is under project.

Even if the estimators of the slope parameters are asymptotically
normally distributed and independant of the threshold estimator, this
may not hold in small sample. Hansen (2000), and Seo and Linton (2006),
both suggest methods to take into account the variability of the threshold
parameter when building confidence intervals for the slope coefficients.
Hansen's method requires lot of computations as it implies to estimate
the confidence interval of $\hat{\beta}(\hat{\theta})$ for all $\theta_{i}$
that are included in the confidence interval of $\hat{\theta}$. Using
the normality of the smoothed-least square, Seo and Linton (2006)
are able to obtain a simpler way to compute the confidence interval
for $\hat{\beta}$. 


\paragraph{	-Carlo Studies}

Globally, Monte Carlo studies of the estimators in the previous papers
(Hansen 2000, Gonzalo and Wolf 2005, Seo and Linton 2006) find that
the threshold parameters exhibit a large variability, higher than
is predicted by the asymptotical theory: super-convergence of the
estimator does not seem to be effective in small samples. Consequently,
the slope estimators exhibit a large variability. Without surprise,
the authors remark that the variability decrease with the sample
size as well as with the effect threshold: the bigger the difference
in the parameters in each regime, the better the estimation of the
threshold. Gonzalo and Pittarakis (2002) find another interesting
factor influencing the variability, namely the number of observations
in each regime. While it seems obvious that this will influence the
precision in estimating the slope parameters, they show that this
affects also the threshold parameter. Indeed, $\hat{\theta}$ is best
estimated when there are an equal number of observations in each regime,
precision of the estimator decreasing when only a few observations
are present in a regime. 


\paragraph{Estimation of the number of lags}

The estimation of the number of lags can be done be using again the
concentration method given above, using as objective function an information
criterion (AIC, BIC) rather than the SSR. 

\begin{equation}
IC()=n*\log{\hat{\sigma}_{\varepsilon}^{2}}+a(n)*k\end{equation}


Where a(n) =2 for the Akaike information criterion (AIC) or a(n)=ln(n)
for the bayesian information criterion (BIC)%
\footnote{There are several formulations of those criterions. We took here the
formulation as in Franses and van Dijk (\citeyear{FransesDijk2000})%
}. 

The parameter are then \begin{equation}
(\hat{\theta_{1,}},\hat{\theta_{2,}},\hat{k_{1,}},\hat{k_{2,}},\hat{k_{3,}}=\arg\min IC(\theta_{1},\theta_{2},k_{1},k_{2},k_{3})\end{equation}


This is a considerable extension of the dimension of the grid search,
and usually one uses the restriction $k_{1}=k_{2}$. 

This is possible in package \tsDyn by specifying the argument
\Rarg{criterion=AIC} in function \code{selectSETAR()}:

\begin{Scode}{grid1, eval=TRUE}
selectSETAR(lynx, m=6, thDelay=0, trim=0.15, criterion="AIC", same.lags=TRUE)
\end{Scode}

The argument same.lags restrict the search to have the same number
of lags in each regime. Its default value, currently set to \Rarg{FALSE}%
\footnote{This will probably be changed soon in future version.%
}, search on all combinations of lags, that is, allows to have different
lags in each regime.


\subsection{Estimation and inference in the TVECM representation}


\paragraph{Estimation }

Estimation of the threshold and cointegrating parameters could be
done in the long-run relationship and those estimates plugged into
the TVECM, as the Engle-Granger advocates for the linear case. To
my knowledge,the the validity of that method has not been investigated
in theoretically. BF mention that the super-convergence of the OLS
estimator in the LR (Watson 1987) still hold when the residuals follow
a SETAR process under the condition (1). 

Rather, Hansen and Seo (2002) and Seo (\citeyear{Seo2009}) study
estimators directly based on the TVECM. Hansen and Seo derive a maximum-likelihood
(ML) estimator, and use a two-dimensional grid for simultaneous estimation
of $\hat{\theta}$ and $\hat{\gamma}$. This two-dimensionality can't
be avoided as the parameters can't be expressed as functions each
of the other one: for each cointegrating value the ECT will be different.
For $\theta$, the grid is restricted to the existing values of the
ECT, with exclusion of the upper and lower ranges. For the cointegrating
value, HS suggest to conduct the search based on a confidence interval
obtained in the linear model. When the two values are give, the slope
and speed adjustment parameters can be concentrated out and the estimator
is simply OLS (though HS depict it as MLE, it is only MLE as starting
values for the algorithm are based on the linear MLE estimate). This
method can be done in a simple bivariate model without intercept in
the cointegrated relationship, but becomes intractable with more than
two cointegrating relationships. 

Note that in what I called the cointegration with threshold effect
framework, where an external variable rather than the ECT is taken
as transition variable, estimation is highly simplified as the interdependency
between the ECT term and the threshold variable is ruled out. Estimation
of multivariate VECM with many cointegrating relationships is then
feasible, the grid search being conduced only over the threshold parameter
space (Krishnakumar and Netto \citeyear{KrishnakumarNeto2009}). 


\paragraph{Inference}

While Hansen and Seo (2002) suggested an estimator for the multivariate
case, they only conjectured its consistency. Interesting results can
be found in Seo (2009) concerning proprieties of the LS estimator.
Seo shows that LS estimators of both the threshold and cointegrating
values are super convergent, the estimator $\beta$ converging at
a faster rate than in linear model, at $n^{\frac{3}{2}}$instead of
$n$. Similarly as in his previous work in the univariate case (Seo
and Linton 2007), Seo considers a smoothed-LS estimator and finds
that is converging at a slower rate but then normally distributed,
allowing to obtain confidence intervals. 


\paragraph{Implementation in R}

The function \code{TVECM()} in package \tsDyn allows to estimate
a bivariate TVECM with two or three regimes with the OLS like estimator.
It should be emphasized here that in my view there is no difference,
except in the starting value, between the OLS and MLE estimator, as
conditional on the threshold and the cointegrating value, the MLE
estimator is simply LS. The model can be specified either with a \Rarg{constant}
a \Rarg{trend}, or \Rarg{none}, (arg \Rarg{include}) and the
lags can be regime specific or not (arg \Rarg{common). }

Procedure for the \code{TVECM()} differ from that of \code{setar()}
as there is no corresponding \code{selectSETAR()} function. As the
search is two dimensional and the cointegrating parameter take continuous
values, it can be easily cumbersome and different options to restrict
the search are given with arguments \Rarg{ngridBeta}, \Rarg{ngrid}, \Rarg{Th}, \Rarg{gamma1},
\Rarg{gamma2}, \Rarg{beta}%
\footnote{Name of this argument will probably be modified in further version
of the package.%
}.

\begin{Scode}{eval=FALSE}
data(zeroyld)
tvecm<-TVECM(zeroyld, nthresh=2,lag=1, ngridBeta=60, ngridTh=30, plot=TRUE,trim=0.05, beta=list(int=c(0.7, 1.1))) 
\end{Scode}

%
\begin{figure}
\caption{Results of the two-dimensional grid search for a TVECM}
\label{Flo:GridTVECM}

\includegraphics{ThCointOverviewGridSearchTVECM}
\end{figure}


It produces an object of class \code{TVECM()} with specific methods
such as \code{print()}, \code{summary()} and \code{toLatex()}
and inherits from the class \Rarg{nlVar} with general methods for
\code{AIC()}, \code{BIC()}, \code{coef()}, \code{deviance()},
\code{fitted()}, \code{logLik()}, \code{residuals()}. 

Note that a plot of the search is given automatically as this has
proved in practice to be a useful tool, experience showing that the
confidence interval for the cointegrating values are too small and
hence only a local minimum is obtained, which can be easily detected
with the plot.


\section{Testing}

Testing for threshold cointegration is particularly difficult as it
involves two aspects: the presence of cointegration and that of non-linearity.
Hence, one may have four different cases:
\begin{itemize}
\item Cointegration and threshold effects
\item Cointegration and no threshold effects
\item No cointegration and no threshold effects
\item No cointegration and threshold effects
\end{itemize}
Hence, a test with threshold cointegration may have as null hypothesis
either cointegration or no cointegration. This distinction is of major
importance as this implies a different distribution under the null.
The distribution is also different whenever the test is done based
on the LR or the VECM representation%
\footnote{Actually a VAR if the null is no cointegration.%
}. Some of the tests also allow to estimate the cointegrating vector,
whereas the majority requires pre-specified ones. Finally, the number
of regime differ in the different specifications, some taking two,
some three regimes, or symmetric outer regimes. To my knowledge, only
one test (Hansen 1999) is able to determine the number of regimes,
through a test of one against two thresholds. As a result, there exist
many different tests for all the possible cases. 

The approach advocated by BF was to conduct a two-step analysis in
the LR with pre-specified cointegrating value: testing first for cointegration,
and if tests indicate presence of cointegration to test for threshold
effects. Nevertheless, this approach may suffer of low power when
the true model contains threshold effects and the first step is conduced
using tests with a linear specification. Indeed, several studies showed
that conventional unit root tests had very low power when the alternative
was a stationary SETAR (Pippenger and Goering \citeyear{PippengerGoering2000}).
Indeed, many studies found that the LOP did not hold, the unit root
being not rejected, contrary to many economic arguments in favor of
its stationarity. 

Taylor (\citeyear{Taylor2001}) advocated that the failure of tests
to reject the unit root for the case of the LOP was due to the use
of test which assume linear adjustment. Use of more appropriate tests
was indeed able to confirm the LOP. He showed through theoretical
and simulation-based arguments that indeed linear tests were biased
towards non-rejection of stationarity. 

Hence, the procedure should be to do, as in BF, a two-step approach,
using first linear tests of cointegration. If linear cointegration
is not rejected, tests for threshold cointegration with linear under
$H_{0}$should be used. Failure of cointegration in the first step
should lead to the use of tests with no cointegration under $H_{0}$and
threshold cointegration under the alternative. The second case is
particularly interesting, as it illustrates how threshold cointegration
is a broader concept that involves linear cointegration as a specific
case. 


\subsection{The problem of the unidentified parameter}

A problem for the statistical testing procedure arises when the threshold
parameter needs to be estimated. In case of a known threshold parameter,
a likelihood-ratio test for the null of no threshold effects (testing
actually equality of the coefficients in each regime) can be formed
and has the usual $\chi^{2}$ distribution (Chan and Tong \citeyear{ChanTong1990}).
But when it is unknown, which is typically the case in practice%
\footnote{unless maybe when one imposes a threshold of zero%
}, the distribution of the test is then non-standard as it entails
a parameter that is not identified under the null, the so-called Davies
problem (\citeyear{Davies1977}, \citeyear{Davies1987}).

Solutions for that problem (Andrews and Ploberger \citeyear{AndrewsPloberger1994})
involve usually applying the test statistic for a wide range of possible
threshold values, and then aggregating those results. One of the solution
encountered is to average all the values, either by using a simple
mean or an exponential average. Another solution is to use a supremum
statistic, that is the value for which the test is most favorably
rejected. This may be seen as an endogeneity bias, but it is not as
long as appropriate asymptotical tools are used, that take into account
this variability of the test. For a discussion on that question in
the similar field of structural break, see Perron (\citeyear{Perron1989})
and Andrews and Zivot (\citeyear{ZivotAndrews1992}). 

As the test are applied on a range of values, the question of the
selection of that range arises. A typical approach is to sort the
threshold values in ascending order and exclude a certain percentage
of the lowest and highest values. There is no clear rule on the choice
of this percentage, but it should not be too small as Andrews and
Ploberger (1994) show that setting it too low result in a considerable
size distortion. Other approaches as in Bec et al. (\citeyear{BecGuayEtAl2008})
is to construct a different grid under the null and under the alternative,
using the ADF unit root test for the pre-testing.

The sup-test procedure looks really similar to the estimation procedure
as both rely on the use of a sorted grid, with exclusion of some extreme
values. The parameter selected nevertheless is the same only in the
case of an homoscedastic Wald (or Fisher) test. Indeed, the threshold
parameter minimizing the SSR need not be the same of that one maximizing
a LM statistic.

Another interesting approach is provided in Altissimo and Corradi
(\citeyear{AltissimoCorradi2002}) who derive bounds for Wald and
LM type tests. Contrary to the usual approach consisting in deriving
the asymptotical distribution of the tests and obtaining critical
values, they simply show that one may apply a functional to the test
that is bounded. 

The decision rule from their bound is easy as the model under the
null (alternative) should be chosen when the bound is below (above)
one. They show that this procedure leads to type I and type II errors
approaching zero asymptotically. This result is of great importance
as it allows to reduce substantially the number of computation, as
critical values don't need to be tabulated. 


\subsection{Cointegration vs. threshold cointegration tests}


\subsubsection{Test based on the long-run relationship}

As discussed above, the idea for the testing procedure if to test
first for cointegration and in the case when cointegration is not
rejected, test for threshold cointegration, taking cointegration as
a null. In our view, implicetly assumed in that methodology is that
the threshold model will be also stationary. Whereas the fact that
a unit root may appear in a three-regime SETAR model, the question
is never asked in a two regimes-model. This is an important gap as
indeed splitting the sample may create a unit root in one of the regime.
This is the case indeed in Hansen (1999), who did not seem to note
it.

In package \tsDyn, a minimal test is done automatically computing
whether the roots of the polynomials don't have values equal or lower
to one. Hence, one obtains an automatical warning with data from Hansen
(1999):

\begin{Scode}
data(IIPUs)
set<-setar(IIPUs, m=16, thDelay=5, th=0.23)
\end{Scode}



Hansen (\citeyear{Hansen1996}) derived the asymptotic properties
of the sup-LM test for a SETAR model with one unknown threshold. The
test follows a complicated empirical distribution process with nuisance
parameters and hence critical values for a general case can't be tabulated.
Hansen nevertheless shows a simulation procedure which allows to generate
asymptotic p-values. In this procedure, heteroskedasticity can also
be taken into account by slight modifications. In a later article,
Hansen (\citeyear{Hansen1999}) studies an alternative way to obtain
the p-values through a residual bootstrap, whose validity is nevertheless
not established but only conjectured. More interestingly, the author
develops an extension of the testing procedure to test against two
threshold, and to determinate the number of thresholds by testing
the null of one threshold against two. 

This test is available in \tsDyn with the function \code{setarTest()},
for which the homoskedastic bootstrap have been implemented. It takes
as argument \Rarg{nboot} the number of bootstrap replications and
\Rarg{test}=''\Rarg{1vs}'' (1 regimes against 2) or ''\Rarg{2vs3}''
(2 regimes against 3). 

\begin{Scode}{eval=FALSE}
Hansen.Test<-setarTest(lynx, m=1, nboot=1000)
\end{Scode}

Available methods are \code{print()}, \code{summary()} and \code{plot()},
as well as an \code{extendBoot()} function to run new bootstrap
replications and merge the result with the old ones. This can be useful
for a preliminary test and to check how the result is influenced by
new runs. 

Another type of test has been suggested by \citet{PetrucelliDavies1986}
and Tsay (\citeyear{Tsay1989}). By transforming the specification
into an arranged autoregression, Tsay reformulates the problem into
a structural change test. With a test of stability of recursive residual
to detect structural change, the problem of the unidentified parameter
under $H_{0}$ is avoided and hence the test follows a simple $\chi^{2}$distribution.
This test has been implemented in R but not included in version 0.7
as the result differ sometimes drastically from those in the paper%
\footnote{actually as well as from those also differing in the GAUSS procedure
distributed by Lo and Zivot (2002). %
}

BF suggested to extend the approach of Tsay using several other structural
change tests, using techniques as in Hansen (1996). Appropriateness
of this method has been nevertheless discussed by Hansen (2000) as
the the ordering of the variable for the arranged autoregression may
induce a trend, in case the structural tests are not consistent. 


\paragraph{Criterion based approaches}

Differing from a pure testing procedure, model selection procedures
based on information criteria (IC) have gained much interest in the
literature and their use has been sometimes advocated rather than
formal testing procedure (see for example Lètkephol\citeyear{Luetkepohl2007}).
This is the case in the well-known selection of lags in time series
models, but also for estimating the cointegrating rank (Gonzalo and
Pittarakis \citeyear{GonzaloPitarakis1998}, Cheng and Phillips\citeyear{ChengPhillips2009}). 

This has been also applied for the determination of the number of
regimes in a SETAR model by Gonzalo and Pittarakis (\citeyear{GonzaloPitarakis2002}).
They show indeed that this works well in practice using a modified
BIC. This result is of great interest in practice as it avoids the
use of bootstrap replications and hence significantly diminishes the
number of computations required. The authors remark in simulations
studies that the AIC has big type one error compared to other BIC,
while it has a smaller type II error. 

This is easily implemented in package \tsDyn with the generic
function \code{AIC()} and the similar \code{BIC()}. Furthermore,
with the argument \Rarg{k}, practically any penalty term can be
used. Using the example of Hansen (1999):

\begin{Scode}
sun<-(sqrt(sunspot.year+1)-1)*2 
lin<-linear(sun, m=11)
set1<-setar(sun, m=11, th=7.4, thDelay=1, nested=TRUE)
set2<-setar(sun, m=11, th=c(5.3,8),nthresh=2, thDelay=1, nested=TRUE)
matrix(c(AIC(lin),AIC(set1),AIC(set2),BIC(lin),BIC(set1),BIC(set2)),ncol=2,dimnames=list(c("lin","set1", "set2"),c("AIC", "BIC")))
\end{Scode}

As mentioned above, one could also use bounds derived by Altissimo
and Corradi (\citeyear{AltissimoCorradi2002}). The authors indeed
investigate proprieties of their bound and find that is has considerable
size distorsion but excellent power for the alternative they choose.

This bound has not been implemented in \tsDyn as results were different
compared to other studies (see Galvao \citeyear{Galvao2006}) and
there is no comparison of the results with other testing procedures. 


\subsubsection{Test based on the TVECM representation}

Hansen and Seo \citeyearpar{HanSeo2002}suggest a sup-LM test of a
linear VECM against a threshold VECM with two regimes. In the case
of unknown cointegrating vector, the search for the sup-LM maximal
value can be reasonably done only for the case of a bivariate TVECM.

\begin{equation}
\begin{bmatrix}\Delta X_{t}\\
\Delta Y_{t}\end{bmatrix}=+\left\{ \begin{array}{ll}
\begin{bmatrix}c^{XL}\\
c^{YL}\end{bmatrix}+\begin{bmatrix}a_{XL}\\
a_{YL}\end{bmatrix}ECT_{L,t-1}+B_{1L}\begin{bmatrix}\Delta X_{t-1}\\
\Delta Y_{t-1}\end{bmatrix}+\ldots+B_{pL}\begin{bmatrix}\Delta X_{t-p}\\
\Delta Y_{t-p}\end{bmatrix}\\
\begin{bmatrix}c^{XH}\\
c^{YH}\end{bmatrix}+\begin{bmatrix}a_{XH}\\
a_{YH}\end{bmatrix}ECT_{H,t-1}+B_{1H}\begin{bmatrix}\Delta X_{t-1}\\
\Delta Y_{t-1}\end{bmatrix}+\ldots+B_{pH}\begin{bmatrix}\Delta X_{t-p}\\
\Delta Y_{t-p}\end{bmatrix}\end{array}\right.\label{eq:TVECMHanSeo}\end{equation}


Collecting the various parameters into $A_{L}={C_{L},a_{L},B_{L}}$and
similarly for $A_{H}$, the $H_{0}$ of a linear model $A_{H}=A_{L}$is
rejected when $A_{H}\neq A_{L}$. The distribution of the sup-LM test
is found to be the same as in the univariate case as in Hansen (1996). This 
distribution can't be tabulated due to the presence of nuisance parameters and hence 
the authors suggest two bootstrap approaches, with either a fixed-regressor or a residual bootstrap. While in the paper the sup-LM tests is conditional on both the cointegrating and threshold value, the implementation of this test done by the authors takes the cointegrating vector as given, based on the value estimated from the linear VECM.  

This is available in package \tsDyn using the function \code{TVECM.HStest()}. 

\subsection{No cointegration vs. threshold cointegration tests}

Numerous tests for the null of no cointegration may be used, either
from the L-R or the TVECM representation. They generally suffer from
two major drawbacks which merit on my opinion more attention. 

The first is that unit roots tests with a stationary SETAR as alternative
which developed recently may be used in the case of a known cointegrating
vector, in analogy to the linear case (see section 1). This has curiously
not been discussed by their authors. Formal test which allow to estimate
the beta have not been derived in the L-R nor in TVECM form, and hence
constrain the threshold cointegration field to analyze only cases
where the cointegrated values are meant to be known. Even if it is
a strong restriction, it is still interesting, since there are many
applications where theory predicts a particular cointegrated vector,
as Horvath and Watson (1995) claim:
\begin{quotation}
Economic models often imply that variables are cointegrated with simple
and known cointegrating vectors. Examples include the neoclassical
growth model, which implies that income, consumption, investment,
and the capital stock will grow in a balanced way, {[}...{]}. Asset
pricing models with stable risk premia imply corresponding stable
differences in spot and forward prices, long- and short-term interest
rates, and the logarithms of stock prices and dividends. Most theories
of international trade imply long-run purchasing power parity, so
that long-run movements in nominal exchange rates are matched by countries
relative price levels. Certain monetarist propositions are centered
around the stability of velocity, implying cointegration among the
logarithms of money, prices, and income. 
\end{quotation}
A second drawback of the test with the null of no cointegration is
that the alternative stationary model always take the form of the
condition (1). Hence, the presence of a unit root is considered as
non-stationarity of the series. We saw nevertheless above that a SETAR
process may still be stationary even with a unit root in a regime.
Henceforth, the non-rejection towards stationarity is not in itself
a sign that the series is indeed non-stationary. It is only a first
step indicating that condition (1) of stationarity does not hold,
but  this does not mean that other conditions may not hold and the
process hence be stationary. However, those further investigations
are not possible as there are to my knowledge no tests that take as
alternative hypothesis other conditions such as (2) or (3). We may
hence conjecture that many of the series described as non-stationary
in the literature may well be SETAR-stationary. 


\subsubsection{Tests based on the long-run relationship}

Taking no cointegration as null hypothesis has the implication that
under the null, the series (which is also, remember, the transition
variable) is non-stationary, which affects the distribution of the
tests. Analogously as in the linear case, when the cointegrating vector
is known, usual unit root tests can be used, whereas estimation of
the cointegrating vector affect the distribution of the test, which
require use of different tests/critical values. Hence, when the cointegrating
vector is known, unit root tests with a stationary SETAR as alternative
can be used, whereas the case of unknown cointegrating vector needs
correction. 


\paragraph{Known threshold}


\paragraph{Bec, Ben Salem and Carrasco (BBC)}

Bec, Ben Salem and Carrasco (\citeyear{BecSalemEtAl2004}) (referred
as to BBC), test for unit root against a symmetric three regime SETAR
model. The model specification is very general as intercepts as well
as lags are included in each regime, and hence corresponds to the
model in \ref{eq:SETAR-reg}. \begin{equation}
\Delta y_{t}=I_{L}\left(\mu_{L}+\rho_{L}y_{t-1}+\sum\Delta\gamma_{L,i}y_{t-i}\right)I_{M}\left(\mu_{M}+\rho_{M}y_{t-1}+\sum\Delta\gamma_{M,i}y_{t-i}\right)I_{H}\left(\mu_{H}+\rho_{H}y_{t-1}+\sum\Delta\gamma_{H,i}y_{t-i}\right)+\varepsilon_{t}\label{eq:TAR-BBC}\end{equation}


where $I_{L}=I_{\left\{ y_{t-1}\leq-\theta\right\} }$, $I_{H}=I_{\left\{ y_{t-1}>\theta\right\} }$
and $I_{M}=I_{\left\{ -\theta\leq y_{t-1}\leq\theta\right\} }$

The null hypothesis of unit root is $H_{0}$: $\rho_{L}=\rho_{H}=\rho_{M}=0$
with the alternative $H_{A}$: $\rho_{L}<1$, $\rho_{H}<0$ and $\rho_{M}\leq1$.
That is, a unit root is allowed in the middle regime, but an explosive
behavior is ruled out. BBC find that the distribution of sup-Wald,
sup-LM and sup-LR are free of nuisance parameters and provide critical
values. The authors suggest that the extension to a SETAR with non
symmetric thresholds should not lead to further complications. 


\paragraph{Kapetanios and Shin (KS)}

Kapetanios and Shin (\citeyear{KapetaniosShin2006}) uses as alternative
a three-regime model with a unit root in the inner middle which is
meant to be more consistent with the concept of a band without adjustment.
As then coefficients in the middle regime do not need to be estimated,
the test is meant to have better power when the true model is indeed
a model with a unit root in the inner regime. They allow the possibility
to add lags common to all the regimes. \begin{equation}
\Delta y_{t}=\mu_{L}+\rho_{L}y_{t-1}+\mu_{H}+\rho_{H}y_{t-1}+\sum_{i=1}^{p}\gamma_{i}\Delta y_{t-i}+\varepsilon_{t}\label{eq:TARKapShin}\end{equation}


The null hypothesis of unit root is $H_{0}$: $\rho_{L}=\rho_{H}=0$
with the alternative $H_{A}$: $\rho_{L}<1$, $\rho_{H}<0$. The grid
for the thresholds is selected such that the probability of being
in the middle regime decreases as the sample size increases, converging
to zero asymptotically. Under this specification, the authors derive
statistics (the sup-Wald, exp Wald and ave Wald) which are obtain nuisance
parameter free and provide critical values. 


\paragraph{Bec, Guay and Guerre (BGG)}

Under a similar model such as in BBC, Bec, Guay and Guerre (\citeyear{BecGuayEtAl2008})
(hereafter BGG) concentrate on the selection of the grid to obtain
a consistent test diverging under $H_{A}$. The idea is that if $H_{0}$
is true, the grid should be small as to ensure a good size, whereas
if $H_{A}$ holds, the grid should be as large as possible to ensure
power. Hence, the width of the grid is selected depending on a pre-test
based on the AD test. 


\paragraph{Seo}

Seo (\citeyear{Seo2008}) derive a test with a two-regime SETAR as
alternative, allowing for non linear and serial correlated errors.
Under those weaker assumptions, the asymptotical distribution of the
sup-Wald selecting the values as in KS depends on nuisance parameters
and critical values can't be tabulated. Seo hence suggests a residual
based block bootstrap, shown to be asymptotically consistent. Extension
of the bootstrap for a three regime model is meant to be easy.


\paragraph{Monte Carlo comparisons}

Maki (\citeyear{Maki2009}) provides a Monte Carlo simulation for
the size and power of the BBC (using sup-Wald) and KS (sup-Wald, ave
and exp-Wald) tests, along with the traditional ADF. Size of the test
is definitely better for the ADF test, with the ave-Wald being close,
the sup- and exp-Wald of KS showing size distorsion, while the sup-wald
of BBC is seen to be too conservative. Power of the tests is investigated
based on an alternative model with three regimes, a symmetric threshold,
a unit root in the middle regime and no lags. Different values of
the thresholds are tested. With a threshold value of zero (i.e. no
thresholds effects), the ADF test has without surprise the best power.
More surprisingly, this is still the case with small thresholds, with
values 1 and 2, the ADF test having the best power for models with
an inner regime counting 40\% of the observations. When the thresholds
increase (and hence the number of observations in the unit root regime),
power of the ADF decreases consequently. This is also the case for
the KS test, as it is based on a asymptotically degenerated threshold,
whereas there is no clear effect on the BBC. As the KS test does not
estimate the inner regime whereas the BBC does, the power of the KS
is much higher, because the true process has indeed a unit root. 

When the mean-reversion of the outer regime is increased (which has
the effect to have more observations in the inner regime), all the
tests have power near to 1 unless the threshold are high (value of
6), where power of ADF test falls. The BBC test has in those cases
a much better power than the KS, as the latter is based on a diminishing
threshold effect. 


\paragraph{Implementation in R}

BBC and KS have been implemented in package \tsDyn. They are nevertheless
for now in an experimental version and may contain errors. Practitioners
should use them with care as the results could not be compared to
those of the authors as the data sets are not publicly available. 


\subsubsection{Unknown cointegrating values}

Two early tests deserve here mention. Enders and Granger (\citeyear{EndersGranger1998})
first provided an empirical framework to deal with unit root tests
having a two-regime SETAR as alternative. They tabulated critical
values for a test when the threshold is known. Critical values with
unknown threshold were given later by Enders (\citeyear{Enders2001}).
Enders and Siklos (\citeyear{EndersSiklos2001}) adopt similar approach
but relying on the cointegration framework and allowing to estimate
the cointegrating values. They hence provide larger critical values
from those of Enders and Granger \citeyearpar{EndersGranger1998}. 

This is to my knowledge the only test which allows to work with an
unknown cointegrating vector. It is not sure nevertheless whether
it more appropriate to use this one rather that the more formal unit
root tests of BBC and KS, as no distribution theory is given in the
former. 


\subsubsection{Test based on the TVECM representation}

Seo \citeyearpar{Seo2006} is to my knowledge the only one to discuss
a test of no cointegration against threshold cointegration based on
the VECM model. His framework is a TVECM model with the ECT splitted
into three regime, the middle one being not adjusted and not taken
into account, and lags common to all regimes:

\begin{equation}
\Delta X_{t}=\mu+\left\{ \begin{array}{l}
a_{L}ECT_{L,t-1}\\
a_{H}ECT_{H,t-1}\end{array}\right.+C_{1}\Delta X_{t-1}+\ldots+C_{p}\Delta X_{t-p}+\varepsilon_{t}\label{eq:TVECM-SeoTest}\end{equation}


The idea of the test is based on results as in \citet{HorvathWatson1995},
who show that when the cointegrating vector is known, a test of cointegration
can be simply done on testing whether coefficients from the ECT are
significant. In the TVECM framework, the null hypothesis of no cointegration
becomes: $H_{0}$: $a_{L}=a_{H}=0$ and the alternative that either
$a_{L}$ or $a_{H}$ is different from zero. The sup-Wald test suggested
does not depend on nuisance parameter and critical values can be obtained.
As the asymptotical distribution is seen to perform badly in small
samples, Seo provide a residual based bootstrap and shows its asymptotic
consistency. 

This test is available in package \tsDyn as \code{TVECM.SeoTest()}. 

\begin{Scode}{eval=FALSE}
data(zeroyld)
dat<-zeroyld
testSeo<-TVECM.SeoTest(dat, lag=1, beta=1, nboot=1000)
summary(testSeo)
\end{Scode}

It requires the argument \Rarg{beta} for the cointegrating value
and \Rarg{nboot} for the number of bootstrap replications. The methods
\code{print()}, \code{summary()} and \code{plot()} are available
for objects issued by \code{TVECM.SeoTest()}. As the model specification
is done for two thresholds, a two-dimensional grid search has been
implemented. This is definitely very slow and a single test may take
a few hours %
\footnote{As this is a sup-Wald test, for which the best thresholds pair is
also maximizing the OLS criterion, one may think that the conditional
search could be applied. This is currently under implementation. %
}.%



\subsection{Conclusion for the test}

I reviewed here some of the most popular and applied tests in the
literature. This is definitely not exhaustive and there exist many
different tests, especially for the univariate case. Despite of this
great amount of different tests and model specification, some points 


\section{Interpretation}

Once threshold cointegration have been indicated by the different
tests and estimation made, there remain interesting questions on the
interpretation of the model obtained. A first one concerns the type
of adjustment and the presence of an attractor. A second one concerns
stability of the system and its reactions to exogeneous shocks. 


\subsection{Types of adjustment\label{sub:Types-of-adjustment}}

As was presented earlier, a SETAR model may be stationary under diverse
conditions. Nevertheless, almost the only one to have been investigated
empirically, and for which there exist some tests, is that described
by the condition (1). Within this type, there are nevertheless different
degrees of adjustment, where adjustment pushes back either to an equilibrium
(EQ-SETAR) or to a band (BAND-SETAR). This distinction is best shown
in a case of perfectly symmetric SETAR with three regimes but same
(of opposed signs) thresholds, same outer coefficients, no lags and
a random-walk without drift in the inner regime. The EQ-SETAR is then:

\begin{equation}
y_{t}=\begin{cases}
\rho y_{t-1}+\varepsilon_{t} & \text{ if }y_{t-1}>\theta\\
y_{t-1}+\varepsilon_{t} & \text{ if }-\theta<y_{t-1}<\theta\\
\rho y_{t-1}+\varepsilon_{t} & \text{ if }y_{t-1}<-\theta\end{cases}\label{eq:EQ-TAR}\end{equation}


Interpretation of such model is done remembering that the conditional
mean in each regime is given by the mean of an AR(1) process, that
is $\frac{\mu}{1-\rho}$. So in, \ref{eq:EQ-TAR}, the conditional
mean in each regime is 0 and hence one may think that 0 is the mean
of the global process as well as its equilibrium, hence its name of
equilibrium-SETAR. A model with a different type of adjustment, the
BAND-SETAR, is given by:

\begin{equation}
y_{t}=\begin{cases}
\theta(1-\rho)+\rho y_{t-1}+\varepsilon_{t} & \text{ if }y_{t-1}>\theta\\
y_{t-1}+\varepsilon_{t} & \text{ if }-\theta<y_{t-1}<\theta\\
-\theta(1-\rho)+\rho y_{t-1}+\varepsilon_{t} & \text{ if }y_{t-1}<-\theta\end{cases}\label{eq:BAND-TAR}\end{equation}


Here, the adjustment process is pushing the deviations exceeding the
threshold back to the edge of the band, defined by the space between
the two thresholds. Indeed, the conditional mean of the outer regimes
are equal to the threshold $\left(\frac{\theta(1-\rho)}{(1-\rho)}=\theta\right)$,
whereas the conditional mean of the inner regime is equal to 0. Therefore,
adjustment does not push back to a certain equilibrium point such
as in a EQ-SETAR but just pull within a certain band. The more complete
BAND-SETAR nests the EQ-SETAR so determining which model describe
best the data can be easily made. 

Whereas the adjustment seems to be faster in a EQ-SETAR than in a
BAND-SETAR, it could be that a SETAR described by condition (4) (denoted
by returning-drift, RD-SETAR, by BF) has a faster adjustment. Effectively,
the outer regimes are unit root process with drift that may have much
faster dynamics than simple AR processes. Despite of this potentially
interesting feature, RD-SETAR models don't seem to have been used much
in the literature, probably because of the unavailability of tests
as we discussed below and also because of the more complicated distribution
of the parameters. 

Note that the difficulty of the comparison of adjustment described
by conditions (1) to (4) comes partly from the fact that first the
computation of the mean of a SETAR model is difficult and requires
numerical methods, and second that the relevance itself of the concept
of mean for non-linear model may be questioned, due for example of
the potential bimodality of the distribution of the process. Concepts
such as attractors and equilibria may be more adequate (see Tong 1990),
with the drawback nevertheless that those only be described case to
case. By extension, the term itself of mean-reversion in the SETAR
framework may be misleading. This is the case in the process 1 where
mean-reversion adjustment appears as a more restrictive condition
than stationarity: taking the case where $\mu_{H}>\theta_{H}$ and
$\mu_{L}<\theta_{L}$along with $|\rho_{L}|<1$ and $|\rho_{H}|<1$
ensures stationarity but not mean-reversion to the inaction band:
once in the outer regime, the process may remain there. 

Whereas the seminal paper of BF was based on models with three regimes
(in some cases the outer regimes are symmetric which may appear as
a two-regime specification), other studies (Enders and Granger \citeyear{EndersGranger1998})
focused on two-regimes model. In our view, two-regimes models offer
interesting insights into asymmetric behaviors, though they have a
complicated interpretation. Empirical studies seek indeed to estimate
a threshold in a two-regime model rather than imposing its value to
zero. Whereas in a three regime model it makes sense to observe a
strictly positive and a strictly negative threshold, there are few
economical arguments in favor of a two-regime model with a non-zero
threshold, where for example positive deviations would behave like
small negative deviations but differently from big ones. 


\subsection{Non linear impulse response functions}

Note that the package tsDyn does NOT provide generalised impulse response functions, altough it does provide standard imuplse response functions for the linear VAR/VECM only, 
building on the function from package \pkg{vars}/\pkg{urca}. Several people have been asking for this functionality, but it should not be too difficult to do it with the existing
functions, since tsDyn already implements a TVECM.sim() and TVECM.boot() functions. For more informations, see various discussions on the tsDyn mailing lists:
\url{http://groups.google.com/group/tsdyn/t/5c517a94a3a3ab0c}


\section{Running the functions on parallel CPUs}
A major drawback of the threshold cointegration tools is that those, due to the probem of the unidentified parameter or the need of bootstrap replications, are heavily computer intensive. The test of Seo (2006) takes indeed a very long time to run. 

To alleviate these problems, a possibility is to run the functions on parallel, ie. either on a unique computer with multiple-CPUs processor or on more complex computer clusters. Nowadays, it is quite common to find even laptops equiped with processors like Intel Dual-Core, and hence parallel functionalities can be used by practically everyone. Furthermore, it has become quite easy to do it in R thanks to packages like \pkg{foreach} which offer a great level of abstraction, requiring the user to do a minimal number of steps to get it running. 
Indeed, this package will function as a wrapper for other parallel packages, and allows the user to run it either on the protocol \Rarg{MPI}, \Rarg{nws} and \Rarg{pvm}, or using the R internal \Rarg{socket} system, as well as the \pkg{multicore}\footnote{For a review on R facilities for high performance computing, see \citet{SchmidbergerMorganEtAl2009}. There exist also a dedicated R mailing list, as well as a \emph{task view} \url{http://stat.ethz.ch/CRAN/web/views/HighPerformanceComputing.html} }. 

Furthermore, parallel computation is quite easy in the context of threshold cointegration as the grid search or the bootstrap replications are independant of each other and can easily be run on different nodes. 

Parallel facilities are for now only available for the function \code{TVECM.HStest()}, through the argument \Rarg{hpc} (standing for high performance computing). When set to \Rarg{foreach}, the package will run the \pkg{foreach} package. It is then up to the user to choose a paralellisation protocol, the current choice being now between \pkg{doMC}, \pkg{doSNOW} (MPI, pvm and nws, as well as internal R sockets), \pkg{doMPI} and \pkg{doRedis}. We illustrate here with the easiest package, \pkg{multicore}, wich proved to be quite powerful, with the disadvantage nerverthless that it can pose problems when R is used within a GUI. 
\begin{Scode}{eval=FALSE}
system.time(test1<-TVECM.HStest(dat, lag=1, nboot=200))

library(doMC)
registerDoMC(2) #Number of cores
system.time(test1<-TVECM.HStest(dat, lag=1, nboot=200, hpc="foreach"))
\end{Scode}

Results are quite impressive, as they show that by simply adding a second core the execution time is divided by two, while using 4 cores will divide the time by three, as shown in figure \ref{Flo:plotCluster}\footnote{It should be mentioned that the relationship between the number of CPUs and the reduction in execution time is decreasing with the number of CPUs: the more the CPUs, the less their additional effect is.}. 

\begin{figure}
\caption{Execution time of function TVECM.HStest using multiple cores}
\label{Flo:plotCluster}
\includegraphics{ClusterTime}
\end{figure}


\section{Conclusion}

In this paper, I showed the interest of threshold cointegration towards
traditional cointegration as being a better framework to model
real world adjustment process with stickiness and asymmetries. Indeed,
a great number of empirical studies applied this model, and an evenly
great number of theoretical results have been obtained. I presented
also how one can use those developments using the package \tsDyn, offering
a comprehensive framework for analysis and testing that, despite of
the great interest in this field, that was until now not available.
Using this package, one may conduce a whole analysis, testing for
threshold cointegration in different situations and different model
specifications, and estimating those models. 

Whereas great developments occured since the seminal work in 1997,
many questions remain unanswered . The complexity of the SETAR model
is actually so high that simple aspects such its distribution or its
moments are still only known in restricted cases. Estimation of more
than one threshold stills create problems, and actual tests of stationary
consider only a small amount of the possible features of a SETAR.
There is up to now no framework allowing to test the stationarity with
an unknown cointegrating vector, and test discriminating 2 against
3 regimes only work in a restricted case.

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\end{document}
