Johansen Cointegration Test
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In
statistics Statistics (from German language, German: ''wikt:Statistik#German, Statistik'', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of ...
, the Johansen test, named after
Søren Johansen Søren Johansen (born 6 November 1939) is a Danish statistician and econometrician who is known for his contributions to the theory of cointegration. He is currently a professor at the Department of Economics, University of Copenhagen and in the ...
, is a procedure for testing
cointegration Cointegration is a statistical property of a collection of time series variables. First, all of the series must be integrated of order ''d'' (see Order of integration). Next, if a linear combination of this collection is integrated of order less ...
of several, say ''k'',
I(1) In statistics, the order of integration, denoted ''I''(''d''), of a time series is a summary statistic, which reports the minimum number of differences required to obtain a covariance-stationary series. Integration of order ''d'' A time ser ...
time series In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data. Exa ...
. This test permits more than one cointegrating relationship so is more generally applicable than the Engle–Granger test which is based on the Dickey–Fuller (or the augmented) test for unit roots in the residuals from a single (estimated) cointegrating relationship. There are two types of Johansen test, either with
trace Trace may refer to: Arts and entertainment Music * Trace (Son Volt album), ''Trace'' (Son Volt album), 1995 * Trace (Died Pretty album), ''Trace'' (Died Pretty album), 1993 * Trace (band), a Dutch progressive rock band * The Trace (album), ''The ...
or with
eigenvalue In linear algebra, an eigenvector () or characteristic vector of a linear transformation is a nonzero vector that changes at most by a scalar factor when that linear transformation is applied to it. The corresponding eigenvalue, often denoted b ...
, and the inferences might be a little bit different. The null hypothesis for the trace test is that the number of cointegration vectors is ''r'' = ''r''* < ''k'', vs. the alternative that ''r'' = ''k''. Testing proceeds sequentially for ''r''* = 1,2, etc. and the first non-rejection of the null is taken as an estimate of ''r''. The null hypothesis for the "maximum eigenvalue" test is as for the trace test but the alternative is ''r'' = ''r''* + 1 and, again, testing proceeds sequentially for ''r''* = 1,2,etc., with the first non-rejection used as an estimator for ''r''. Just like a
unit root test In statistics, a unit root test tests whether a time series variable is non-stationary and possesses a unit root. The null hypothesis is generally defined as the presence of a unit root and the alternative hypothesis is either Stationary process, s ...
, there can be a constant term, a trend term, both, or neither in the model. For a general
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(''p'') model: :X_t=\mu+\Phi D_t+\Pi_p X_+\cdots+\Pi_1 X_+e_t,\quad t=1,\dots,T There are two possible specifications for error correction: that is, two vector
error correction model An error correction model (ECM) belongs to a category of multiple time series models most commonly used for data where the underlying variables have a long-run common stochastic trend, also known as cointegration. ECMs are a theoretically-driven ap ...
s (VECM): 1. The longrun VECM: ::\Delta X_t =\mu+\Phi D_+\Pi X_+\Gamma_\Delta X_+\cdots+\Gamma_\Delta X_+\varepsilon_t,\quad t=1,\dots,T :where ::\Gamma_i = \Pi_1 + \cdots + \Pi_i - I,\quad i=1,\dots,p-1. \, 2. The transitory VECM: ::\Delta X_=\mu+\Phi D_-\Gamma_\Delta X_-\cdots-\Gamma_\Delta X_+\Pi X_+\varepsilon_,\quad t=1,\cdots,T :where ::\Gamma_i = \left(\Pi_+\cdots+\Pi_p\right),\quad i=1,\dots,p-1. \, Be aware that the two are the same. In both VECM, : \Pi=\Pi_+\cdots+\Pi_-I. \, Inferences are drawn on Π, and they will be the same, so is the explanatory power.


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