Cointegration

About: Cointegration is a research topic. Over the lifetime, 17130 publications have been published within this topic receiving 506215 citations.

Testing for error correction in panel data

Abstract: This paper proposes new error correction-based cointegration tests for panel data. The limiting distributions of the tests are derived and critical values provided. Our simulation results suggest that the tests have good small-sample properties with small size distortions and high power relative to other popular residual-based panel cointegration tests. In our empirical application, we present evidence suggesting that international healthcare expenditures and GDP are cointegrated once the possibility of an invalid common factor restriction has been accounted for.

Numerical distribution functions for unit root and cointegration tests

Abstract: SUMMARY This paper employs response surface regressions based on simulation experiments to calculate distribution functions for some well-known unit root and cointegration test statistics. The principal contributions of the paper are a set of data files that contain estimated response surface coefficients and a computer program for utilizing them. This program, which is freely available via the Internet, can easily be used to calculate both asymptotic and finite-sample critical values and P-values for any of the tests. Graphs of some of the tabulated distribution functions are provided. An empirical example deals with interest rates and inflation rates in Canada. Tests of the null hypothesis that a time-series process has a unit root have been widely used in recent years, as have tests of the null hypothesis that two or more integrated series are not cointegrated. The most commonly used unit root tests are based on the work of Dickey and Fuller (1979) and Said and Dickey (1984). These are known as Dickey-Fuller (DF) tests and Augmented Dickey-Fuller (ADF) tests, respectively. These tests have non-standard distributions, even asymptotically. The cointegration tests developed by Engle and Granger (1987) are closely related to DF and ADF tests, but they have different, non-standard distributions, which depend on the number of possibly cointegrated variables. Although the asymptotic theory of these unit root and cointegration tests is well developed, it is not at all easy for applied workers to calculate the marginal significance level, or P-value, associated with a given test statistic. Until a few years ago (MacKinnon, 1991), accurate critical values for cointegration tests were not available at all. In a recent paper (MacKinnon, 1994), I used simulation methods to estimate the asymptotic distributions of a large number of unit root and cointegration tests. I then obtained reasonably simple approximating equations that may be used to obtain approximate asymptotic P-values. In the present paper, I extend the results to allow for up to 12 variables, instead of six, and I correct two deficiencies of the earlier work. The first deficiency is that the approximating equations are considerably less accurate than the underlying estimated asymptotic distributions. The second deficiency is that, even though the simulation experiments provided information about the finite-sample distributions of the test statistics, the approximating equations were obtained only for the asymptotic case. The key to overcoming these two deficiencies is to use tables of response surface coefficients, from which estimated quantiles for any sample size may be calculated, instead of equations to

A Note with Quantiles of the Asymptotic Distribution of the Maximum Likelihood Cointegration Rank Test Statistics1

Abstract: The recent literature on maximum likelihood cointegration theory studies Gaussian vector autoregression (VAR) models allowing for some deterministic components in the form of polynomials in time. An examination is presented of such models for variables integrated at most of order one, when tests for cointegration involve statistics with non-standard asymptotic distributions. The asymptotic distributions of these test statistics are known to be functions of the distribution of certain matrices of stochastic variables involving integrals of Brownian motions. In fact, conditional on which restrictions on the coefficients of the polynomial in time are valid, different asymptotic distributions are obtained. The cases examined exhaust the hypotheses relevant to the cointegration rank analysis of I(1) variables in models involving up to linear trends and possibly seasonal dummies. The examination solves the numerical problem in making most of the interesting quantiles of these asymptotic distributions available to the applied econometrician.