Helmut Lütkepohl

Applied time series econometrics

Time series econometrics is a rapidly evolving field. Particularly, the cointegration revolution has had a substantial impact on applied analysis. Hence, no textbook has managed to cover the full range of methods in current use and explain how to proceed in applied domains. This gap in the literature motivates the present volume. The methods are sketched out, reminding the reader of the ideas underlying them and giving sufficient background for empirical work. The treatment can also be used as a textbook for a course on applied time series econometrics. Topics include: unit root and cointegration analysis, structural vector autoregressions, conditional heteroskedasticity and nonlinear and nonparametric time series models. Crucial to empirical work is the software that is available for analysis. New methodology is typically only gradually incorporated into existing software packages. Therefore a flexible Java interface has been created, allowing readers to replicate the applications and conduct their own analyses.

Making Wald Tests Work for Cointegrated VAR Systems

Wald tests of restrictions on the coefficients of vector autoregressive (VAR) processes are known to have nonstandard asymptotic properties for 1(1) and cointegrated systems of variables. A simple device is proposed which guarantees that Wald tests have asymptotic X2-distributions under general conditions. If the true generation process is a VAR(p) it is proposed to fit a VAR(p+l) to the data and perform a Wald test on the coefficients of the first p lags only. The power properties of the modified tests are studied both analytically and numerically by means of simple illustrative examples.

Non-causality due to omitted variables

Abstract Generally the causal structure of a subprocess of a multivariate stochastic process does not allow conclusions concerning the causal structure of the higher dimensional process. It is well- known that Granger-causality in a bivariate system may be due to an omitted variable. It is also known that non-causality in a bivariate system may theoretically result from neglected variables. Using Canadian income, money and interest rate it is demonstrated that this actually occurs in practice.

Impulse response analysis of cointegrated systems

Abstract Impulse response or dynamic multiplier analysis of vector autoregressive systems with cointegrated variables is considered. The asymptotic distribution of the responses estimated with Johansens (1988) maximum likelihood procedure is derived. The results are illustrated with an analysis of a West German money demand system. The investigation shows that a direct interpretation of the cointegration relations may be difficult or misleading. Thereby the virtue of impulse response analysis for applied work is illustrated.

Comparison of unit root tests for time series with level shifts

Unit root tests are considered for time series which have a level shift at a known point in time. The shift can have a very general nonlinear form, and additional deterministic mean and trend terms are allowed for. Prior to the tests, the deterministic parts and other nuisance parameters of the data generation process are estimated in a first step. Then, the series are adjusted for these terms and unit root tests of the Dickey–Fuller type are applied to the adjusted series. The properties of previously suggested tests of this sort are analysed and modifications are proposed which take into account estimation errors in the nuisance parameters. An important result is that estimation under the null hypothesis is preferable to estimation under local alternatives. This contrasts with results obtained by other authors for time series without level shifts.

TESTING FOR A UNIT ROOT IN A TIME SERIES WITH A LEVEL SHIFT AT UNKNOWN TIME

Unit root tests for time series with level shifts of general form are considered when the timing of the shift is unknown. It is proposed to estimate the nuisance parameters of the data generation process including the shift date in a first step and apply standard unit root tests to the residuals. The estimation of the nuisance parameters is done in such a way that the unit root tests on the residuals have limiting distributions for which critical values are tabulated elsewhere in the literature. Empirical examples are discussed to illustrate the procedure.

Testing for the Cointegrating Rank of a VAR Process with Structural Shifts

Tests for the cointegrating rank of a vector autoregressive process are considered that allow for possible exogenous shifts in the mean of the data-generation process. The break points are assumed to be known a priori. It is proposed to estimate and remove the deterministic terms such as mean, linear-trend term, and a shift in a first step. Then systems cointegration tests are applied to the adjusted series. The resulting tests are shown to have known limiting null distributions that are free of nuisance parameters and do not depend on the break point. The tests are applied for analyzing the number of cointegrating relations in two German money-demand systems.

Asymptotic Distributions of Impulse Response Functions and Forecast Error Variance Decompositions of Vector Autoregressive Models

In recent years, vector autoregressive models have become standard tools for economic analyses. Impulse response functions and forecast error variance decompositions are usually computed from these models in order to investigate the interrelationships within the system. However, sometimes no measures of estimation uncertainty are provided by authors. One reason may be that the relevant asymptotic distribution theory is distributed over various publications. In this article, the available results are summarized and the missing links are provided in order to facilitate the computation of standard errors and test statistics. Copyright 1990 by MIT Press.

TESTING FOR THE COINTEGRATING RANK OF A VAR PROCESS WITH AN INTERCEPT

Testing the cointegrating rank of a vector autoregressive process with an intercept is considered. In addition to the likelihood ratio (LR) tests developed by Johansen and Juselius and others we also consider an alternative class of tests which is based on estimating the trend parameters of the deterministic term in a different way. The asymptotic local power of these tests is derived and compared to that of the corresponding LR tests. The small sample properties are investigated by simulations. The new tests are seen to be substantially more powerful than conventional LR tests.

Maximum eigenvalue versus trace tests for the cointegrating rank of a VAR process

The properties of a range of maximum eigenvalue and trace tests for the cointegrating rank of a vector autoregressive process are compared. The tests are alilikelihood ratio type tests and operate under different assumptions regarding the deterministic part of the data generation process. The asymptotic distributions under local alternatives are given and the local power is derived. It is found that the local power of corresponding maximum eigenvalue and trace tests is very similar. A Monte Carlo comparison shows, however, that there may be slight differences in small sampies. The trace tests tend to have more distorted sizes whereas their power is in some situations superior to that of the maximum eigenvalue tests.