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Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes

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  • Jean-Marie Dufour

    ()

  • Olivier Torrès

Abstract

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method. Dans cet article, nous proposons des procédures d'inférence valides à distance finie pour des modèles autorégressifs (AR) stationnaires et non-stationnaires. La méthode suggérée est fondée sur des propriétés particulières des processus markoviens combinées à une technique de subdivision d'échantillon. Les résultats sur les processus de Markov (indépendance intercalaire, troncature) ne requièrent que l'existence de densités conditionnelles. Nous démontrons les propriétés requises pour des processus markoviens multivariés possiblement non-stationnaires et non-gaussiens. Pour le cas des modèles de régression linéaires avec erreurs autorégressives d'ordre un, nous montrons comment utiliser ces résultats afin de simplifier les propriétés distributionnelles du modèle en considérant la distribution conditionnelle d'une partie des observations étant donné le reste. Cette transformation conduit à un nouveau modèle qui a la forme d'une autorégression bilatérale à laquelle on peut appliquer les techniques usuelles d'analyse des modèles de régression linéaires. Nous montrons comment obtenir des tests et régions de confiance pour la moyenne et les paramètres autorégressifs du modèle. Nous proposons aussi un test pour l'ordre d'une autorégression. Nous montrons qu'une technique de combinaison de tests obtenus à partir de plusieurs sous-échantillons peut améliorer la performance de la procédure. Enfin la méthode est appliquée à un modèle de l'investissement aux États-Unis.

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Paper provided by CIRANO in its series CIRANO Working Papers with number 2000s-17.

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Date of creation: 01 May 2000
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Handle: RePEc:cir:cirwor:2000s-17

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Keywords: Time series; Markov process; autoregressive process; autocorrelation; dynamic model; distributed-lag model; two-sided autoregression; intercalary independence; exact test; finite-sample test; Ogawara-Hannan; investment; Séries chronologiques; processus de Markov; processus autorégressif; autocorrélation; modèle dynamique; modèle à retards échelonnés; autorégression bilatérale; indépendance intercalaire; test exact; Ogawara-Hannan; investissement;

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  1. Dufour, Jean-Marie, 1990. "Exact Tests and Confidence Sets in Linear Regressions with Autocorrelated Errors," Econometrica, Econometric Society, vol. 58(2), pages 475-94, March.
  2. Maasoumi, Esfandiar, 1992. "Fellow's opinion : Rules of thumb and pseudo-science," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 1-4.
  3. Nankervis, John C & Savin, N E, 1996. "The Level and Power of the Bootstrap t Test in the AR(1) Model with Trend," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 161-68, April.
  4. Dufour, Jean-Marie, 1989. "Nonlinear Hypotheses, Inequality Restrictions, and Non-nested Hypotheses: Exact Simultaneous Tests in Linear Regressions," Econometrica, Econometric Society, vol. 57(2), pages 335-55, March.
  5. Savin, N.E. & Wurtz, A., 1996. "The Effect of Nuisance Parameters on the Power of LM Tests in Logit and Probit Models," Working Papers 96-05, University of Iowa, Department of Economics.
  6. Dufour, J.M. & Kiviet, J.F., 1995. "Exact Inference Methods for First-Order Autoregressive Distributed Lag Models," Cahiers de recherche 9547, Universite de Montreal, Departement de sciences economiques.
  7. DeJong, David N. & Nankervis, John C. & Savin, N. E. & Whiteman, Charles H., 1992. "The power problems of unit root test in time series with autoregressive errors," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 323-343.
  8. Dufour, J.M. & Kiviet, J.F., 1995. "Exact Tests in Single Equation Autoregressive Distributed Lag Models," Cahiers de recherche 9549, Universite de Montreal, Departement de sciences economiques.
  9. Marc Hallin & Jean-Marie Dufour & Ivan Mizera, 1998. "Generalized run tests for heteroscedastic time series," ULB Institutional Repository 2013/2077, ULB -- Universite Libre de Bruxelles.
  10. Jean-Marie Dufour, 1997. "Some Impossibility Theorems in Econometrics with Applications to Structural and Dynamic Models," Econometrica, Econometric Society, vol. 65(6), pages 1365-1388, November.
  11. Burnside, Craig & Eichenbaum, Martin S, 1996. "Small-Sample Properties of GMM-Based Wald Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 294-308, July.
  12. Miyazaki, Shigetaka & Griffiths, William E., 1984. "The properties of some covariance matrix estimators in linear models with AR(1) errors," Economics Letters, Elsevier, vol. 14(4), pages 351-356.
  13. Park, Rolla Edward & Mitchell, Bridger M., 1980. "Estimating the autocorrelated error model with trended data," Journal of Econometrics, Elsevier, vol. 13(2), pages 185-201, June.
  14. Craig Burnside & Martin Eichenbaum, 1994. "Small sample properties of generalized method of moments based Wald tests," Working Paper Series, Macroeconomic Issues 94-12, Federal Reserve Bank of Chicago.
  15. Campbell, B. & Dufour, J.M., 1994. "Excat Nonparametric Tests of Orthogonality and Random Walk in the Presence of a Drift Parameter," Cahiers de recherche 9407, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  16. Dufour, Jean-Marie & Kiviet, Jan F., 1996. "Exact tests for structural change in first-order dynamic models," Journal of Econometrics, Elsevier, vol. 70(1), pages 39-68, January.
  17. Savin, N.E., 1984. "Multiple hypothesis testing," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 14, pages 827-879 Elsevier.
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Cited by:
  1. DUFOUR, Jean-Marie & KHALAF, Lynda, 2000. "Exact Tests for Contemporaneous Correlation of Disturbances in Seemingly Unrelated Regressions," Cahiers de recherche 2000-11, Universite de Montreal, Departement de sciences economiques.
  2. Gossner, Olivier & Schlag, Karl H., 2013. "Finite-sample exact tests for linear regressions with bounded dependent variables," Journal of Econometrics, Elsevier, vol. 177(1), pages 75-84.
  3. Luger, Richard, 2001. "Exact Non-Parametric Tests for a Random Walk with Unknown Drift under Conditional Heteroscedasticity," Working Papers 01-2, Bank of Canada.
  4. Dufour, J.M. & Khalaf, L. & Bernard, J.T. & Genest, I., 2001. "Simulation-Based Finite-Sample Tests for Heteroskedasticity and ARCH Effects," Cahiers de recherche 2001-08, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  5. DUFOUR, Jean-Marie & FARHAT, Abdeljelil, 2001. "Exact Nonparametric Two-Sample Homogeneity Tests for Possibly Discrete Distributions," Cahiers de recherche 2001-23, Universite de Montreal, Departement de sciences economiques.

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