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

Jean-Marie Dufour () and Olivier Torrès

Cahiers de recherche from Universite de Montreal, Departement de sciences economiques

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.

Keywords: time series; Markov ocess; autoregressive ocess; autocorrelation; dynamic model; distributed lag model; two-sided autoregression; intercalary indendence; exact test; finite-same test; Ogawara-Hannan; investment (search for similar items in EconPapers)
JEL-codes: C15 C20 C42 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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http://hdl.handle.net/1866/336 (application/pdf)

Related works:
Journal Article: Markovian processes, two-sided autoregressions and finite-sample inference for stationary and nonstationary autoregressive processes (2000)
Working Paper: Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes (2000)
Working Paper: Markovian Progresses, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:2000-12

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