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Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Jean-Marie Dufour () and Tarek Jouini

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

Abstract: Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Keywords: Vector autoregression; VAR; exact test; Monte Carlo test; maximized Monte Carlo test; bootstra; Granger causality; order selection; nonstationary model; macroeconomics; money and income; interest rate; inflation (search for similar items in EconPapers)
JEL-codes: C32 C12 C15 E4 E5 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
Date: 2005
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http://hdl.handle.net/1866/541 (application/pdf)

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Working Paper: Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing (2005) Downloads
Working Paper: Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing (2005) Downloads
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Persistent link: https://EconPapers.repec.org/RePEc:mtl:montde:2005-12

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