Testing linear regression model with AR(1) errors against a first-order dynamic linear regression model with white noise errors: A point optimal testing approach

Sivagowry Sriananthakumar

Economic Modelling, 2013, vol. 33, issue C, 126-136

Abstract: We know very little about the performance of point optimal (PO) and approximate point optimal (APO) tests in the presence of unavoidable nuisance parameters. Because marginal likelihood based tests are said to perform well in the presence of unavoidable nuisance parameters, this paper compares the performance of marginal likelihood based APO tests and classical tests using a testing problem which has been largely overlooked by econometric practitioners, namely testing for a static linear regression model with AR(1) errors against a dynamic linear regression model with white noise errors. It is well known that the classical tests are specifically designed for nested testing, they are applied to test for the significance of the dynamic coefficient of a dynamic linear regression model with AR(1) errors.

Keywords: Approximate point optimal test; Marginal likelihood based tests; Simulated annealing algorithm; Monte Carlo simulations; Generalized Neyman–Pearson lemma; Nuisance parameters (search for similar items in EconPapers)
JEL-codes: C1 C2 C4 C5 C8 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:33:y:2013:i:c:p:126-136

DOI: 10.1016/j.econmod.2013.03.022

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