Inference in Models with Nearly Integrated Regressors

Abstract

This paper examines regression tests of whether x forecasts y when the largest autoregressive root of the regressor is unknown. It is shown that previously proposed two-step procedures, with first stages that consistently classify x as I(1) or I(0), exhibit large size distortions when regressors have local-to-unit roots, because of asymptotic dependence on a nuisance parameter that cannot be estimated consistently. Several alternative procedures, based on Bonferroni and Scheffe methods, are therefore proposed and investigated. For many parameter values, the power loss from using these conservative tests is small.

Keywords

MathematicsNuisance parameterBonferroni correctionEconometricsAutoregressive modelUnit rootInferenceStatisticsRegressionApplied mathematicsArtificial intelligenceComputer science

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Publication Info

Year: 1995
Type: article
Volume: 11
Issue: 5
Pages: 1131-1147
Citations: 381
Access: Closed

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APA Style

                            
                                    Christopher L. Cavanagh, 
                                
                                    Graham Elliott, 
                                
                                    James H. Stock
                                
                            (1995). 
                            Inference in Models with Nearly Integrated Regressors. 
                            Econometric Theory
                            , 11
                            (5)
                            , 1131-1147.
                            https://doi.org/10.1017/s0266466600009981

Identifiers

DOI: 10.1017/s0266466600009981