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Yale University

Cowles Foundation Discussion Papers

Series

2009

Misspecification

Articles 1 - 2 of 2

Full-Text Articles in Social and Behavioral Sciences

Optimal Comparison Of Misspecified Moment Restriction Models Under A Chosen Measure Of Fit, Vadim Marmer, Taisuke Otsu Aug 2009

Optimal Comparison Of Misspecified Moment Restriction Models Under A Chosen Measure Of Fit, Vadim Marmer, Taisuke Otsu

Cowles Foundation Discussion Papers

Suppose that the econometrician is interested in comparing two misspecified moment restriction models, where the comparison is performed in terms of some chosen measure of fit. This paper is concerned with describing an optimal test of the Vuong (1989) and Rivers and Vuong (2002) type null hypothesis that the two models are equivalent under the given measure of fit (the ranking may vary for different measures). We adopt the generalized Neyman-Pearson optimality criterion, which focuses on the decay rates of the type I and II error probabilities under fixed non-local alternatives, and derive an optimal but practically infeasible test. Then, …


Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C.B. Phillips Jun 2009

Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C.B. Phillips

Cowles Foundation Discussion Papers

Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The requisite limit theory involves sample covariances of integrable transformations of non-stationary sequences and time translated sequences, allowing for the presence of a bandwidth parameter so as to accommodate kernel regression. The theory is an extension of Wang and Phillips (2008) and is useful for the analysis of nonparametric regression models with a misspecified lag structure and in situations where temporal aggregation issues arise. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression …