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Title:Predictive inference under model misspecification with an application to assessing the marginal predictive content of money for output PDF Logo
Authors:Armah, Nii Ayi
Swanson, Norman R.
Issue Date:2011
Series/Report no.:Working Papers, Department of Economics, Rutgers, the State University of New Jersey 2011,03
Abstract:In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (CS: 2002) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).
Subjects:block bootstrap
forecasting
recursive estimation scheme
rolling estimation scheme
model misspecification
nonlinear causality
parameter estimation error
prediction
JEL:C22
C51
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, Rutgers University

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