Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output
Nii Ayi C. Armah
Rutgers University, New Brunswick/Piscataway, Faculty of Arts and Sciences-New Brunswick/Piscataway, Department of Economics
Norman R. Swanson
Rutgers University - Department of Economics
FORECASTING IN THE PRESENCE OF STRUCTURAL BREAKS AND MODEL UNCERTAINTY, Mark Wohar, ed., Elsevier, 2006
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).
Number of Pages in PDF File: 37
Keywords: block bootstrap, forecasting, recursive estimation scheme, rolling estimation scheme, model misspecification, nonlinear causality, parameter estimation error, prediction
JEL Classification: C22, C51Accepted Paper Series
Date posted: March 6, 2007
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