Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output

Norman Swanson () and Nii Ayi Armah ()
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Nii Ayi Armah: Bank of Canada

Departmental Working Papers from Rutgers University, Department of Economics

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).

Keywords: block bootstrap; recursive estimation scheme; rolling estimation scheme; prediction; nonlinear causality (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
Pages: 20 pages
Date: 2011-05-14
New Economics Papers: this item is included in nep-for
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Published in Forecasting in the Presence of Structural Breaks and Model Uncertainty, eds. Mark Wohar, Emerald, Bingley, UK, pp. 195-230.

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Related works:
Working Paper: Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:rut:rutres:201103

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