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

  • Norman R. Swanson

    ()

    (Rutgers University)

  • Nii Ayi Armah

    ()

    (Bank of Canada)

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).

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Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201103.

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Length: 20 pages
Date of creation: 14 May 2011
Date of revision:
Publication status: Published in Forecasting in the Presence of Structural Breaks and Model Uncertainty, eds. Mark Wohar, Emerald, Bingley, UK, pp. 195-230.
Handle: RePEc:rut:rutres:201103
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