Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes

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Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes

Author Info

Norman Swanson () (Rutgers University)
Valentina Corradi () (Queen Mary, University of London)

Additional information is available for the following registered author(s):

Norman Rasmus Swanson

Abstract

Our objectives in this paper are twofold. First, we introduce block bootstrap techniques that are (first order) valid in recursive estimation frameworks. Thereafter, we present two examples where predictive accuracy tests are made operational using our new bootstrap procedures. In one application, we outline a consistent test for out-of-sample nonlinear Granger causality, and in the other we outline a test for selecting amongst multiple alternative forecasting models, all of which are possibly misspecified. More specifically, our examples extend the White (2000) reality check to the case of non vanishing parameter estimation error, and extend the integrated conditional moment tests of Bierens (1982, 1990) and Bierens and Ploberger (1997) to the case of out-of-sample prediction. In both examples, appropriate re-centering of the bootstrap score is required in order to ensure that the tests have asymptotically correct size, and the need for such re-centering is shown to arise quite naturally when testing hypotheses of predictive accuracy. In a Monte Carlo investigation, we compare the finite sample properties of our block bootstrap procedures with the parametric bootstrap due to Kilian (1999); all within the context of various encompassing and predictive accuracy tests. An empirical illustration is also discussed, in which it is found that unemployment appears to have nonlinear marginal predictive content for inflation.

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Paper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 200618.

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Length: 20 pages
Date of creation: 22 Sep 2006
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Handle: RePEc:rut:rutres:200618

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Article
- Valentina Corradi & Norman R. Swanson, 2007. "Nonparametric Bootstrap Procedures For Predictive Inference Based On Recursive Estimation Schemes," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(1), pages 67-109, 02. [Downloadable!] (restricted)

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

This paper has been announced in the following NEP Reports:

NEP-ALL-2007-03-10 (All new papers)
NEP-ECM-2007-03-10 (Econometrics)
NEP-ETS-2007-03-10 (Econometric Time Series)
NEP-FOR-2007-03-10 (Forecasting)

References listed on IDEAS
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repec:cup:macdyn:v:5:y:2001:i:4:p:598-620 is not listed on IDEAS
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Other versions:
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

Kenneth D. West & Todd Clark, 2006. "Approximately Normal Tests for Equal Predictive Accuracy in Nested Models," NBER Technical Working Papers 0326, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
Other versions:
- Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City. [Downloadable!]
- Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May. [Downloadable!] (restricted)
Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," Caepr Working Papers 2007-005_updated, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington. [Downloadable!]
Oleg Korenok & Stanislav Radchenko & Norman R. Swanson, 2006. "International Evidence on the Efficacy of new-Keynesian Models of Inflation Persistence," Working Papers 0602, VCU School of Business, Department of Economics. [Downloadable!]
Other versions:
- Norman Swanson & Oleg Korenok & Stanislav Radchenko, 2006. "International Evidence on the Efficacy of new-Keynesian Models of Inflation Persistence," Departmental Working Papers 200617, Rutgers University, Department of Economics. [Downloadable!]
Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics. [Downloadable!]
Ali Dib & Mohamed Gammoudi & Kevin Moran, 2006. "Forecasting Canadian Time Series With the New-Keynesian Model," Working Papers Central Bank of Chile 382, Central Bank of Chile. [Downloadable!]
Other versions:
- Ali Dib & Mohamed Gammoudi & Kevin Moran, 2005. "Forecasting Canadian Time Series with the New-Keynesian Model," Cahiers de recherche 0527, CIRPEE. [Downloadable!]
- Ali Dib & Mohamed Gammoudi & Kevin Moran, 2006. "Forecasting Canadian Time Series with the New Keynesian Model," Working Papers 06-4, Bank of Canada. [Downloadable!]
- Ali Dib & Mohamed Gammoudi & Kevin Moran, 2008. "Forecasting Canadian time series with the New Keynesian model," Canadian Journal of Economics, Canadian Economics Association, vol. 41(1), pages 138-165, February. [Downloadable!] (restricted)
Norman Swanson & Oleg Korenok, 2006. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models. Extended Working Paper Version," Departmental Working Papers 200612, Rutgers University, Department of Economics. [Downloadable!]

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