In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation.
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Paper provided by Michigan - Center for Research on Economic & Social Theory in its series Papers with number
99-01.
Length: 58 pages Date of creation: 1999 Date of revision: Handle: RePEc:fth:michet:99-01
Contact details of provider: Postal: UNIVERSITY OF MICHIGAN, DEPARTMENT OF ECONOMICS CENTER FOR RESEARCH ON ECONOMIC AND SOCIAL THEORY, ANN ARBOR MICHIGAN U.S.A.
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Find related papers by JEL classification: C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
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Francis X. Diebold & Lutz Kilian & Marc Nerlove, 2006.
"Time Series Analysis,"
PIER Working Paper Archive
06-019, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
[Downloadable!]
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Diebold, F.X. & Kilian, L. & Nerlove, M., 2006.
"Time Series Analysis,"
Working Papers
28556, University of Maryland, Department of Agricultural and Resource Economics.
[Downloadable!]
Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 1998.
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NBER Working Papers
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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.)
Lutz Kilian & Alessandro Rebucci & Nikola Spatafora, 2007.
"Oil Shocks and External Balances,"
Working Papers
562, Research Seminar in International Economics, University of Michigan.
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