Keywords: Bootstrap, nonparametric, time series
Abstract: 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
Full paper (259 KB PDF)
| Full paper (1313 KB Postscript)
Home | Economic research and data | FR working papers | FEDS | 1999 FEDS papers
To comment on this site, please fill out our feedback form.
Last update: March 2, 1999