On the finite-sample accuracy of nonparametric resampling algorithms for economic time series

In recent years, there has been increasing interest in nonparametric bootst rap 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 d ynamic economic models in terms of their spectra, impulse responses, and re lated 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 accurac y of impulse response and spectral density bootstrap confidence intervals f or standard sample sizes. We find that the autoregressive sieve approach ba sed on the encompassing model is most accurate. However, care must be exerc ised in selecting the lag order of the autoregressive approximation.