21 A perspective on application of bootstrap methods in econometrics

Abstract

This chapter presents a review of the several applications of bootstrap in econometrics. Almost every type of model used in econometric work has been bootstrapped: regression models with heteroskedastic and autocorrelated errors, seemingly unrelated regression models, models with lagged dependent variables, state–space models and the Kalman filter, panel data models, simultaneous equation models, logit, probit, tobit, and other limited dependent variable models, generalized autoregressive conditional heteroskedasticity (GARCH) models, robust estimators (LAD estimators), data mining, pretesting, James–Stein estimation, semi-parametric estimators, and so on. Estimation of standard errors of parameters, confidence intervals for parameters and generating forecast intervals (for multiperiod forecasts), have been considered in the chapter. In some models, the asymptotic theory of the estimator is intractable (Manski's maximum score estimator). In such cases, bootstrap provides a tractable method of deriving confidence intervals and so on. The computational advances, like the use of balanced sampling, importance sampling, antithetic variates, and so, do not seem to have been implemented in econometric work. These methods, if properly used, would substantially increase the efficiency of bootstrap computations, and it would be possible to use more bootstrap samples with no extra computational burden. An important point to remember is that bootstrapping defective models is of no value. Bootstrap does not rescue bad models.