The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Heteroskedasticity and Autocorrelation Corrections

  • Kenneth D. West
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2369

Abstract

Many time series studies, including in particular those estimated by generalized method of moments, involve disturbances that are serially correlated and, possibly, conditionally heteroskedastic. The serial correlation and heteroskedasticity often are of unknown form. Corrections for serial correlation and heteroskedasticity are required for inference and efficient estimation. This article surveys procedures to implement such corrections.

Keywords

Autocovariances Bartlett kernel Generalized method of moments Heteroskedasticity Heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimation Kernel weights Long-run variance Moving average processes Newey–West estimator Quadratic spectral kernel Serial correlation Spectral density estimation Truncated estimators Vector autoregressions 
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Notes

Acknowledgment

I thank Steven Durlauf, Masayuki Hirukawa and Tim Vogelsgang for helpful comments, and the National Science Foundation for financial support.

Bibliography

  1. Andrews, D.W.K. 1991. Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59: 817–858.CrossRefGoogle Scholar
  2. Andrews, D.W.K., and J.C. Monahan. 1992. An improved heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 60: 953–966.CrossRefGoogle Scholar
  3. Cumby, R.E., J. Huizanga, and M. Obstfeld. 1983. Two step, two-stage least squares estimation in models with rational expectations. Journal of Econometrics 21: 333–355.CrossRefGoogle Scholar
  4. den Haan, W.J., and A.T. Levin. 1997. A practitioner’s guide to robust covariance matrix estimation. In Handbook of statistics: Robust inference, ed. G. Maddala and C. Rao, Vol. 15. New York: Elsevier.Google Scholar
  5. Eichenbaum, M.S., L.P. Hansen, and K.J. Singleton. 1988. A time series analysis of representative agent models of consumption and leisure choice under uncertainty. Quarterly Journal of Economics 103: 51–78.CrossRefGoogle Scholar
  6. Hamilton, J. 1994. Time series analysis. Princeton: Princeton University Press.Google Scholar
  7. Hannan, E.J. 1970. Multiple time series. New York: Wiley.CrossRefGoogle Scholar
  8. Hansen, L.P. 1982. Large sample properties of generalized method of moments estimators. Econometrica 50: 1029–1054.CrossRefGoogle Scholar
  9. Hansen, L.P., and R.J. Hodrick. 1980. Forward exchange rates as optimal predictors of future spot rates: An econometric analysis. Journal of Political Economy 96: 829–853.CrossRefGoogle Scholar
  10. Hansen, L.P., and K.J. Singleton. 1982. Generalized instrumental variables estimation of nonlinear rational expectations models. Econometrica 50: 1269–1286.CrossRefGoogle Scholar
  11. Hansen, B.E., and K.D. West. 2002. Generalized method of moments and macroeconomics. Journal of Business and Economic Statistics 20: 460–469.CrossRefGoogle Scholar
  12. Hirukawa, M. 2006. A modified nonparametric prewhitened covariance estimator. Journal of Time Series Analysis 27: 441–476.CrossRefGoogle Scholar
  13. Hodrick, R.J. 1992. Dividend yields and expected stock returns: Alternative procedures for inference and measurement. Review of Financial Studies 5: 357–386.CrossRefGoogle Scholar
  14. Jansson, M. 2004. The error in rejection probability of simple autocorrelation robust tests. Econometrica 72: 937–946.CrossRefGoogle Scholar
  15. Kiefer, N.M., and T.J. Vogelsang. 2002. Heteroskedasticity-autocorrelation robust standard errors using the Bartlett kernel without truncation. Econometrica 70: 2093–2095.CrossRefGoogle Scholar
  16. Kiefer, N.M., and T.J. Vogelsang. 2005. A new asymptotic theory for heteroskedasticity-autocorrelation robust tests. Econometric Theory 21: 1130–1164.CrossRefGoogle Scholar
  17. Kiefer, N.M., T.J. Vogelsang, and H. Bunzel. 2000. Simple robust testing of regression hypotheses. Econometrica 68: 695–714.CrossRefGoogle Scholar
  18. Newey, W.K., and K.D. West. 1987. A simple, positive semidefinite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55: 703–708.CrossRefGoogle Scholar
  19. Newey, W.K., and K.D. West. 1994. Automatic lag selection in covariance matrix estimation. Review of Economic Studies 61: 631–654.CrossRefGoogle Scholar
  20. Phillips, P.C.B., Y. Sun, and S. Jin. 2006. Spectral density estimation and robust hypothesis testing using steep origin kernels without truncation. International Economic Review 47: 837–894.CrossRefGoogle Scholar
  21. Phillips, P.C.B., Y. Sun, and S. Jin. 2007. Long run variance estimation and robust regression testing using sharp origin kernels with no truncation. Journal of Statistical Planning and Inference 137: 985–1023.CrossRefGoogle Scholar
  22. Politis, D.N., and J.P. Romano. 1995. Bias-corrected nonparametric spectral estimation. Journal of Time Series Analysis 16: 67–103.CrossRefGoogle Scholar
  23. Priestley, M.B. 1981. Spectral Analysis and Time Series. New York: Academic Press.Google Scholar
  24. West, K.D. 1997. Another heteroskedasticity and autocorrelation consistent covariance matrix estimator. Journal of Econometrics 76: 171–191.CrossRefGoogle Scholar
  25. White, H. 1980. A heteroskedasticity consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48: 817–838.CrossRefGoogle Scholar
  26. Xiao, Z., and O. Linton. 2002. A nonparametric prewhitened covariance estimator. Journal of Time Series Analysis 23: 215–250.CrossRefGoogle Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Kenneth D. West
    • 1
  1. 1.