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Estimators For Persistent And Possibly Nonstationary Data With Classical Properties

  • Gorodnichenko, Yuriy
  • Mikusheva, Anna
  • Ng, Serena

This paper considers a moments-based nonlinear estimator that is -consistent and uniformly asymptotically normal irrespective of the degree of persistence of the forcing process. These properties hold for linear autoregressive models, linear predictive regressions, and certain nonlinear dynamic models. Asymptotic normality is obtained because the moments are chosen so that the objective function is uniformly bounded in probability and so that a central limit theorem can be applied. Critical values from the normal distribution can be used irrespective of the treatment of the deterministic terms. Simulations show that the estimates are precise and the t -test has good size in the parameter region where the least squares estimates usually yield distorted inference.

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Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 28 (2012)
Issue (Month): 05 (October)
Pages: 1003-1036

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Handle: RePEc:cup:etheor:v:28:y:2012:i:05:p:1003-1036_00
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  1. Chirok Han & Peter C.B. Phillips & Donggyu Sul, 2010. "Uniform Asymptotic Normality in Stationary and Unit Root Autoregression," Cowles Foundation Discussion Papers 1746, Cowles Foundation for Research in Economics, Yale University.
  2. Laroque, Guy & Salanie, Bernard, 1997. "Normal estimators for cointegrating relationships," Economics Letters, Elsevier, vol. 55(2), pages 185-189, August.
  3. Olivier Coibion & Yuriy Gorodnichenko, 2011. "Strategic Interaction among Heterogeneous Price-Setters in an Estimated DSGE Model," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 920-940, August.
  4. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.
  5. Peter C. B. Phillips & Chirok Han, 2006. "Gaussian Inference in AR(1) Time Series with or without a Unit Root," Cowles Foundation Discussion Papers 1546, Cowles Foundation for Research in Economics, Yale University.
  6. Rossi, Barbara & Pesavento, Elena, 2003. "Small Sample Confidence Intervals for Multivariate Impulse Response Functions at Long Horizons," Working Papers 03-19, Duke University, Department of Economics.
  7. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-45, March.
  8. Eugene Canjels & Mark W. Watson, 1994. "Estimating Deterministic Trends in the Presence of Serially Correlated Errors," NBER Technical Working Papers 0165, National Bureau of Economic Research, Inc.
  9. Michael Jansson & Marcelo J. Moreira, 2004. "Optimal Inference in Regression Models with Nearly Integrated Regressors," NBER Technical Working Papers 0303, National Bureau of Economic Research, Inc.
  10. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  11. Peter C. B. Phillips & Zhijie Xiao, 1998. "A Primer on Unit Root Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 423-470, December.
  12. So, Beong Soo & Shin, Dong Wan, 1999. "Cauchy Estimators For Autoregressive Processes With Applications To Unit Root Tests And Confidence Intervals," Econometric Theory, Cambridge University Press, vol. 15(02), pages 165-176, April.
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