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Optimal Inference in Regression Models with Nearly Integrated Regressors

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Author Info
Michael Jansson
Marcelo J. Moreira

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Abstract

This paper considers the problem of conducting inference on the regression coeffcient in a bivariate regression model with a highly persistent regressor. Gaussian power envelopes are obtained for a class of testing procedures satisfying a conditionality restriction. In addition, the paper proposes feasible testing procedures that attain these Gaussian power envelopes whether or not the innovations of the regression model are normally distributed.

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File URL: http://www.economics.harvard.edu/pub/hier/2004/HIER2047.pdf
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Paper provided by Harvard - Institute of Economic Research in its series Harvard Institute of Economic Research Working Papers with number 2047.

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Date of creation: 2004
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Handle: RePEc:fth:harver:2047

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  1. Wright, Jonathan H, 2000. "Confidence Sets for Cointegrating Coefficients Based on Stationarity Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 211-22, April.
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  1. Marc Hallin & Ramon van den Akker & Bas Werker, 2009. "A class of Simple Semiparametrically Efficient Rank-Based Unit Root Tests," ECARES Working Papers 2009_001, Université Libre de Bruxelles, Ecares. [Downloadable!]
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  2. Hjalmarsson, Erik, 2005. "Predictive regressions with panel data," Working Papers in Economics 160, Göteborg University, Department of Economics. [Downloadable!]
  3. Erik Hjalmarsson, 2006. "New methods for inference in long-run predictive regressions," International Finance Discussion Papers 853, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  4. Elena Pesavento, 2006. "Near-Optimal Unit Root Tests with Stationary Covariates with Better Finite Sample Size," Economics Working Papers ECO2006/18, European University Institute. [Downloadable!]
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  5. Pástor, Luboš & Stambaugh, Robert F, 2007. "Predictive Systems: Living with Imperfect Predictors," CEPR Discussion Papers 6076, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  6. Wei Liu & Alex Maynard, 2007. "A New Application of Exact Nonparametric Methods to Long-Horizon Predictability Tests," Studies in Nonlinear Dynamics & Econometrics, Berkeley Electronic Press, vol. 11(1), pages 1376-1376. [Downloadable!] (restricted)
  7. Erik Hjalmarsson, 2006. "Predictive regressions with panel data," International Finance Discussion Papers 869, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  8. Erik Hjalmarsson & Par Osterholm, 2007. "A residual-based cointegration test for near unit root variables," International Finance Discussion Papers 907, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  9. Erik Hjalmarsson, 2008. "Predicting global stock returns," International Finance Discussion Papers 933, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  10. Benjamin Chiquoine & Erik Hjalmarsson, 2008. "Jackknifing stock return predictions," International Finance Discussion Papers 932, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
  11. Alex Maynard & Katsumi Shimotsu, 2007. "Covariance-based orthogonality tests for regressors with unknown persistence," Working Papers 1122, Queen's University, Department of Economics. [Downloadable!]
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  12. Ulrich Müller & Mark W. Watson, 2009. "Low-Frequency Robust Cointegration Testing," NBER Working Papers 15292, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  13. Hjalmarsson, Erik, 2005. "On the Predictability of Global Stock Returns," Working Papers in Economics 161, Göteborg University, Department of Economics. [Downloadable!]
  14. Stanislav Anatolyev & Nikolay Gospodinov, 2007. "Modeling Financial Return Dynamics by Decomposition," Working Papers w0095, Center for Economic and Financial Research (CEFIR). [Downloadable!]
  15. Donald W.K. Andrews & Patrik Guggenberger, 2007. "Hybrid and Size-Corrected Subsample Methods," Cowles Foundation Discussion Papers 1606, Cowles Foundation, Yale University. [Downloadable!]
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