IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v28y2012i05p1003-1036_00.html
   My bibliography  Save this article

Estimators For Persistent And Possibly Nonstationary Data With Classical Properties

Author

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

Abstract

This paper considers a moments-based nonlinear estimator that is $\root \of T $-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.

Suggested Citation

  • Gorodnichenko, Yuriy & Mikusheva, Anna & Ng, Serena, 2012. "Estimators For Persistent And Possibly Nonstationary Data With Classical Properties," Econometric Theory, Cambridge University Press, vol. 28(5), pages 1003-1036, October.
    • Handle: RePEc:cup:etheor:v:28:y:2012:i:05:p:1003-1036_00
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0266466612000035/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
    2. Elena Pesavento & Barbara Rossi, 2006. "Small‐sample confidence intervals for multivariate impulse response functions at long horizons," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1135-1155, December.
    3. Han, Chirok & Kim, Beomsoo, 2011. "A GMM interpretation of the paradox in the inverse probability weighting estimation of the average treatment effect on the treated," Economics Letters, Elsevier, vol. 110(2), pages 163-165, February.
    4. Michael Jansson & Marcelo J. Moreira, 2006. "Optimal Inference in Regression Models with Nearly Integrated Regressors," Econometrica, Econometric Society, vol. 74(3), pages 681-714, May.
    5. Han, Chirok & Phillips, Peter C. B. & Sul, Donggyu, 2011. "Uniform Asymptotic Normality In Stationary And Unit Root Autoregression," Econometric Theory, Cambridge University Press, vol. 27(6), pages 1117-1151, December.
    6. 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.
    7. Phillips, Peter C.B. & Han, Chirok, 2008. "Gaussian Inference In Ar(1) Time Series With Or Without A Unit Root," Econometric Theory, Cambridge University Press, vol. 24(3), pages 631-650, June.
    8. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-445, March.
    9. Prokhorov, Artem & Schmidt, Peter, 2009. "GMM redundancy results for general missing data problems," Journal of Econometrics, Elsevier, vol. 151(1), pages 47-55, July.
    10. Eugene Canjels & Mark W. Watson, 1997. "Estimating Deterministic Trends In The Presence Of Serially Correlated Errors," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 184-200, May.
    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(2), pages 165-176, April.
    13. repec:bla:jecsur:v:12:y:1998:i:5:p:423-69 is not listed on IDEAS
    14. Laroque, Guy & Salanie, Bernard, 1997. "Normal estimators for cointegrating relationships," Economics Letters, Elsevier, vol. 55(2), pages 185-189, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    2. Sentana, Enrique, 2024. "Finite underidentification," Journal of Econometrics, Elsevier, vol. 240(1).
    3. John C. Chao & Peter C. B. Phillips, 2019. "Uniform Inference in Panel Autoregression," Econometrics, MDPI, vol. 7(4), pages 1-28, November.
    4. Demetrescu, Matei & Georgiev, Iliyan & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2022. "Testing for episodic predictability in stock returns," Journal of Econometrics, Elsevier, vol. 227(1), pages 85-113.
    5. Breitung, Jörg & Demetrescu, Matei, 2015. "Instrumental variable and variable addition based inference in predictive regressions," Journal of Econometrics, Elsevier, vol. 187(1), pages 358-375.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
    2. 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.
    3. repec:spo:wpecon:info:hdl:2441/6ggbvnr6munghes9od0s108ro is not listed on IDEAS
    4. Müller, Ulrich K. & Wang, Yulong, 2019. "Nearly weighted risk minimal unbiased estimation," Journal of Econometrics, Elsevier, vol. 209(1), pages 18-34.
    5. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2016. "Matching, Sorting and Wages," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 19, pages 63-87, January.
    6. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2013. "Mismatch, Sorting and Wage Dynamics," Cowles Foundation Discussion Papers 1886, Cowles Foundation for Research in Economics, Yale University.
    7. Benjamin Friedrich & Costas Meghir & Lisa Laun & Luigi Pistaferri, 2018. "Earnings Dynamics and Firm-Level Shocks," 2018 Meeting Papers 536, Society for Economic Dynamics.
    8. Polbin, Andrey & Sinelnikov-Murylev, Sergey, 2024. "Developing and impulse response matching estimation of the DSGE model for the Russian economy," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 73, pages 5-34.
    9. Rodrigues, Paulo M. M. & Taylor, A. M. Robert, 2004. "Alternative estimators and unit root tests for seasonal autoregressive processes," Journal of Econometrics, Elsevier, vol. 120(1), pages 35-73, May.
    10. repec:spo:wpmain:info:hdl:2441/6ggbvnr6munghes9od0s108ro is not listed on IDEAS
    11. repec:hal:journl:hal-01070442 is not listed on IDEAS
    12. Xiao, Zhijie, 2004. "Estimating average economic growth in time series data with persistency," Journal of Macroeconomics, Elsevier, vol. 26(4), pages 699-724, December.
    13. Chengwang Liao & Ziwei Mei & Zhentao Shi, 2024. "Nickell Meets Stambaugh: A Tale of Two Biases in Panel Predictive Regressions," Papers 2410.09825, arXiv.org.
    14. Breitung, Jörg & Demetrescu, Matei, 2015. "Instrumental variable and variable addition based inference in predictive regressions," Journal of Econometrics, Elsevier, vol. 187(1), pages 358-375.
    15. Jhih-Gang Chen & Biing-Shen Kuo, 2013. "Gaussian inference in general AR(1) models based on difference," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 447-453, July.
    16. repec:hal:spmain:info:hdl:2441/78hlmdbud88hhp5vbdddivv2hu is not listed on IDEAS
    17. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2016. "Matching, Sorting, and Wages," SciencePo Working papers hal-03392023, HAL.
    18. Jeremy Lise & Costas Meghir & Jean-Marc Robin, 2013. "Mismatch, Sorting and Wages Dynamics," SciencePo Working papers hal-03473908, HAL.
    19. Michael Jansson, 2008. "Semiparametric Power Envelopes for Tests of the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 76(5), pages 1103-1142, September.
    20. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2007. "A simple, robust and powerful test of the trend hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 1302-1330, December.
    21. Anna Kormilitsina & Denis Nekipelov, 2016. "Consistent Variance Of The Laplace‐Type Estimators: Application To Dsge Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(2), pages 603-622, May.
    22. repec:hal:spmain:info:hdl:2441/32h1padvln887b3u0epa07o4ub is not listed on IDEAS
    23. repec:spo:wpmain:info:hdl:2441/78hlmdbud88hhp5vbdddivv2hu is not listed on IDEAS
    24. Zongwu Cai & Seong Yeon Chang, 2018. "A New Test In A Predictive Regression with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201811, University of Kansas, Department of Economics, revised Dec 2018.
    25. Ruidong Han & Xinghui Wang & Shuhe Hu, 2018. "Asymptotics of the weighted least squares estimation for AR(1) processes with applications to confidence intervals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 479-490, August.
    26. Xu, Ke-Li, 2016. "Multivariate trend function testing with mixed stationary and integrated disturbances," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 38-57.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:etheor:v:28:y:2012:i:05:p:1003-1036_00. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ect .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.