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Estimation of AR(1) models with unequally spaced pseudo-panels

Author

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  • DAVID J. MCKENZIE

Abstract

Surveys in developing countries are often taken at unequally spaced intervals. This paper provides for the estimation of dynamic pseudo-panel models with such data. Non-linear least squares, minimum distance, and one-step estimators are used to impose the non-linear parameter restrictions which occur in dynamic models over unequally spaced periods. Consistency and asymptotic normality of the estimators is established. A small-scale Monte Carlo simulation study corroborates the results. The paper also shows how these methods can be applied to allow estimation of dynamic models with irregularly spaced genuine panel data.

Suggested Citation

  • David J. Mckenzie, 2001. "Estimation of AR(1) models with unequally spaced pseudo-panels," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-40.
    • Handle: RePEc:ect:emjrnl:v:4:y:2001:i:1:p:40
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    Cited by:

    1. Raf Van Gestel & Tobias Müller & Johan Bosmans, 2018. "Learning from failure in healthcare: Dynamic panel evidence of a physician shock effect," Health Economics, John Wiley & Sons, Ltd., vol. 27(9), pages 1340-1353, September.
    2. Vaona, Andrea, 2015. "Price–price deviations are highly persistent," Structural Change and Economic Dynamics, Elsevier, vol. 33(C), pages 86-95.
    3. Luisa Corrado & Roberta Distante & Majlinda Joxhe, 2019. "Body mass index and social interactions from adolescence to adulthood," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(4), pages 425-445, October.
    4. Sasaki, Yuya & Xin, Yi, 2017. "Unequal spacing in dynamic panel data: Identification and estimation," Journal of Econometrics, Elsevier, vol. 196(2), pages 320-330.
    5. Hanig, Ross, 2006. "The Returns to Wages for Mexican Workers in a Post NAFTA World: Has the Gap in Returns to Wages Grown Between Mexican Workers in the U.S. and their Domestic Counterparts?," 2006 Annual meeting, July 23-26, Long Beach, CA 21385, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Greß, Stefan & Tamm, Marcus & Tauchmann, Harald & Wasem, Jürgen, 2005. "Price Elasticities and Social Health Insurance Choice in Germany: A Dynamic Panel Data Approach," RWI Discussion Papers 28, RWI - Leibniz-Institut für Wirtschaftsforschung.
    7. Francisca Antman & David McKenzie, 2007. "Poverty traps and nonlinear income dynamics with measurement error and individual heterogeneity," Journal of Development Studies, Taylor & Francis Journals, vol. 43(6), pages 1057-1083.
    8. Khalaf, Lynda & Kichian, Maral & Saunders, Charles J. & Voia, Marcel, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Journal of Econometrics, Elsevier, vol. 220(2), pages 589-605.
    9. Colin Black & David C Broadstock & Alan Collins & Lester C Hunt, 2006. "Food Superstores, Food Deserts and Traffic Generation in the UK: A Semi-Parametric Regression Approach," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 112, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    10. Hamza Bennani & Matthias Neuenkirch, 2017. "The (home) bias of European central bankers: new evidence based on speeches," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1114-1131, March.
    11. Steele, Fiona & Grundy, Emily, 2021. "Random effects dynamic panel models for unequally-spaced multivariate categorical repeated measures: an application to child-parent exchanges of support," LSE Research Online Documents on Economics 106255, London School of Economics and Political Science, LSE Library.
    12. repec:zbw:rwidps:0028 is not listed on IDEAS
    13. Marcus Tamm & Harald Tauchmann & Stefan Greß & Jürgen Wasem, 2005. "Price Elasticities and Social Health Insurance Choice in Germany: A Dynamic Panel Data Approach," RWI Discussion Papers 0028, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
    14. Badi H. Baltagi & Long Liu, 2020. "Forecasting with unbalanced panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 709-724, August.
    15. Badi Baltagi & Seuck Song, 2006. "Unbalanced panel data: A survey," Statistical Papers, Springer, vol. 47(4), pages 493-523, October.
    16. Artūras Juodis, 2018. "Pseudo Panel Data Models With Cohort Interactive Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(1), pages 47-61, January.

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