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Estimating Interdependence Across Space, Time and Outcomes in Binary Choice Models Using Pseudo Maximum Likelihood Estimators


  • Wucherpfennig, Julian
  • Kachi, Aya

    () (University of Basel)

  • Bormann, Nils-Christian
  • Hunziker, Philipp


Binary outcome models are frequently used in Political Science. However, such models have proven particularly dicult in dealing with interdependent data structures, including spatial autocorrelation, temporal autocorrelation, as well as simultaneity arising from endogenous binary regressors. In each of these cases, the primary source of the estimation challenge is the fact that jointly determined error terms in the reduced-form specication are analytically intractable due to a high-dimensional integral. To deal with this problem, simulation approaches have been proposed, but these are computationally intensive and impractical for datasets with thousands of observations. As a way forward, in this paper we demonstrate how to reduce the computational burder signicantly by (i) introducing analytically tractable pseudo maximum likelihoodestimators for latent binary choice models that exhibit interdependence across space, time and/or outcomes, and by (ii) proposing an implementation strategy that increases computational eciency considerably. Monte-Carlo experiments demonstrate that our estimators perform similarly to existing alternatives in terms of error, but require only a fraction of the computational cost.

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  • Wucherpfennig, Julian & Kachi, Aya & Bormann, Nils-Christian & Hunziker, Philipp, 2018. "Estimating Interdependence Across Space, Time and Outcomes in Binary Choice Models Using Pseudo Maximum Likelihood Estimators," Working papers 2018/11, Faculty of Business and Economics - University of Basel.
  • Handle: RePEc:bsl:wpaper:2018/11

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    References listed on IDEAS

    1. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    2. Luc Anselin, 2003. "Spatial Externalities, Spatial Multipliers, And Spatial Econometrics," International Regional Science Review, , vol. 26(2), pages 153-166, April.
    3. Simmons, Beth A. & Elkins, Zachary, 2004. "The Globalization of Liberalization: Policy Diffusion in the International Political Economy," American Political Science Review, Cambridge University Press, vol. 98(1), pages 171-189, February.
    4. Anping Chen & Marlon Boarnet & Mark Partridge & Raffaella Calabrese & Johan A. Elkink, 2014. "Estimators Of Binary Spatial Autoregressive Models: A Monte Carlo Study," Journal of Regional Science, Wiley Blackwell, vol. 54(4), pages 664-687, September.
    5. Von Stein, Jana, 2005. "Do Treaties Constrain or Screen? Selection Bias and Treaty Compliance," American Political Science Review, Cambridge University Press, vol. 99(4), pages 611-622, November.
    6. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    7. Harry H. Kelejian & Ingmar R. Prucha, 2008. "Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," CESifo Working Paper Series 2448, CESifo Group Munich.
    8. Franzese, Robert J. & Hays, Jude C. & Cook, Scott J., 2016. "Spatial- and Spatiotemporal-Autoregressive Probit Models of Interdependent Binary Outcomes," Political Science Research and Methods, Cambridge University Press, vol. 4(1), pages 151-173, January.
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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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