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베이지안 추정법을 이용한 주택선택의 다항프로빗 모형 분석
[Analysis of housing choice using multinomial probit model – Bayesian estimation]

Author

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  • Park, Sang Soo
  • Lee, Chung-Ki

Abstract

We employ a multinomial probit model to understand the housing choice of Koreans, more specifically to understand the decision factors that affect Koreans’ housing decisions. The data used are from surveys conducted in July, 2006, and we use the Gibbs sampling technique in analyzing them. Apparently the housing decision is based on three dimensions–types of housing, size of places, and home ownership. A household may make its decision by simultaneously considering these three dimensions given its income and decision factors. Therefore, it would be ideal to categorize the alternatives based on these three dimensions. Due to the limited data, we treated the home ownership decision as if it were given outside the model or, in other words, as if a household made the decision before taking the type and size into account. From the results, we can see the followings: Firstly, the estimate of the coefficient of the alternative specific variables is negative and significant in all equations. Secondly, a household which decides to purchase housing is more likely to buy AS rather than HS. This is understandable considering Koreans’ inclination toward apartments.

Suggested Citation

  • Park, Sang Soo & Lee, Chung-Ki, 2011. "베이지안 추정법을 이용한 주택선택의 다항프로빗 모형 분석 [Analysis of housing choice using multinomial probit model – Bayesian estimation]," MPRA Paper 37150, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:37150
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    References listed on IDEAS

    as
    1. Keane, Michael P, 1994. "A Computationally Practical Simulation Estimator for Panel Data," Econometrica, Econometric Society, vol. 62(1), pages 95-116, January.
    2. McFadden, Daniel L., 1984. "Econometric analysis of qualitative response models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 24, pages 1395-1457, Elsevier.
    3. Geweke, John & Keane, Michael P & Runkle, David, 1994. "Alternative Computational Approaches to Inference in the Multinomial Probit Model," The Review of Economics and Statistics, MIT Press, vol. 76(4), pages 609-632, November.
    4. McCulloch, Robert E. & Polson, Nicholas G. & Rossi, Peter E., 2000. "A Bayesian analysis of the multinomial probit model with fully identified parameters," Journal of Econometrics, Elsevier, vol. 99(1), pages 173-193, November.
    5. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    6. Imai, Kosuke & van Dyk, David A., 2005. "A Bayesian analysis of the multinomial probit model using marginal data augmentation," Journal of Econometrics, Elsevier, vol. 124(2), pages 311-334, February.
    7. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
    8. Planas, Christophe & Rossi, Alessandro & Fiorentini, Gabriele, 2008. "Bayesian Analysis of the Output Gap," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 18-32, January.
    9. Bhat, Chandra R., 1995. "A heteroscedastic extreme value model of intercity travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 471-483, December.
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    More about this item

    Keywords

    Bayesian estimation; Gibbs sampling; housing choice; multinomial probit model;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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