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Probit model for panel data with heterogeneity and endogenous explanatory variables
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Probit model for panel data with heterogeneity and endogenous explanatory variables : ウィキペディア英語版
Probit model for panel data with heterogeneity and endogenous explanatory variables
In many cases, there is an unobservable heterogeneity in the probit model. For instance, when modelling the consumption choice of a certain brand, consumers’ personal preference is unobserved but needs to be considered in the model 〔 Pradeep K. Chintagunta, Dipak C. Jain and Naufel J. Vilcassim (1991), “Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data”, Journal of Marketing Research, Vol. 28, pp. 417-428.〕 . Owing to omitted variable or measurement error, endogeneity issue also could arise〔 Wooldridge, J. (2002): Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, Mass, pp 473.〕 . A probit model including both of these two issues can be represented as:
y_it = 1 (y_it^
* > 0 )

y_it ^
* = x_^ \beta + z_ \delta + c_i + u_
z_it = x_^ \gamma_1 + x_^ \gamma_2+v_
where c_i is the unobservable heterogeneity effect and u_ \mid x_i ~ N (0, 1), v_ | x_i ~ N ( 0, \sigma^) . If v_ and u_ are independent, this model will degenerate to a probit model with unobservable heterogeneity. In this case, we can just integrate P (y_,\ldots,y_ \mid x_i,c_i) against the density of c_i conditional on x_i , then P(y_,\ldots,y_ |x_i) can be obtained 〔 Wooldridge, J. (2002): Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, Mass, pp 494.〕 and the objective for the conditional Maximum Likelihood Estimation is
\sum_^N \log (|x_i) )
If v_ and u_ are correlated, under the normality assumption, it can be assumed that v_ = \rho u_ + \epsilon_ 〔 For more details, refer to Whitney K. Newey (1987), “Efficient Estimation of Limited Dependent Variable Models with Endogenous Explanatory Variables”, Journal of Econometrics 36, pp. 231-250.〕 , where \epsilon_ \sim _ N (0, \sigma^2 - \rho^2) and \epsilon_i is independent with v_i and u_i . Then the model can be rewritten as:
y_ = 1 ((\beta + \delta\gamma_1 ) + x_^ \delta\gamma_2 + c_i+ \omega_ > 0 )
where \omega_ = (1+\rho\delta) u_ + \delta\epsilon_, \omega_ /sim N (0, (1+\rho\delta)^2 + \delta^2 (\sigma^2 - \rho^2 )) and corr(\omega_ , \omega _ ) = \frac )} .
Based on this, following the same Maximum Likelihood Estimation procedure and the scaled parameter (\beta + \delta\gamma_1 , \delta\gamma_2) / \sqrt can be consistently estimated, then the APE 〔 Wooldridge, J. (2002): Econometric Analysis of Cross Section and Panel Data, MIT Press, Cambridge, Mass, pp 22.〕 can be consistently estimated correspondingly.



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