Identification of multiple equation probit models with endogenous dummy regressors

By Joachim Wilde. 2000

579 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d9f6860f05485834a0780e1ba3c3b2c7c7853d45

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Citations in the corpus

Return migrants: The rise of new entrepreneurs in rural China (2010, unknown): “Although no exclusion restrictions are theoretically needed to achieve identification of the model parameters (Wilde, 2000), Monfardini and Radice (2008) advocate the use of instruments to help in obtaining results more robust to distributional misspecification.”
Impact of Interventions on Discrete Outcomes: Maximum Likelihood Estimation of the Binary Choice Models with Binary Endogenous Regressors (2011, result): “These findings are consistent with conclusions of Wilde (2000) that “in recursive multiple-equation probit models with endogenous dummy regressors no exclusion restrictions for the exogenous variables are needed if there is sufficient variation in the data”.”
Grandparenting and mothers√≠ labour force participation: A comparative analysis using the Generations and Gender Survey (2012, unknown): “To improve identification (Wilde 2000) we use two instruments for the probability that the mother received help in caring for her children: Z1 (a dummy variable taking value 1 if the woman’s mother is alive at the time of the interview and 0 otherwise) and Z2 (number of siblings the woman has).”
The Impact of Financial Constraints on Innovation: What Can Be Learned from a Direct Measure? (2008, background): “No additional restrictions on the parameters are needed to achieve the identification of this bivariate probit model with endogenous dummy regressor (Monfardini and Radice 2004 , Wilde 2000 ).”
The bivariate probit model, maximum likelihood estimation, pseudo true parameters and partial identification (2019, methodology): “Hence we can conclude that arguments that parallel those employed in Heckman (1978) and Wilde (2000) can be carried over to the RBVS-P and RBVL-P models to show that the RBVP, RBVS-P and RBVL-P models will all be identified in the absence of exclusion constraints if the matrix of regressors has full”
Poverty–Food Security Nexus: Evidence from a Survey of Urban Slum Dwellers in Kolkata (2014, methodology): “…in estimation and an estimate of are achieved by full information maximum likelihood estimation (Greene, 2012). ii) Identification The model was estimated by imposing an exclusion restriction even though it is not strictly necessary, as “identification by functional form” (Wilde, 2000) is possible.”
Entrepreneurial exit and entrepreneurial engagement (2011, unknown): “Without identifying restrictions, the model parameters are theoretically identified (Wilde 2000) and can be estimated with full information maximum likelihood (Greene 1998; 2008, p 823).”
Does Fertility Behavior Spread among Friends? (2014, unknown): “(Wilde, 2000).”