scholarly journals Estimation in a generalization of bivariate probit models with dummy endogenous regressors

2019 ◽  
Vol 34 (6) ◽  
pp. 994-1015
Author(s):  
Sukjin Han ◽  
Sungwon Lee
Keyword(s):  
Bivariate Probit ◽  
Endogenous Regressors ◽  
Probit Models

scholarly journals Endogenous financial constraints and innovation

2020 ◽  
Author(s):  
Henry Lahr ◽  
Andrea Mina
Keyword(s):  
Research And Development ◽  
Financial Constraints ◽  
Bivariate Probit ◽  
Innovation Activities ◽  
Probit Models ◽  
Innovation Surveys ◽  
The Uk

Abstract We investigate which indicators of a firm’s innovation activities are associated with financial constraints and analyze the nature and direction of causal links between innovation and financial constraints. By estimating simultaneous bivariate probit models on data from the UK Innovation Surveys, we show that among innovation inputs, research and development (R&D) activity increases the likelihood that firms face financial constraints. Among innovation outputs, only new-to-market products generate financial constraints. Reverse effects on innovation appear limited to external R&D.

Bayesian Versus Maximum Likelihood Estimation of Treatment Effects in Bivariate Probit Instrumental Variable Models

2018 ◽  
Vol 7 (3) ◽  
pp. 651-659 ◽  
Author(s):  
Florian M. Hollenbach ◽  
Jacob M. Montgomery ◽  
Adriana Crespo-Tenorio
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Instrumental Variable ◽  
Likelihood Estimation ◽  
Direct Calculation ◽  
Causal Effects ◽  
Parameter Estimates ◽  
Bivariate Probit ◽  
Probit Models ◽  
Quantities Of Interest

Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty.

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