Comparison of SML and GMM estimators for the random coefficient logit model using aggregate data

Business & Economics

• Logit Model 100%
• Simulated Maximum Likelihood 88%
• Generalized Method of Moments Estimator 73%
• Random Coefficients 73%
• Aggregate Data 66%
• Maximum Likelihood Estimator 54%
• Estimator 45%
• Parameter Heterogeneity 34%
• Moment Conditions 30%
• Market Share 17%
• Parameter Identification 17%
• Gradient 12%
• Marketing Research 11%
• Product Market 10%
• Analysts 9%
• United States of America 4%

Mathematics

• Simulated Maximum Likelihood 94%
• Generalized Method of Moments 80%
• Random Coefficient Models 80%
• Logit Model 76%
• Moment Estimator 71%
• Maximum Likelihood Estimator 39%
• Market 34%
• Estimator 33%
• Moment Conditions 26%
• Marketing 14%
• Parameter Identification 14%
• Strictly positive 12%
• Likelihood Function 11%
• Recovery 11%
• Gradient 8%
• Observation 7%
• Zero 5%
• Model 3%

Social Sciences

• aggregate data 74%
• market share 28%
• marketing 9%
• market 6%