Panel Data Discrete Choice Models with Lagged Dependent Variables

By Bo E. Honoré, Ekaterini Kyriazidou. 2000

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Subfields: Economics and Econometrics, Marketing

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Citations in the corpus

Generalized random forests (2016, methodology): “In economics, popular applications of these techniques include multinomial choice modeling in a panel data setting (e.g., Honoré and Kyriazidou, 2000) and instrumental variables regression (e.g., Su, Murtazashvili and Ullah, 2013).”
Binary choice panel data models with predetermined variables (2003, background): “In a model with only strictly exogenous variables in addition to lagged y, it leads to estimators with a slower than root-N rate of convergence (HonorÃ e and Kyriazidou, 2000), and little is known about models with other predetermined variables.”
Hyperbolic Transformation and Average Elasticity in the Framework of the Fixed Effects Logit Model (2011, background): “(2)Additionally, Honoré and Kyriazidou (2000) [5] propose an estimator for the fixed effects logit model with the lagged dependent variable (as for details, see also pp.”
Estimating Semi-Parametric Panel Multinomial Choice Models Using Cyclic Monotonicity (2016, methodology): “Honoré and Kyriazidou (2000) used a maximum score-type estimator for a dynamic panel binary choice model.”
Exporting from Manufacturing Firms in Sub-Saharan Africa (2006, background): “Honoré and Kyriazidou (2000) have demonstrated that the parameters of the model are identified (subject to regularity conditions), if the econometrician has access to 4 or more observations per individual.”
Understanding Bias in Nonlinear Panel Models: Some Recent Developments ∗ (2005, methodology): “The discussion of orthogonality in the context of panel data models is due to Lancaster (2000, 2002), together with a Bayesian proposal that we consider below.”
Psychological Pressure in Competitive Environments: Evidence from a Randomized Natural Experiment (2010, background): “Honoré and Kyriazidou (2000) include one lagged dependent variable but require that the remaining explanatory variables should be strictly exogenous, thus excluding the possibility of a lagged dependent regressor.”
Censored Data and Truncated Distributions (2005, background): “Several studies have pursued this, including Kyriazidou (1997), Honore and Kyriazidou (2000) and Lee (2001).”
Identification and Estimation of Average Partial Effects in “Irregular” Correlated Random Coefficient Panel Data Models (2012, background): “γ0  ≡ E ∙  (x ())  ̧ = E ∙μ x ()  ¶0  () ̧ (32)”