tailieunhanh - Lecture Advanced Econometrics (Part II) - Chapter 4: Discrete choice analysis - Multinomial models

Lecture "Advanced Econometrics (Part II) - Chapter 4: Discrete choice analysis - Multinomial models" presentation of content: The multinomial logit model, conditional logit model, mixed logit mode, independence of irrelevant alternatives, nested logit model, multinomial probit model. | Advanced Econometrics - Part II Chapter 4 Discrete choice analysis Multinomial Models Chapter 4 DISCRETE CHOICE ANALYSIS MULTINOMIAL MODELS We look at settings with multiple unordered choices. A key notion here is the independence of irrelevant alternative property Models for discrete choice with more than two choices We assume for the ith consumer faced with i choices j 1 2 . J suppose that the utility of choice j is U1 XjP S1 If the consumer makes choice j in particular then we assume that Uij is the maximum among J alternatives. Prob Uiz. Uk for all k j This is a probability of individual I makes choice j. Y j if Uj Uk for all k j The model is made by a particular choice of distribution for the disturbances. Let Yi be a random variable that indicates the choice made McFadden 1974 has shown that if and only if the J disturbances are independent and identically distributed with type I extreme value distribution F Sj exp - exp -Sj e 11 Then exp XjF _ exp ZyF Pr ob Y 1 J J E exp Xij E exp Zjớ 1 1 1 1 Utility depends on Zij which includes aspects specific to the individual i as well as to choice j . Let Zj Xj wi e p a Xij varies across choices j and possibly across individual i as well . wi contains the characteristics of the individual i therefore the same for all choice. Nam T. Hoang UNE Business School 1 University of New England Advanced Econometrics - Part II Chapter 4 Discrete choice analysis Multinomial Models exp Xjp p ai Prob Y j exp Xjfi wa J E exp XjP wp j 1 J E exp XP . j 1 exp wia exp Xjp E exp X P j 1 For example a model of a shopping centre choices by individual Depends on number of stores Sij distance from the centre of the city Dij and income of the individual i i which varies across individuals but not across the choices. Zij S a I. THE MULTINOMIAL LOGIT MODEL Suppose we have only individual specifre characteristics i w which is the same for all choice. The model response probability as exp w .a Prob Y j w i Pj J X 1 E exp j 1 For all .

• Kinh tế học
• Lecture Advanced Econometrics
• Discrete choice analysis
• Multinomial models
• The multinomial logit model
• Conditional logit model
• Mixed logit mode
• Advanced Econometrics
• Lecture Heteroskedasticity
• Proterties of ols in rpesence
• Teesting for heteroskedasticity
• Treatment for heteroskedasticity
• Simultaneous equations models
• Order conditions for identification
• Estimation of a simultaneous equation system
• Lecture Simultaneous equations models
• Review of least squares
• Likelihood methods
• Least quares methods
• Maximum likelihood estimation
• Limited dependent variable models
• Some issues in specification
• Sample selection model
• Regression analysis of treatment effects
• Greneralized linear regression model
• Properties of ols estimators
• Whites heteroscedascity consistent estimator
• Greneralized least squares estimation
• Properties of ols estimator under autocorrelation
• Disturnabce rprocess
• Estimation under autocorrelation
• Estimator under autocorrelation
• General framework for panel data
• Pooled regression
• Fixed effects
• Random effects model
• Choosing between fixed
• Seemingly unrelated regressions
• Generalized least squares estimation
• Kronecker product
• Two case when sur provides
• Eficiency gain over
• Hypothesis testing
• Lecture Hypothesis testing
• Maximum likelihood estimators
• Likelihood ratio test
• Lagrange multiplier test
• Hausman specification test
• Binary outcome models
• Discrete choice model
• Basic types of discrete values
• The probability models
• Dummy varialable
• Intercept dummies with interactions
• Seasonal efects
• Test for structure break
• Differences in differences
• Models for count data
• Poisson regression model
• Goodness of fit
• Negative binomial regression model
• Too many zeros data
• Generalized method of moments
• Orthogonality condition
• Method of moments
• The advantages of GMM estimator