TAILIEUCHUNG - Lecture Advanced Econometrics (Part II) - Chapter 3: Discrete choice analysis - Binary outcome models

Lecture "Advanced Econometrics (Part II) - Chapter 3: Discrete choice analysis - Binary outcome models" presentation of content: Discrete choice model, basic types of discrete values, the probability models, estimation and inference in binary choice model, binary choice models for panel data. | Advanced Econometrics - Part II Chapter 3 Discrete choice analysis Binary outcome models Chapter 3 DISCRETE CHOICE ANALYSIS BINARY OUTCOME MODELS I. INTRODUCTION The simplest of the model in which the dependent variable is discrete value is the model with y is binary. 1. Discrete choice model Model in which the dependent variable assumes discrete values Example -J1 y 5 1 0 if person i is employed in labor force otherwise Regardless the definition of y it is traditional to refer to y 1 as a success and y 0 as a failure . 2. Basic types of discrete values a Dichotomous or binary These take on a value of one or zero depending on which of two possible results occur b Polychotomons variables These take on a discrete number greater than two of possible values non-categoring y number of patent issued to a company during a year c Unordered Variables These are variables for which there is no natural ranking of the alternatives. Example for a sample of commuters we define a variable Nam T. Hoang UNE Business School 1 University of New England Advanced Econometrics - Part II Chapter 3 Discrete choice analysis Binary outcome models 1 2 y 3 4 c 1 2 y 3 4 c if person i is a lawyer if person i is a teacher if person i is a doctor if person i is a plumer if person i drives to work if person i takes a bus if person i takes a train if otherwise We can define these dependent variables in any order desired unordered categorical variables. d Ordered variables With these variables outcome have a natural ranking. Examples 1 if person i is in poor health yt I 2 if person i is in good health 3 if person i is in excellent health 1 if person i spent less than 1 000 _ 2 y I 3 if person i spent 1 000 - 2 000 if person i spent 2 000 - 4000 4 if person i spent more than 4 000 A special case of an ordered variable is a sequential variables . This occurs when second event is dependent on the first event the third event is dependent on the previous two event . 1 if person i has not completed high .

• Kinh tế học
• Lecture Advanced Econometrics
• Discrete choice analysis
• Binary outcome models
• Discrete choice model
• Basic types of discrete values
• The probability models
• 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
• Multinomial models
• The multinomial logit model
• Conditional logit model
• Mixed logit mode
• 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