TAILIEUCHUNG - Lecture Advanced Econometrics (Part II) - Chapter 10: Models for panel data

Lecture "Advanced Econometrics (Part II) - Chapter 10: Models for panel data" presentation of content: General framework for panel data, pooled regression, fixed effects, random effects model, choosing between fixed and random effects models, finding big. | Advanced Econometrics Chapter 10 Models for panel data Chapter 10 MODELS FOR PANEL DATA I. GENERAL FRAMEWORK FOR PANEL DATA Panel data longitudinal data same entities are observed overtime. The basic framework for panel data is a regression model of the form k 1 Ytt 2 f Y- za sit j 1 1xl l x1 XitP za e t 1xk k x1 1xl l x1 Where zi 1 zt 2 . za a a a a X Xt1 X 2 X k There are k regressors in X t not including a constant term. The heterogeneity or individual effect is za where z contains a constant term and a set of individual or group specific variable which may be observed such as race sex location. or unobserved such as family specific characteristics individual heterogeneity in skill or preferences . All of which are taken to be constant over time t. Therefore z 1 z 2 . za constant over time t. If z ị is observed for all cross-sections individuals then the entire model can be treated as an ordinary linear model and fit by least squares. When z ị is not observed most of the cases complications arise that leads to main objective of the analysis will be consistent and efficient estimation of the partial effects. Nam T. Hoang University of New England - Australia 1 University of Economics - HCMC - Vietnam Advanced Econometrics Chapter 10 Models for panel data s YtlXt X Assumption of strict exogeneity E sit X Xi2 . XiT 0 That is the current disturbance is uncorrelated with the independent variable in every period of t. Assumption of mean independence E zi a X1 Xi2 --- XiT a 1 1 f 1x1 Or fixed effect h Xị at II. POOLED REGRESSION If zt 1 zt2 . za 1 which contains only a constant term. Then OLS provides consistent and efficient estimates of the intercept a and the slope vector p common effect model . Thus equation 1 Yit Xitp za Sit 1xk k x1 1xl l x1 Where Xừ it obs of all k explanatory variables TY1 Y2 yn. _ T x1 _ 1 NT x1 1 ts H- 1 L k x1 L 1 i a 1x1 S1 rx S2 _XN _ _ X SN _ T x1 _ nT x1 NT x1 V nT x1 1 1 Tix1 1st country Y1 X1P ia S1 1 E sit 0 with classical assumptions

• Kinh tế học
• Lecture Advanced Econometrics
• Advanced Econometrics
• General framework for panel data
• Pooled regression
• Fixed effects
• Random effects model
• Choosing between fixed
• 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
• 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
• Discrete choice analysis
• Binary outcome models
• Discrete choice model
• Basic types of discrete values
• The probability models
• 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