TAILIEUCHUNG - Lecture Advanced Econometrics (Part II) - Chapter 9: Autocorrelation

Lecture "Advanced Econometrics (Part II) - Chapter 9: Autocorrelation" presentation of content: Properties of ols estimator under autocorrelation, disturnabce rprocess, estimation under autocorrelation. | Advanced Econometrics Chapter 9 Autocorrelation Chapter 9 AUTOCORRELATION Non-zero correlation between errors at different observations E stst 0 t s violated assumption 4 E sể ơỀI because the off-diagonals 0. Example log Qt P1 2 log Kt Mog Lt St t 1 2 . T In recession Qị more than inputs St 0 In boom QỲ more than inputs St 0 Autocorrelation also called serial correlation can exist in any research study in which the order of the observations has some meaning it occur most frequently in time-series data. Pure serial correlation is caused by the underlying distribution of the error term of the true specification of an equation. Impure serial correlation is caused by a specification error such as an omitted variable or incorrect functional form. We here study about the pure serial correlation. Nam T. Hoang University of New England - Australia 1 University of Economics - HCMC - Vietnam Advanced Econometrics Chapter 9 Autocorrelation I. PROPERTIES OF OLS ESTIMATOR UNDER AUTOCORRELATION à 1. Pols is still unbiased. à 2. Pols is still consistent. 3. Pols is longer best efficient it is less efficient than Pglsvariances. 4. VarCov Pols Ơ2 X X -1 so the standard errors of Pj 5 are biased downward and inconsistent because they are based on incorrect formula. 5. t-statistic R2 overall F-statistics upward. II. DISTURBANCE PROCESS For testing or treatment we need to make more explicit assumption about the type of autocorrelation. The most common is first order autoregressive process AR 1 . St pst -1 ut ut satisfies all classical assumptions. E ut 0 E u ut ƠJ E utu5 0 t s p coefficient of autocorrelation. p stationary of St. Covariance stationary of St the mean variance and all autocovariances of St are constant. Autocovariances Cov st st_ s X Cov st 5 st X a Q t t - 5 Ys Y- 5 So Cov st st-5 X does not depend on t only depend on s. St pst-1 ut p pst-2 ut-1 ut p -2 pip-1 ut Nam T. Hoang University of New England - Australia 2 University of Economics - HCMC - Vietnam Advanced .

• Kinh tế học
• Advanced Econometrics
• Lecture Advanced Econometrics
• Properties of ols estimator under autocorrelation
• Disturnabce rprocess
• Estimation under autocorrelation
• Estimator under autocorrelation
• 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
• Lecture Heteroskedasticity
• Proterties of ols in rpesence
• Teesting for heteroskedasticity
• Treatment for heteroskedasticity
• 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
• Simultaneous equations models
• Order conditions for identification
• Estimation of a simultaneous equation system
• Lecture Simultaneous equations models
• 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
• Financial econometrics
• Advanced modeling techniques
• Regression analysis
• Selected topics
• Regression applications
• Regression applications in finance
• Stepwise regression
• Autoregressive conditional heteroskedastic models
• Vector autoregressive models
• Robust estimation
• Principal components analysis
• Factor analysis
• bank
• stock
• Stochastic Volatility
• Efficient Method of Moments
• econometrics
• Managerial economics
• Multiple regression analysis
• Qualitative information
• Data issues
• Advanced panel data methods
• Pooling cross sections across
• Speculative Prices
• Finite Variance
• Security Prices
• Random Variables
• Nonstationary Variance
• Stochastic Volatilities