syllabus advanced econ ueh

7/22/2019 Syllabus Advanced Econ UEH

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Ni dung chng trnh

Kinh TLng Nng Cao

Kinh tlng (Econometrics) l cng cchyu ca cc nh nghin cu trong cc chuynngnh kinh tv ti chnh (Economics and Finance). Khi tin hnh cc nghin cu hn lm(academic research) vi sliu kinh tv ti chnh, vic xy dng, c lng v kim nhcc m hnh sdng cc cng ckinh t lng l hng nghin cu chyu (mainstreamresearch) c tha nhn rng ri trn thgii.

Kha hc kinh tlng nng cao nhm trang bcho hc vin nhng kin thc cp caonht trong kinh tlng vmt l thuyt cng nh thc hnh. Cc m hnh kinh tlng sc gii thiu chi tit nn tng l thuyt, cc tnh cht ca cc c lng sc kho stv mt l thuyt v minh ha bng cc chng trnh m phng (simulation) vit trnMATLAB. Cc bi thc hnh vi sliu ssdng STATA hoc EVIEWS. Cc phng

php v pht trin mi nht trong kinh tlng cng sc cp v tho lun.

tham gia tt kha hc, hc vin c yu cu c kin thc vng chc vxc sut thng kv i stuyn tnh trnh i hc, c bit l cc php tnh ton trn ma trn. Hc vincng c cho l hc qua cc mn kinh t lng trnh nhp mn hoc trung cp(introductory or intermediate level). Ti liu n tp kin thc xc sut thng k v i sma

trn sc pht cho hc vin vo u kha hc.

Bi ging (lecture notes) ca kha hc sc cung cp cho hc vin. Kha hc sdngting Vit, nhng cc ti liu cung cp cho hc vin sbng ting Anh.

Ti liu tham kho chnh:

William H. Greene, Econometric Analysis, 7thedition, 2012

(Ghi ch: vic mua ti liu tham kho l khng bt buc v bi ging c son chi tit)

Kha hc sbao gm 3 hc phn:

1. Kinh tlng: cc vn cn bn2. La chn ri rc v kinh tlng vi m (micro-econometrics)3. Chui thi gian v kinh tlng v m (macro-econometrics)

Hc phn 1 l yu cu tin quyt (pre-requisite) cho hc phn 2 hoc hc phn 3. iu ny cngha l, sau phn 1, ty theo dnh nghin cu ca c nhn, hc vin c thla chn phn2 hoc phn 3 (hoc chai) hc tip.

tham gia cc hc phn, hc vin c trng i l trang bmy tnh vi cc phn mm

EVIEWS hoc STATA. Vic c khnng vit cc chng trnh m phng trn MATLABc khuyn khch, tuy rng iu ny l khng bt buc.


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nh gicui mi hc phn:

Kha hc mrng v tnguyn cho cc i tng trong thng bo. Cc bi tp l thuyt v

ng dng sc giao cho hc vin, hc vin slm cc bi tp trn tinh thn tgic.

Cui mi hc phn, hc vin c yu cu chn mt ti ng dng vi sliu thc tin, vtin hnh vit mt bi nghin cu ng dng. Ti a 2 hc vin c thkt hp vit mt binghin cu. Cc ti v cch khai thc cc ngun sliu trn internet sc tho lun trnlp. Hn np bi l 8 tun sau khi kt thc hc phn.

Hc vin chc coi l hon thnh hc phn sau khi trnh by bi nghin cu ng dng

ca mnh cho ging vin ca kha hc.


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University of Economics - Ho Chi Minh City

2012

ADVANCED ECONOMETRIC COURSE (PhD level)

PART I

BASIC ECONOMETRIC MODELS

Chapter 1: LINEAR REGRESSION MODEL

I. ModelII. Assumptions of the Classical Regression ModelIII. Ordinary Least Squares Estimation (OLS)IV. Algebraic Properties of Least SquaresV. Partitioned RegressionVI. Goodness of Fit

Chapter 2: FINITE SAMPLE PROPERTIES OF LEAST SQUARES

ESTIMATORS

I. UnbiasedII. LinearityIII. VarCov( ), estimation of 2 IV. EfficientV. Gauss - Markov TheoremVI. Review of Stochastic Inference

1. Normally distributed vector2. Chi-squares distribution3. Eigenvalue - Eigenvector problem4. F and t-distribution

VII. Testing Hypothesis on Individual Coefficients


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VIII. Family of F-test

Chapter 3: STOCHASTIC REGRESSION MODEL

I. Asymptotic Properties of the Least Squares Estimators1. Consistency2. Cramer theorem3. Slutsky Theorem

II. Stochastic Regression1. Stationary random variable2. Unbiasedness of 3. VarCov ( )4. Consistency of

III. Limiting Distributions and Asymptotic Distributions1. Definition2. Central limit theorem3. Proposition

IV. Asymptotic Distribution of Chapter 4: INSTRUMENTAL VARIABLES ESTIMATION

I. Endogeneity Problem1. Errors in measurement of independent variables2. Jointly determined variables3. Omitted variables4. Lagged dependent variables

II. Estimation by Instrumental VariableIII. Two-stage Least Squares EstimationIV. Asymptotic Distribution of IVV. Hausmans Specification Test and an Application to IV estimation

1. Theorem2. Hausmans test


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3. Wus approachVI. Choosing Instruments

Chapter 5: INFERENCE AND PREDICTION

I. Nested and Non-nested ModelsII. Wald test

1. The asymptotic 2distribution and exact F-distribution2. The restricted least squares estimatiors

Chapter 6: DUMMY VARIABLES

I. Intercept DummyII. Intercept Dummy with InteractionsIII. Seasonal EffectsIV. Differences in DifferencesV. Test for Structural Break

Chapter 7: GENERALIZED LINEAR REGRESSION

I. ModelII. Properties of OLS EstimatorsIII. Whites Heteroskedasticity Consistent Estimator for VarCov( )IV. Generalized Least Squares EstimationV. Feasible GLS Estimation

Chapter 8: HETEROSKEDASTICTY

I. Properties of OLS Estimator under HeteroskedasticityII. Testing for HeteroskedasticityIII. Treatment for Heteroskedasticity


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Chapter 9: AUTOCORRELATION

I. Properties of OLS Estimator under AutocorrelationII. Disturbance ProcessesIII. Treatment for AutocorrelationIV. Testing for AutocorrelationV. Neway-West Procedure for Consistent Estimation of VarCov()

Chapter 10: MODELS FOR PANEL DATA

I. General FrameworkII. Panel RegressionIII. Fixed-Effects Models

1. Least Squares Dummy Variables (LSDV) model2. Within-estimator and Between-estimator3. Fixed time and Group effects4. Unbalanced panel5. Testing for group effect6. Shortcoming of fixed-effect model7. Autocorrelation and Heteroskedasticity in fixed-effect model

IV. Random Effects ModelV. Choosing between Fixed-Effects and Random Effects ModelVI. Finding the big VarCov( )

Chapter 11: SEEMINGLY UNRELATED REGRESSION (SUR)

I. ModelII. Kronecker ProductIII. Generalized Least Squares (GLS)Estimation for SURIV. Feasible GLS Estimation for SUR modelV. Cases in which there is no efficiency gain of SUR over OLSVI. Hypothesis Testing and Autocorrelation in SUR


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Chapter 12: SIMULTANEOUS EQUATION MODELS

I. Model1. General framework2. Reduced form

II. Rank and Order Conditions for IdentificationIII. Estimation of a Simultaneous Equation Model

1. OLS estimation2. Two-stage least squares (2-SLS)method3. Computational formula for 2-SLS4. Three-stage least squares estimation

Chapter 13: GENERALIZED METHOD OF MOMENTS (GMM)

ESTIMATION

I. The Basic for The GMM estimation.1. Estimation based on orthogonality condition2.

Generalizing the method of moments

3. Properties of the GMM estimatorII. GMM and Conventional Estimators

1. GMM and IV, 2-SLS2. Why use GMM?

III. GMM Estimation of Econometric Models1. Single-equation linear models2. Seemingly unrelated regression models3. Simultaneous equations models with heteroskedasticity


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PART II

DISCRETE CHOICE AND MICRO-ECONOMETRICS

Chapter 1: DISCRETE CHOICE ANALYSIS: BINARY OUTCOMES

I. Models for Binary OutcomesII. Estimation and Inference in Binary Choice Models

1. Marginal effects and average partial effects2. Measuring goodness of fit3. Hypothesis Tests4. Specification Analysis

III. Binary Choice Models for Panel DataIV. Multivariate Probit Model

Chapter 2: DISCRETE CHOICE ANALYSIS: MULTINOMIAL AND

EVEN COUNTS MODELS

I. Models for Unordered Multiple Choices1. The multinomial logit model2. The conditional logit model3. The independent from irrelevant alternatives (IIA) assumption4. Nested logit models5. The mixed logit model

II. Random Utility Models for Ordered ChoicesIII. Models for Counts of Events

1. The Poisson regression model2. Measuring goodness of fit3. Testing for overdispersion4. Negative binomial regression model5. Two-part models: zero inflation and hurdle models


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Chapter 3: LIMITED DEPENDENT VARIABLES

I. Truncation1. Truncated distribution2. The truncated regression model3. The stochastic frontier model

II. Censored Data1. The censored normal distribution2. The Tobit model3. Heteroskedasticity and non-normality

III. Sample Selection Models1. Incidental truncation in a bivariate distribution2. Regression in a model of selection3. Two-step and maximum likelihood estimation4. Panel data applications of sample selection models

IV. Evaluating Treatments Effects1. Regression analysis of treatment effects2. Propensity score matching3. Regression discontinuity


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PART III

TIME SERIES AND MACRO-ECONOMETRICS

Chapter 1: STATIONARY TIME-SERIES MODEL

I. Stochastic Difference Equation ModelII. ARMA ModelIII. StationaryIV. Properties of Forecasts

Chapter 2: MODELING VOLATILITY

I. ARCH ProcessesII. A GARCH Model of RiskIII. The ARCH-M ModelIV. Properties of GARCH ProcessesV. Maximum-Likelihood Estimation of GARCH ModelVI. Multivariate GARCH

Chapter 4: VECTOR AUTOREGRESSION MODEL (VAR)

I. Introduction to VAR analysisII. Estimation and IdentificationIII. The Impulse Response Function (IRF)IV. Testing HypothesesV. Structural VARs

Chapter 5: NON-STATIONARY DATA

I. Non-stationary Processes and Unit Roots1. Integrated processes and differencing2. Random walks and trend


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3. Spurious regressions4. Testing for unit roots

II. Cointegration1. Common trends between non-stationary variables2. Testing for cointegration3. Estimating cointegrating vector

III. Vector Error-Correction Model1. The Engle-Granger methodology2. Trace and maximum eigenvalue tests for cointegration3. The Johansen methodology

IV. Non-stationary Panel Data1. Testing for unit roots and cointegration in non-stationary panel2. Models estimation using non-stationary panel data

syllabus advanced econ ueh

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