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(ECONOMETRICS
IV)
Period 1, 2000/01
Instructor:
Stanislav
Anatolyev
Monday and Wednesday,
10:00 - 11:30
The
course links basic econometric knowledge to serious thinking of econometric
model building and contemporary methods of estimation. The emphasis
will be put on conceptual content rather than mathematical sophistication,
although the latter is sometimes unavoidable. The assigned exercises
will include regular problems as well as computer tasks. The home assignments
will serve as an important ingredient of the learning process. Theoretical
and empirical examples will be abundant throughout.
ORGANIZATION
There will
be six weekly homework assignments that account for 30% of the final
grade. The assignments can be submitted one for a group of 2 or 3 students.
The groups should be determined at the beginning and should not change
during the period. The final exam, which will account for 70% of the
grade, will be open-book, open-notes.
LITERATURE
· Occasional
lecture notes
· Goldberger,
A. A Course in Econometrics, Harvard University Press (AG)
· Greene,
W. Econometric Analysis, 3rd edition (WG)
· Hamilton,
J. Time Series Analysis, Princeton University Press (TS)
· Potcher, B.,
Prucha, I. (1999) Basic elements of asymptotic theory, University
of Maryland – College Park. Can be found at http://www.bsos.umd.edu/econ/papers/prucha1.pdf
(PP)
· Horowitz,
J. (1999) The bootstrap, forthcoming in Handbook of Econometrics,
vol. 5. Can be found at http://www.biz.uiowa.edu/faculty/horowitz/papers/Bootstr.pdf
(JH)
· Hall, A.
(1993) Some aspects of Generalized Method of Moments estimation,
in G. S. Maddala, C. R. Rao and H. D. Vinod, eds., Handbook of
Statistics, vol. 11, Ch. 15. Elsevier Science (AH)
· Ogaki, M.
(1993) Generalized Method of Moments: econometric applications,
in G. S. Maddala, C. R. Rao and H. D. Vinod, eds., Handbook of
Statistics, vol. 11, Ch. 17. Elsevier Science (MO)
SYLLABUS
I. Approximate
Inference (2.5 weeks)
1.
Three approaches to inference (PP 1; AG 8; WG 6.6)
-
Three approaches
to inference: exact, asymptotic, bootstrap.
-
Problems
with exact inference.
2.
Asymptotic approach: independent data (PP 2, 3.1, 4.1; AG 9-10; WG
4.4)
- Modes of convergence
of sequences of random variables. Rates of convergence.
- Laws of Large Numbers.
Central Limit Theorems.
- Continuous mapping theorems.
Delta-method.
- Asymptotic confidence
intervals and large sample hypothesis testing.
3.
Asymptotic approach: time series data (PP 3.2, 4.2; TS 7)
- Measures of dependence.
Stationarity and ergodicity. Martingale difference sequence.
- Ergodic Theorem. Central
Limit Theorem for martingale difference sequences.
- Robust inference. Heteroskedasticity
and autocorrelation consistent estimators.
- Introduction to asymptotic
inference in models with nonstationary data.
4.
Bootstrap approach (JH 1-3)
- Data and empirical distribution
function. Approximation by bootstrapping and approximation by
simulation.
- Nonparametric bootstrap
in a linear mean regression model. The residual bootstrap. Parametric
and not fully nonparametric bootstrap.
- Bootstrap bias correction.
Bootstrap confidence interval and hypothesis testing.
- Why does the bootstrap
work? Asymptotic expansions.
- The bootstrap with time
series data: bootstrapping innovations, fixed and moving blocks
bootstrap, the stationary bootstrap.
II. Linear
Models: Identification, Estimation and Inference (1.5 weeks)
1. Main concepts
(AG 11; NM)
- Identification and estimation.
Analogy principle. Notion of regression.
- Parametrics, semiparametrics,
seminonparametrics and nonparametrics.
2.
Estimation of a linear mean regression (WG 6.7, 11.2-4, 12.2-5; TS
8)
- OLS and GLS estimators
in linear mean regression models.
- Asymptotic inference
in linear mean regression models.
- Bootstrapping OLS and
GLS estimators.
3.
Instrumental variables in a linear model (WG 9.5)
- Endogeneity and simultaneity.
Instrumental variables.
- IV and 2SLS estimators
and their asymptotic distributions.
- Bootstrapping IV and
2SLS estimators.
- Nonlinear Models: Identification,
Estimation and Inference (3 weeks)
1. Estimation
of a nonlinear mean regression (AG 13, 29; WG 10; NM)
- Nonlinear Least Squares
(NLLS) estimator.
- Consistency and asymptotic
normality of NLLS estimators.
- Nonlinear optimization:
the concentration method.
2. The Maximum
Likelihood (ML) estimator (AG 12; WG 4.5, 4.9, 5.5; TS 5; NM)
- Likelihood function
and likelihood principle. ML estimation.
- Consistency and asymptotic
normality of ML estimators. Asymptotic efficiency.
- Applications: limited
dependent variables models.
- Conditional, joint
and marginal ML.
- ML asymptotic tests:
Wald, Likelihood Ratio, Lagrange Multiplier.
3.
The Classical Method of Moments (CMM) estimator (WG 4.7)
- Population moments and
sample moments. The analogy principle.
- Just identifying moment
restrictions and CMM.
- Asymptotic properties
of CMM estimators.
4. Generalized
Method of Moments (GMM) estimator (AH; MO; WG 11.5-6; TS 14; NM)
- Overidentifying moment
restrictions. The GMM optimization problem.
- Asymptotic properties
of GMM estimators.
- Efficient GMM and feasible
efficient GMM.
- GMM asymptotic tests:
Wald, Distance Difference, Lagrange Multiplier.
- The test of overidentifying
restrictions (the J-test).
- GMM as optimal instrumental
variables estimation.
- OLS, IV, 2SLS, 3SLS and
ML as special cases of GMM estimation.
- GMM and time series data.
GMM and Rational Expectations models.
5. Further GMM topics
(AH; MO; TS 14)
- Finite sample deficiencies
of GMM estimators.
- Modifications of GMM.
- Bootstrapping GMM.
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Contract
Theory
Corruption
Development
Economics*
Econometrics-1
Econometrics-2
Econometrics-3
Econometrics-4
(obligotary)
Economic
Statistics
Economics
of Transition
(elective)
Elements
of the Economics
of Transition*
English
Financial
Economics
Game
Theory
Growth
Theory*
Health
Economics*
History
of Economic
Thought (obligotary)
International
Finance*
Industrial
Organization-1*
Industrial
Organization-2*
Institutions
International
Trade*
Labor
Economics*
Macroeconomics-1
Macroeconomics-2
Macroeconomics-3
Macroeconomics-4
Macroeconomics-5
Macroeconomics-6
(obligotary)
Mathematical
Statistics
Mathematics
for Economists
Microeconomics-1
Microeconomics-2
Microeconomics-3
Microeconomics-4
Microeconomics-5
Microeconomics-6
(obligotary)
Natural
Resources
Non-Cooperative
Games
Open
Macroeconomics*
Political
Economy
Probability
Theory
Public
Economics-1*
Public
Economics-2*
Public
Finance*
Research
Seminar
Russia
in global environment:
past and present (rus)
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