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APPLIED
ECONOMETRICS
Course Webpage: http://www.nes.ru/~sanatoly/AE/AE.htm
2nd Module, 2002/2003
Instructor: Stanislav Anatolyev
The course is devoted to the modern
applied time series analysis. First we will study popular models
of the conditional mean dynamics such as linear ARs and VARs
as well as nonlinear structures like bilinear and threshold
models, chaos and the like. We will also review such issues
as stationarity vs. integratedness, unit roots and cointegration.
Then we will turn to modeling of the conditional variance as
represented by the class of ARCH models. Finally, we will study
some special methods like bootstrapping, forecasting, analysis
of structural breaks, and some others as time permits. We focus
on methods, on the one hand, and applications, on the other.
ORGANIZATION
The course presumes the use of publications
in applied time series and computer work. The home assignment
(50% of the grade) will be an empirical analysis of a time series
of own choice. The exam (50% of the grade) will contain questions
on a published applied time series paper handed out in advance.
RECOMMENDED
TEXTS
Links to important papers will be posted at the course
Webpage
·
Hamilton, J. Time Series Analysis, Princeton University
Press
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Enders, W. Applied Econometric
Time Series, John Wiley
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Mills, T. The Econometric Modeling of Financial Time
Series, Cambridge University Press
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Harvey, A. Time Series Models, MIT Press
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Maddala, G., I.-M. Kim. Unit
Roots, Cointegration and Structural Change, Cambridge University
Press
SYLLABUS
I. Univariate time series: modeling the mean
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Model selection: diagnostic
testing, information criteria and prediction criteria.
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Stationary AR models: properties,
estimation, inference, forecasting.
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Stochastic and deterministic
trends, unit root testing.
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Nonlinear time series modeling
of the mean: threshold autoregressions.
II. Multivariate
time series: modeling the mean
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Stationary VAR models: properties,
estimation, analysis and forecasting.
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VAR models with elements of
nonlinearity.
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Spurious regression and cointegration.
III.
Modeling the variance
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The class of ARCH models: properties,
estimation, inference and forecasting.
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Extensions: IGARCH, ARCH-t,
ARCD, multivariate GARCH.
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Time-varying risk and ARCH-in-mean.
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Stochastic volatility models.
IV.
Other topics in applied time series analysis
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Modeling seasonality in time
series.
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Analysis of structural breaks.
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Markov switching models.
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Bootstrap in time series models.
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Forecast evaluation and comparison.
Combination of forecasts.
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