EERC Methodological Seminars Series
ECONOMETRIC SEQUENCE
Time Series Analysis
Instructor: Stanislav Anatolyev,
New Economic School,
This seminar is the second part of the econometric sequence. It is devoted to time series analysis as seen by an applied researcher. We will see how estimation of structural time series models differs from that in cross sectional analysis. Then we will turn to nonstructural modeling. First we will study popular models of the conditional mean dynamics, such as linear ARs and VARs as well as nonlinear structures like threshold models, Markov switching models and the like. We will also review such issues as stationarity vs. nonstationarity, unit roots and seasonal adjustment. Then we will turn to modeling of the conditional variance as represented by the class of ARCH models. Theoretical and empirical examples will be abundant throughout. Beside a chapter in Greene's book, several theoretical surveys and applied papers will be assigned as readings.
ORGANIZATION
Along with lectures, there will be separate computer sessions. The statistical working tool is Econometric Views. The students are encouraged to work in groups. The instructor will be available during office hours.
LITERATURE
Greene, W. (2000) Econometric Analysis, 4th edition, chapter 18
Mishkin, F. (1990) What Does the Term Structure Tell Us
About Future Inflation? Journal of
Monetary Economics 25, 77-95.
Potter, S. (1999) Nonlinear Time Series Modelling: An Introduction. Manuscript,
Federal Reserve Bank of
Evans, G. (1989) Output and
Unemployment Dynamics in the
Diebold, F. and Lopez, J. (1995) "Modeling Volatility
Dynamics," in K. Hoover (ed.), Macroeconometrics:
Developments, Tensions, and Prospects,
Hsieh, D. (1989) Modeling Heteroskedasticity in Daily Foreigh-Exchange Rates. Journal of Business and Economic Statistics 7, 307-317.
SYLLABUS
Stationarity and ergodicity, mean reversion, loss of memory
Asymptotics and bootstrap in time series
Serial correlation, conditional heteroskedasticity and robust inference
Structural Rational Expectations models
Stationary and nonstationary variables: totally different behavior
Trends versus random walks: is GNP trend deterministic or stochastic?
Seasonal adjustment
Autoregressive linear processes: estimation, testing and model selection
Prediction and prediction errors
Testing for unit roots: the augmented Dickey-Fuller test
Nonstationary univariate time series: stochastic and deterministic trends
Nonlinear time series modeling of the mean: thresholds, structural breaks, chaos
Vector autoregressions: simultaneous determination of money and prices
Identification of VAR: structural vs. reduced forms, the simultaneity problem and identifying restrictions
Estimation of VAR: single equation OLS estimators
Granger causality: does printing money cause GNP growth?
Impulse response functions: which shocks are more long-lived?
Variance decomposition: which shocks are more important?
Nonstationary multivariate time series: spurious regression, cointegration, common trends
Stylized facts about financial data: volatility clustering, volatility comovements, leptokurtosis
Engle's ARCH and Bollerslev's GARCH. The properties of ARCH and GARCH processes: stationarity, existence of moments, thickness of tails, persistence
Estimation of ARCH and GARCH models. The Maximum Likelihood estimator and its asymptotic properties
Testing for ARCH effects
Conditionally non-normal innovations and Quasi-ML estimation
ARCH-M models: time varying risk premium
IGARCH models: long memory in variance