Time Series and Panel Data Analysis
- introduce the students to the modern methods of time series and panel data analysis
- prepare students for individual work, in particular on their bachelor's theses
- Explain specifics of time series data
- construct linear models for time series data and apply the Box-Jenkins procedure
- test data for stationarity and transform non-stationary series into stationary ones.
- model the dynamics of several variables simultaneously, and analyze relations between different time series
- be able to estimate different time series models with the help of statistical software
- construct forecasts for macroeconomic and financial variables
- model dependence in conditional variance of times series data
- estimate the basic models of conditional heteroskedacticity using statistical software
- explain specifics of panel data: when it is used and what flexibility it adds to econometric models
- construct and estimate linear models with unobserved heterogeneous effects
- compute the pooled OLS, fixed effects, and random effects estimators
- compute Arellano-Bond estimator
- construct nonlinear models for panel data, in particular, binary choice models, and estimate those models in practice
- Time series: basic conceptsDefinition of time series. Introduction of main characteristics of time series (stationarity, ergodicity, autocovariance function, correlogram). Lag operator.
- ARMA modelsAutoregressive models. Moving-Average models. Wold decomposition. Moments, stationarity and invertibility conditions. Autoregressive Moving-Average models. Aggregation. ADL models.
- Nonstationary time seriesDeviations from stationarity: unit roots, deterministic trends, structural breaks. Tests of stationarity
- Conditional heteroskedasticityARCH and GARCH models: introduction, properties, estimation
- Multivariate time seriesVAR models: properties and characteristics. Granger causality.
- Estimation and forecastingEstimation of ARMA and VAR models. Forecasting. Properties of forecasts. HAC variance estimation
- Panel data: IntroductionIntroduction to panel data analysis. Advantages of panel data
- Linear Panel Data ModelsFixed effects and random effects. Between, within, and pooled estimators. Estimation and hypothesis testing
- Dynamic Panel Data ModelsDynamic panels. Arellano-Bond estimator.
- Nonlinear panel modelsBinary response models with panel data. Logit and probit models of panel data.
- Interim assessment (2 module)0.15 * essay + 0.5 * final exam + 0.2 * midterm + 0.15 * problem sets
- Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2010). Econometric analysis of cross section and panel data / Jeffrey M. Wooldridge. Cambridge, Mass. [u.a.]: MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.263114414
- Wooldridge, J. M. . (DE-588)131680463, (DE-576)298669293. (2006). Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. Mason, Ohio [u.a.]: Thomson/South-Western. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.250894459