Course on Time--series Econometrics

This page is about a course that I teach second--year Ph.D. students at IGIDR, Bombay. The course syllabus is here.

This course will emphasise the application of econometric method to analyse time series properties of data. The data that will be used to demonstrate the application of these tools are here as an R data file.

Classes

• Introduction to time-series analysis. [Slideshow]
• Hands on session #1: An introduction to time series classes in R. [R for time series data]
• Introduction to stochastic processes. [Slideshow]
• Introduction to the AR/MA processes. [Slideshow]
• Hands on session #2: Montecarlo simulations of ARMA models in R. [Simulating ARMA datasets]
• More about AR/MA processes. [Slideshow]
• Hands on session #3: Montecarlo simulations demonstrating impact of shocks in AR/MA models with different AR(1) coefficients. [Simulating shocks in ARMA processes]
• Introducing ARMA processes. [Slideshow]
• Hands on session #4: Identifying AR/MA models with sample datasets: [Dataset1][Dataset2][Dataset3][Dataset4]
• Estimation of AR/MA processes. [Slideshow]
• Model identification, specification. [Slideshow]
• Measuring forecast accuracy. [Slideshow]
• Stochastic models for the second moment: conditional heteroskedasticity. [Slideshow]
• A lecture-demo on Simulation methods using R: MonteCarlo and Bootstrap.[R Program]
• Nonstationary time series models. [Slideshow]
• Analysing the time series dynamics in seasonally adjusted IIP, and Nifty levels from samples.rda.[R Program]

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• Quiz dataset containing two time series, both of them zoo objects. [Dataset1][R quiz]

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• Trend models: Deterministic trends and stochastic trends. [Slideshow]
• Trend models: comparisons. [Slideshow]
• Tests for non-stationarity. [Slideshow]

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• Exam dataset containing two time series, both of them zoo objects. [Exam-dataset][R Exam]

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Books

• Time Series Analysis by J. Hamilton.
• Time series analysis -- forecasting and control by G. Box and G. Jenkins.
• Time series forecasting by C. Chatfield.
• Unit roots, cointegration and structural change by G. S. Maddala and In-Moo Kim
• Cointegration, error correction and the econometric analysis of non-stationary data by A. Bannerjee et al.

Useful links

• Ideas for computational tools.

Back up to Susan Thomas' teaching page