ECON 690: Topics in Applied Time Series Analysis Professor Mohitosh Kejriwal Spring Lectures: Tuesdays and Thursdays, 2:50-4:20pm in Rawls 2079

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1 ECON 690: Topics in Applied Time Series Analysis Professor Mohitosh Kejriwal Spring 2011 Lectures: Tuesdays and Thursdays, 2:50-4:20pm in Rawls 2079 Offi ce: KRAN 410 Telephone: (765) Offi ce Hours: Tuesdays and Thursdays, 4:30-5:30pm and by appointment Course Overview: This course covers selected topics of current interest in time series analysis with an emphasis on empirical applications rather than the underlying statistical theory. The aim of the course is to equip students with a working knowledge of important econometric techniques used in macroeconomics and financial economics. Each topic will begin with a survey of the relevant state of the art econometric methods followed by a discussion of their applicability to analyzing various economic questions. The course will be primarily based on a discussion of journal articles and working papers Familiarity with a matrix oriented programming language (such as MATLAB) is essential. Prerequisites: ECON Course Format and Grading: There will be no exams. The evaluation for the course will be based on class participation (15%), two in-class presentations (15% each), a replication exercise (15%) and an extensive literature survey (40%). For the first presentation, each student will choose a published/working paper from a list that I will make available soon. The second presentation will be based on the literature survey that each student will need to submit in the last week of the course. The survey topics will also be chosen from a provided list. It is also possible to choose a topic outside the list if it is of special interest to you but you will need to get my approval for this. The first set of presentations is scheduled for February 8 and February 10 while the second set is scheduled for March 1 and March 3. The literature survey is due by March 3. Course Website: All material related to the course will be available on the course website in the Katalyst (located on the web at: You will need to log in with your Purdue username and password. Textbooks: There is no required textbook for the course. Two books that may serve as useful references are: (1) Applied Econometric Time Series, Third Edition, Wiley (by W. Enders) and (2) Analysis of Financial Time Series, Second Edition, Wiley (by R.S. Tsay) 1

2 Other 1. Handbook of Financial Time Series (2009), Springer Verlag. 2. Campbell J.Y., Lo, A.W. and MacKinlay, A.C. (1997): The Econometrics of Financial Markets, Princeton University Press. 3. Hamilton, J.D. (1994): Time Series Analysis, Princeton University Press. 4. Wang, P. (2007): Financial Econometrics, Routledge 5. Diebold, F.X. (2006): Elements of Forecasting, South-Western 6. Gourieroux, C. and Jasiak, J. (2001): Financial Econometrics, Princeton University Press. Emergency: In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to changes that may be necessitated by a revised semester calendar or other circumstances. Course Topics (A) Structural Breaks and Unit Roots The goal is to introduce you to various issues involved in the detection and estimation of structural breaks in time series data. We will learn how to test for breaks, estimate the break points and form confidence intervals as well as distinguish between models with unit roots and those with structural breaks. 1. Andreou, E. and Ghysels, E. (2009): Structural breaks in financial time series, in Handbook of Financial Time Series, Springer Verlag. 2. Andrews, D. (1993): Tests for parameter instability and structural change with unknown change point, Econometrica 61, Bai, J (1997): Estimation of a change point in multiple regression models, Review of Economics and Statistics 79, Bai, J., Lumsdaine, R.L. and Stock, J.H. (1998): Testing for and dating common breaks in multivariate time series, Review of Economic Studies 63, Bai, J. and Perron, P. (1998): Estimating and testing linear models with multiple structural changes, Econometrica 66,

3 6. Hansen, B. (2001): The new econometrics of structural change: dating changes in U.S. labor productivity," Journal of Economic Perspectives 15, Perron, P. (2006): Dealing with structural breaks, in Palgrave Handbook of Econometrics, Palgrave Macmillan, Rapach, D.E. and Wohar, M.E. (2006): Structural breaks and predictive regression models of aggregate U.S. stock returns," Journal of Financial Econometrics 4, Stock, J.H. (1994): Unit Roots, Structural Breaks and trends, Handbook of Econometrics Vol IV, Chapter Vogelsang, T.J. (1999): Sources of nonmonotonic power when testing for a shift in mean of a dynamic time series, Journal of Econometrics 88, (B) Models of Volatility The goal is to develop a working knowledge of models with time varying volatility such as ARCH, GARCH and stochastic volatility models. Estimation, inference and prediction using these models will be discussed. We will also compare several volatility models in terms of their ability to make accurate forecasts. An introduction to multivariate GARCH models will also be given. 1. Andersen, T.G. and Bollerslev, T. (1998): Answering the skeptics: yes, standard volatility models provide accurate forecasts, International Economic Review Bauwens, L., Laurent, S. and Rombouts, J.V.K. (2006): Multivariate GARCH models: a survey, Journal of Applied Econometrics 21, Bollerslev, T. (1986): Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, Engle, R.F. (1982): Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation, Econometrica 50, Hansen, P. and Lunde, A. (2005): A forecast comparison of volatility models: does anything beat a GARCH(1,1)?, Journal of Applied Econometrics 20, Hillebrand, E. (2005): Neglecting parameter changes in GARCH models, Journal of Econometrics 129, Nelson, D (1991): Conditional heteroskedasticity in asset returns: a new approach, Econometrica 59,

4 8. Rapach, D.E. and Strauss, J.K. (2008): Structural Breaks and GARCH Models of exchange rate volatility," Journal of Applied Econometrics 23, Teräsvirta, T. (2009): An introduction to univariate GARCH models, in Handbook of Financial Time Series, Springer Verlag. 10. Zivot, E. (2009): Practical issues in the analysis of univariate GARCH models, in Handbook of Financial Time Series, Springer Verlag. (C) Issues in Forecasting The goal is to learn how to make and evaluate point as well as density forecasts of economic and financial variables. Topics such as statistical tests for equal forecast accuracy, forecast combinations, forecasting in the presence of structural breaks and in-sample versus out-of-sample predictability in the context of data mining will be covered. 1. Bates, J.M. and Granger, C.W.J., (1969): The combination of forecasts, Operations Research Quarterly 20, Clark, T.E. and McCracken, M.W. (2005): The power of tests of predictive ability in the presence of structural breaks, Journal of Econometrics 124, Diebold, F.X. and Mariano, R.S. (1995): Comparing predictive accuracy, Journal of Business and Economic Statistics Diebold, F.X., Gunther, T. and Tay, A.S. (1998): Evaluating density forecasts with applications to finance and management, International Economic Review 39, Giacomini, R and White, H. (2006): Tests of conditional predictive ability, Econometrica 74, Meese, R.A. and Rogoff, K. (1983): Empirical exchange rate models of the seventies: do they fit out of sample?, Journal of International Economics 14, Pesaran, M.H. and Timmermann, A. (2005): Small sample properties of forecasts from autoregressive models under structural breaks, Journal of Econometrics 129, West, K.D. (1996): Asymptotic inference about predictive ability, Econometrica 64, West, K.D. and Cho, D. (1995): The predictive ability of several models of exchange rate volatility, Journal of Econometrics 69,

5 10. White, H. (2000): A reality check for data snooping, Econometrica 68, (D) Factor Models The goal is to understand the role of factor models as a tool for dimension reduction in the analysis of high-dimensional data. Topics to be covered include how to determine the number of factors, how to conduct inference when estimated factors are used in regressions and how to assess the adequacy of observed variables as proxies for latent factors. 1. Bai, J. and Ng, S. (2008): Large dimensional factor analysis, Foundations and Trends in Econometrics 3, Bai, J. (2003): Inferential theory for factor models of large dimensions, Econometrica 71, Bai, J. and Ng, S. (2002): Determining the number of factors in approximate factor models,. Econometrica 70, Bai, J. and Ng, S. (2004): A PANIC attack on unit roots and cointegration, Econometrica 72, Bai, J. and Ng, S. (2006): Confidence intervals for diffusion index forecasts and inference with factor-augmented regressions,. Econometrica 74, Bai, J. and Ng, S. (2006): Evaluating latent and observed factors in macroeconomics and finance,. Journal of Econometrics 113, Bai, J. and Ng, S. (2007): Determining the number of primitive shocks in factor models,. Journal of Business and Economic Statistics 25, Boivin, J. and Ng, S. (2006): Are more data always better for factor analysis?,. Journal of Econometrics 132, Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2000): The generalized dynamic factor model: identification and estimation,. Review of Economics and Statistics 82, Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2004): The generalized dynamic factor model: consistency and rates,. Journal of Econometrics 119,