Economics 718, Applied Time Series

Transcription

1 Spring 2018 K. West Economics 718, Applied Time Series This Spring, EC718 will study linear time series models, concentrating on aspects relevant to current empirical research in macroeconomics, international economics and finance. The emphasis will be on applications rather than theory. The prerequisite is Econ709 and Econ710. Requirements include homework assignments (15% of the grade) and a research paper (85% of the grade). There is no final exam. The paper may be on any topic related to the course content. In previous years, virtually all papers have been empirical, though papers in theoretical or applied econometrics are welcome. For second year Ph. D. students in economics, the paper generally forms the basis of what ultimately is a third year field paper. Presentations of preliminary versions of the paper will take place in class in the last session of the semester on May 2. The final, written version of the paper is due Monday, June 4. There will be three homework assignments, tentatively scheduled as follows: Assigned Due January 31 February 14 February14 March 14 March 14 April 18 Students will also be asked to submit a one page paper proposal, tentatively due April 11. A recommended though optional text is James D. Hamilton's Time Series Analysis, 1994, Princeton: Princeton University Press. Much of the reading will be from journals, working papers and Handbook chapters. Other treatments of some of the material in the course may be found in Granger, C.W.J. and Paul Newbold, 1986, Forecasting Economic Time Series, 2 nd edition, New York: Academic Press. Somewhat more technical development of the topics covered in the first part of the course may be found in Anderson, T.W., 1971, The Statistical Analysis of Time Series, New York: John Wiley and Sons; Brockwell, Peter J. and Richard A. Davis, Time Series: Theory and Methods, 1991, 2 nd edition, New York: Springer Verlag; and Fuller, Wayne A., 1996, Introduction to Statistical Time Series, 2 nd edition, New York: John Wiley and Sons. An outline of the course and then a detailed reading list follows. In the reading list, a * indicates optional reading. The list of applications is tentative and may be adjusted as the semester progresses.

2 Outline I. Univariate models A. Basic time domain concepts B. Univariate models C. Spectral analysis D. Trends vs. unit roots E. Applications II. Multivariate models A. Basic concepts B. Factor models III. Estimation and inference A. Basic theory B. Covariance matrix estimation C. Bias D. Applications IV. Unit Roots A. Univariate B. Multivariate V. Forecasting A. Forecast combination and instability B. Inference about Predictive Accuracy 2

3 I. Univariate models A. Basic time domain concepts Hamilton, 1, , B. Univariate models Hamilton, , 3.7, 4; ch. 21 (in ch. 21, skip the parts that discuss estimation) *van Dijk, Dick, Timo Terasvirta and Philip Hans Franses, 2002, Smooth Transition Autoregressive Models a Survey of Recent Developments, Econometric Reviews, , *Hansen, Bruce E., 2011, Threshold Autoregression in Economics, Statistics and Its Interface *Ing, Ching-Kang, 2003, Multistep Prediction in Autoregressive Processes, Econometric Theory C. Spectral analysis Hamilton, 6.1, 6.4 D. Trends vs. unit roots Hamilton, ch. 15 *Hamilton, 16, 17. E. Applications Marcellino, Massimiliano, James H. Stock and Mark W. Watson, 2006, A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series, Journal of Econometrics 135, West, Kenneth D. and Dongchul Cho, 1995, The Predictive Ability of Several Models of Exchange Rate Volatility, Journal of Econometrics 69, *Christiano, Lawrence J. and Terry J. Fitzgerald, 2003, The band pass filter, International Economic Review 44: *Faust, Jon and Jonathan Wright, 2013, Forecasting Inflation, 3-56 in Handbook of Economic Forecasting, vol. 2A, G. Elliott and A. Timmermann (eds.), Amsterdam: Elsevier. *Granger, Clive W and Yongil Jeon, 2004, Forecasting Performance of Information Criteria with Many Macro Series, Journal of Applied Statistics 31, *Stock, James H. and Mark W. Watson, 1999, A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series, Festschrift in Honor of C.W.J. Granger, R.F.Engle and H. White (eds.), Oxford University Press. 3

4 II. Multivariate models. A. Basic concepts Hamilton, 10.1, 10.2, 11.2, *Müller, Ulrich K. and Mark W. Watson, 2017, Long-Run Covariability, manuscript, Princeton University. B. Structural VARs Fry, Renee and Adrian Pagan, 2011, Sign Restrictions in Structural Vector Autoregressions: A Critical Review, Journal of Economic Literature, 49(4), Jordà, Òscar, 2005, Estimation and Inference of Impulse Responses by Local Projections, American Economic Review 95:1, Ramey, Valerie A., 2016, Macroeconomic Shocks and Their Propagation, in Handbook of Macroeconomics, vol 2A, John B. Taylor and Harald Uhlig (eds). *Baumeister, Christiane and James D. Hamilton, 2015, Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information, Econometrica, 83(5), *Nakamura, Emi and Jón Steinsson, 2017, Identification in Macroeconomics, manuscript, Columbia University. *Paustian, Matthias, 2007, Assessing Sign Restrictions, The B.E. Journal of Macroeconomics, 7(1), *Stock, James H. and Mark W. Watson, 2017, Identification and Estimation of Dynamic Causal Effects in Macroeconomics, working paper, Princeton University. C. Factor models Bai, Jushan and Serena Ng, 2008, Large Dimensional Factor Analysis, Foundations and Trends in Econometrics, 3(2), , *Stock, James H. and Mark W. Watson, 2016, Factor Models and Structural Vector Autoregressions in Macroeconomics, in Handbook of Macroeconomics, vol 2A, John B. Taylor and Harald Uhlig (eds). D. Applications Blanchard, Olivier and Danny Quah, 1989, The Dynamic Effect of Demand and Supply Disturbances, American Economic Review, Lunsford, Kurt G. and Kenneth D. West, 2017, Some Evidence on Secular Drivers of U.S. Safe Real Rates, manuscript, University of Wisconsin. Ramey, Valerie, 2010, Identifying Government Spending Shocks: It s All in the Timing, QJE CXXVI, Swanson, Eric T., 2017, Measuring the Effects of Federal Reserve Forward Guidance and Asset Purchases on Financial Markets, manuscript, University of California at Irvine. West, Kenneth D. and Ka-fu Wong, 2014, A factor model for co-movements of commodity prices, Journal of International Money and Finance 42,

5 III. Estimation and inference A. Basic theory. Hamilton, 7.1, 7.2, 14 *Andrews, Donald W. K., 1999, Consistent Moment Selection Procedures for GMM Estimation, Econometrica 67, *Ogaki, Masao, 1993, Generalized Method of Moments: Econometric Applications, in Maddala, Rao and Vinod (eds.) Handbook of Statistics, Vol 11, Amsterdam: North Holland. *Hansen, Lars Peter, 1982, Large Sample Properties of Generalized Method of Moments Estimators, Econometrica 50, *White, Halbert, 1984, Asymptotic Theory for Econometricians, New York, Academic Press, ch. V. B. Covariance matrix estimation Hamilton, 10.5 *Kiefer, Nicholas M and Timothy J. Vogelsang, 2003, Heteroskedasticity-Autocorrelation Robust Standard Errors Using the Barlett Kernel without Truncation, Econometrica *Newey, Whitney K., and Kenneth D. West, 1994, Automatic Lag Selection in Covariance Matrix Estimation, Review of Economic Studies 61, *West, Kenneth D., 1997, Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator, Journal of Econometrics, C. Bias Kilian, Lutz, 1998, Small-sample confidence intervals for impulse response functions, Review of Economics and Statistics 80, West, Kenneth D., 2017, Approximate Bias in Time Series Regression, manuscript, University of Wisconsin. *Anatolyev, Stanislav, 2005, GMM, GEL, Serial Correlation, and Asymptotic Bias, Econometrica 73(3), *Engsted, Tom and Thomas Q. Pedersen, 2014, Bias-correction in Vector Autoregressive Models: a Simulation Study, Econometrics 2, *Yuriy Gorodnichenko and Byoungchan Lee, 2017, A Note on Variance Decomposition with Local Projections, NBER Working Paper No *Kilian, Lutz and Yun Jung Kim, 2011, How Reliable Are Local Projection Estimators of Impulse Responses?, The Review of Economics and Statistics, 93(4), D. Applications Hansen, Lars Peter and Kenneth J. Singleton, 1982, Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models, Econometrica 50, Hansen, Lars Peter and Kenneth J. Singleton, 1984, Errata, Econometrica 52, West, Kenneth D., 1988, Dividend Innovations and Stock Price Volatility, Econometrica 56,

6 IV. Unit Roots A. Univariate Hamilton, 15.5, 16.1, 16.2, , pp , pp *Stock, James H., 1994, Unit Roots, Structural Breaks and Trends, in Handbook of Econometrics, Volume IV, R. Engle and D. McFadden (eds), Elsevier: Amsterdam. B. Multivariate Hamilton 18.1, 18.2, 19.1, 20 *Müller, Ulrich K and Mark W. Watson, 2016, Low-Frequency Econometrics, manuscript, Princeton University. *Watson, Mark W., 1994, Vector Autoregressions and Cointegration, in Handbook of Econometrics, Volume IV, R. Engle and D. McFadden (eds), Elsevier: Amsterdam. C. Application Hamilton, pp (ch. 18), (ch. 20) 6

7 V. Forecasting A. Forecast combination and instability Granger and Newbold, 1976, Forecasting Economic Time Series, *Granger and Newbold, ch. 4 *Smith, Jeremy and Kenneth F. Wallis, 2009, A Simple Explanation of the Forecast Combination Puzzle, Oxford Bulletin of Economics and Statistics 71(3), *Stock, James H. and Mark W. Watson, 2003, Forecasting Output and Inflation: The Role of Asset Prices, Journal of Economic Literature. *Stock, James H. and Mark W. Watson, 2007, Why Has U.S. Inflation Become Harder to Forecast? Journal of Money, Credit and Banking 39, supplement s1, pages B. Inference about Predictive Accuracy Clark, Todd E. and Michael W. McCracken, 2013, Advances in Forecast Evaluation, in Handbook of Economic Forecasting, vol. 2B, G. Elliott and A. Timmermann (eds.), Amsterdam: Elsevier. West, Kenneth D., 2006, Forecast Evaluation, in Handbook of Economic Forecasting, Vol. 1, G. Elliott, C.W.J. Granger and A. Timmerman, eds, Amsterdam: Elsevier. *Calhoun, Gray, 2016, An asymptotically normal out-of-sample test based on mixed estimation windows, manuscript Iowa State University. *Clark, Todd E. and Kenneth D. West, 2006, Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis, Journal of Econometrics 135, 1-2, *Diebold, Francis X., and Roberto S. Mariano, Comparing Predictive Accuracy, Journal of Business and Economic Statistics 13, *Giacomini, Rafaella and Halbert White, 2006, Tests of Conditional Predictive Ability, Econometrica 74, *Hansen, Peter Reinhard and Allan Timmermann, 2015, Equivalence Between Out-of-sample Forecast Comparisons and Wald Statistics, Econometrica 83(6), *McCracken, Michael W., 2007, Asymptotics for out of sample tests of Granger causality, Journal of Econometrics 140(2), *West, Kenneth D., 1996, Asymptotic Inference About Predictive Ability, Econometrica 64, *White, Halbert, 2000, A Reality Check for Data Snooping, Econometrica McCracken, Michael W. and Joseph T. McGillicuddy, 2017, An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts, manuscript, Federal Reserve Bank of St. Louis. West, Kenneth D., Hali J. Edison and Dongchul Cho, 1993, A Utility Based Comparison of Some Models of Exchange Rate Volatility, Journal of International Economics 35,