14.384 | Fall 2013 | Graduate

Time Series Analysis


Asterisked references are more important to the course. The following is a tentative list of topics that will be covered in this course.

Required Text

[Hamilton] = Hamilton, James D. Time Series Analysis. Princeton University Press, 1994. ISBN: 9780691042893.

[Brockwell and Davis] = Brockwell, Peter, and Richard Davis. Time Series: Theory and Methods. Springer-Verlag, 1991. ISBN: 9780387974293. [Preview with Google Books]

[Canova] = Canova, Fabio. Methods for Applied Macroeconomic Research. Princeton University Press, 2007. ISBN: 9780691115047. [Preview with Google Books]

[DeJong and Dave] = DeJong, David, and Chetan Dave. Structural Macroeconometrics. Princeton University Press, 2011. ISBN: 9780691126487. [Preview with Google Books]

[Hall and Heyde] = Hall, Peter, and C. C. Heyde. Martingale Limit Theory and Its Application. Probability and Mathematical Statistics. Academic Press, 1980. ISBN: 9780123193506.

[Griliches and Intriligator] = Griliches, Zvi, and Michael Intriligator, eds. Handbook of Econometrics. Vol. 3. North Holland, 1986. ISBN: 9780444861870.

[Lütkepohl] = Lütkepohl, Helmut. Introduction to Multiple Time Series Analysis. Springer-Verlag, 1993. ISBN: 9780387569406.

I. Introduction: Stationary Time Series
1–3 Introduction to stationary time series [Hamilton] Chapters 1–5, 7, and 8.
*[Hall and Heyde] Chapter 3.
[Brockwell and Davis] Chapters 1, 3, and Section 5.7.
*Newey, W. K., and K. D. West. “A Simple Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica 55, no. 3 (1987): 703–8. *Andrews, D. W. K. “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.” Econometrica 59, no. 3 (1991): 817–58. Beveridge, S., and C. R. Nelson. “A New Approach to Decomposition of Economic Time Series into Permanent and Transitory Components with Particular Attention to Measurement of the ‘Business Cycle’.” Journal of Monetary Economics 7, no. 2 (1981): 151–74. Andrews, D. W. K., and J. C. Monahan. “An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator.” Econometrica 60, no. 4 (1992): 953–66.

den Haan, W. J., and A. Levin. “A Practitioner’s Guide to Robust Covariance Matrix Estimation.” Chapter 12 in Handbook of Statistics. Vol. 26. Edited by G. S. Maddala, and C. R. Rao. North Holland, 2006, pp. 291–341. ISBN: 9780444521033. [Preview with Google Books]

Kiefer, N., and T. Vogelsang. “Heteroskedasticity-Autocorrelation Robust Testing Using Bandwidth Equal to Sample Size.” Econometric Theory 18, no. 6 (2002): 1350–66.

3–4 Frequency domain analysis *[Hamilton] Chapter 6.
[Brockwell and Davis] Chapters 4, and 10. *Baxter, M., and R. King. “Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series.” Review of Economics and Statistics 81, no. 4 (1999): 575–93.

Berk, K. N. “Consistent Autoregressive Spectral Estimates.” Annals of Statistics 2 (1974): 489-502.

Hodrick, R., and E. Prescott. “Post-War U.S. Business Cycles: An Empirical Investigation.” Journal of Money Banking and Credit 28, no. 4 (1997): 1–16.

Christiano, L. J., and T. J. Fitzgerald. “The Band Pass Filter.” NBER Working Paper no. 7257, 1999.

5 Model selection and information *Geweke, J., and R. Meese. “Estimating Regression Models of Finite but Unknown Order.” International Economic Review 22, no. 1 (1981): 55–70.
Ng, S., and P. Perron. “A Note on the Selection of Time Series Models.” Oxford Bulletin of Economics and Statistics 67, no. 1 (2005): 115–34. Leeb, H., and B. M. Potscher. “Model Selection and Inference: Facts and Fiction.” Econometric Theory 21 (2005): 21–59.

———. “The Finite-Sample Distribution of Post-Model-Selection Estimators and Uniform versus Nonuniform Approximations.” Econometric Theory 19 (2003): 100–42.

Hansen, B. “Challenges for Econometric Model Selection.” Econometric Theory 21, no. 1 (2005): 60–8.

Kuersteiner, G. M. “Automatic Inference for Infinite Order Vector Autoregressions.” Econometric Theory 21, no. 1 (2005): 85–115.

II. Mutivariate Stationary Analysis
6–7 VAR *[Hamilton] Chapters 10, and 11.
*[Lütkepohl] Chapters 2, and 3 (2005). Watson, M. “Vector Autoregressions and Cointegration.” Chapter 47 in Handbook of Econometrics. Vol. 4. North Holland, 1999. ISBN: 9780444887665.

Stock, J. H., and M. W. Watson. “Vector Autoregressions.” Journal of Economic Perspectives 15, no. 4 (2001): 101–6.

Wright, J. H. “Confidence Intervals for Univariate Impulse Responses with a Near Unit Root.” Journal of Business and Economic Statistics 18, no. 8 (2000): 368–73.

Killian, L. “Small Sample Confidence Intervals for Impulse Response Functions.” Review of Economics and Statistics 80, no. 2 (1998): 218–30.

Mikusheva, A. “One-Dimensional Inferences in Autoregressive Models in a Potential Presence of a Unit Root.” Econometrica 80, no. 1 (2011): 173–212. (Forthcoming)

8 Structural VARs *Sims, C. A. “Macroeconomics and Reality.” Econometrica 48, no. 1 (1980): 1–48.
*Blanchard, O. J., and D. Quah. “Dynamic Effects of Aggregate Demand and Supply Disturbances.” American Economic Review 79, no. 4 (1989): 655–73. Blanchard, O. J. “A Traditional Interpretation of Macroeconomic Fluctuations.” American Economic Review 79, no. 5 (1989): 1146–64.

King, R. G., C. I. Plosser, et al. “Stochastic Trends and Economic Fluctuations.” American Economic Review 81, no. 4 (1991): 819–40.

Cooley, T., and S. LeRoy. “A Theoretical Macroeconomics: A Critique.” Journal of Monetary Economics 16, no. 3 (1985): 283–308.

Braun, P., and S. Mittnik. “Misspecification in VAR and their Effects on Impulse Responses and Variance Decompositions.” Journal of Econometrics 59, no. 3 (1993): 319–41.

Cooley, T., and M. Dwyer. “Business Cycle Analysis Without Much Theory: A Look at Structural VARs.” Journal of Econometrics 83, no. 1–2 (1998): 57–88.

Wright, J. H. “What does Monetary Policy do to Long-Term Interest Rates at the Zero Lower Bound?The Economic Journal 122, no. 564 (2012): F447–66.

Moon, H. R., F. Schorfheide, et al. “Inference for VARs Identified with Sign Restrictions.” Manuscript, University of Pennsylvania (2009).

9 VAR and DSGE models Chari, V., P. Kehoe, et al. “A Critique of Structural VARs Using Business Cycle Theory.” Federal Reserve Bank of Minneapolis, Research Department Staff Report 364 (2005).
*Christiano, L., M. Eichenbaum, et al. “Assessing Structural VARs.” Northwestern University, manuscript (2005). *Fernandez Villaverde, J., J. Rubio Ramirez, et al. “The ABC and (D’s) to Understand VARs.” NYU manuscript (2005).

Erceg, C, L. Guerrieri, et al. “Can Long Run Restrictions Identify Technology Shocks?” Board of Governors of the Federal Reserve, International Finance discussion paper 792 (2005).

Lippi, M., and L. Reichlin. “VAR Analysis, Non-Fundamental Representation, Blaschke Matrices.” Journal of Econometrics 63, no. 1 (1994): 307–25.

Faust, J., and E. Leeper. “Do Long Run Restrictions Really Identify Anything?Journal of Business and Economic Statistics 15, no. 3 (1997): 345–53.

10–11 Factor model and FAVAR *Stock, J. H., and M. W. Watson. “Implications of Dynamic Factor Models for VAR Analysis.” NBER Working Paper no. 11467, 2005.
Bernanke, B. S., and J. Boivin. “Monetary Policy in a Data-rich Environment.” Journal of Monetary Economics 50, no. 3 (2003): 525–46. *Bernanke, B. S., J. Bovian, et al. “Measuring the Effects of Monetary Policy: A Factoraugmented Vector Autoregressive (FAVAR) Approach.” Quarterly Journal of Economics 120 (2005): 387–422.

*Forni, M., D. Giannoni, et al. “Opening the Black Box: Structural Factor Models with Large Cross-Sections.” European Central Bank, working paper 712.

Chamberlain, G., and M. Rothschild. “Arbitrage, Factor Structure and Mean-Variance Analysis of Large Asset Markets.” Econometrica 51, no. 5 (1983): 1281–304.

Favero, C. A., M. Marcellino, et al. “Principal Components at Work: The Empirical Analysis of Monetary Policy with Large Datasets.” Journal of Applied Econometrics 20, no. 5 (2005): 603–20.

Forni, M., M. Hallin, et al. “The Generalized Dynamic Factor Model: Identification and Estimation.” Review of Economics and Statistics 82, no. 4 (2000): 540–54.

Bai, J., and S. Ng. “Determining the Number of Factors in Approximate Factor Models.” Econometrica 70, no. 1 (2002): 191–221.

———. “Determining the Number of Primitive Shocks in Factor Models.” Journal of Business Economics and Statistics 25 (2007): 52–60.

*———. “Instrumental Variable Estimation in a Data Rich Environment.” Econometric Theory 26, no. 6 (2010): 1577–606.

Forni, M., M. Hallin, et al. “One-Sided Representations of Generalized Dynamic Factor Models.” Manuscript (2011).

III. Univariate Non-Stationary Processes
12 Asymptotic theory of empirical processes *[Hamilton] Sections 17.1–17.3.
[Hall and Heyde] Chapters 3, 4, and 5, and the Appendix.
13–14 Univariate unit roots and near unit root problem *[Hamilton] Chapter 17.
*Stock, J. H. “Unit Roots and Trend Breaks in Econometrics.” Sections 1–4 in Handbook of Econometrics. Vol. 4. North Holland, 1999, pp. 2740–841. ISBN: 9780444887665. Dickey, D. A., and W. A. Fuller. “Distribution of the Estimators for Autoregressive Time Series with a Unit Root.” Journal of the American Statistical Association 74, no. 366a (1979): 427–31.

Campbell, J. Y., and P. Perron. “Pitfalls and Opportunities: What Macroeconomists Should Know About Unit Roots.” NBER Macroeconomics Annual 6 (1991): 141–201. (NBER Working Paper no. 100)

Andrews, D. W. K. “Exactly Median-Unbiased Estimation of First Order Autoregressive/Unit Root Models.” Econometrica 61, no. 1 (1993): 139–65.

Hansen, B. E. “The Grid Bootstrap and the Autoregressive Model.” Review of Economics and Statistics 81, no. 4 (1999): 594–607.

*Phillips, P. C. B. “Toward a Unified Asymptotic Theory for Autoregression.” Biometrika 74, no. 3 (1987): 535–47.

Stock, J. “Confidence Intervals for the Largest Autoregressive Root in U.S. Macroeconomic Time Series.” Journal of Monetary Economics 28, no. 3 (1991): 435–59.

Mikusheva, A.  “Uniform Inference in Autoregressive Models.” Econometrica 75, no. 5 (2007): 1411–52.

15 Structural breaks and non-linearity *[Hamilton] Chapter 22.
*Andrews, D. W. K. “Tests for Parameter Instability and Structural Change with Unknown Change-Point.” Econometrica 61, no. 4 (1993): 821–56. *Hansen, B. E. “The New Econometrics of Structural Change: Dating Breaks in U.S. Labor Productivity.” Journal of Economic Perspectives 15, no. 4 (2001): 117–28.

*Perron, P. “The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis.” Econometrica 57, no. 6 (1989): 1361–401.

Andrews, D. W. K., and W. Ploberger. “Optimal Tests When a Nuisance Parameter is Present Only Under the Alternative.” Econometrica 62, no. 6 (1994): 1383–414.

Bai, J. S. “Estimating Multiple Breaks One at a Time.” Econometric Theory 13, no. 3 (1997): 315–52.

Bai, J., and P. Perron. “Estimating and Testing Linear Models with Multiple Structural Changes.” Econometrica 66, no. 1 (1998): 47–78.

Bai, J., R. L. Lumsdaine, et al. “Testing For and Dating Common Breaks in Multivariate Time Series.” Review of Economic Studies 65, no. 3 (1998): 395–432.

Zivot, E., and D. W. K. Andrews. “Further Evidence on the Great Crash, the Oil Price Shock, and the Unit Root Hypothesis.” Journal of Business and Economic Statistics 10, no. 3 (1992): 251–70.

IV. Multivariate Non-Stationary
16–17 Multivariate unit roots and co-integration Stock, J. H. “Asymptotic Properties of Least Squares Estimators of Cointegrating Vectors.” Econometrica 55, no. 5 (1987): 1035–56.
Stock, J. H., and M. W. Watson. “A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems.” Econometrica 61, no. 4 (1993): 783–820. *Watson, M. W. “Vector Autoregressions and Cointegration.” Sections 1, and 2 in Handbook of Econometrics. Vol. 4. North Holland, 1999, pp. 2844–915. ISBN: 9780444887665.

*———. “Cointegration.” In The New Palgrave Dictionary of Economics. 2nd ed. Edited by Steven N. Durlauf, and Lawrence E. Blume. Palgrave Macmillan, 2008. ISBN: 9780333786765.

18 Persistent regressors (prediction regression) Bekaert, G., and R. J. Hodrick. “Expectations Hypotheses Test.” Journal of Finance 56, no. 4 (2001): 1357–94.
Campbell, J. Y., and M. Yogo. “Efficient Tests of Stock Return Predictability.” Journal of Financial Economics 81, no. 1 (2006): 27–60. *Cavanagh, C. L., G. Elliott, et al. “Inference in Models with Nearly Integrated Regressors.” Econometric Theory 11 (1995): 1131–47.

*Stambaugh, R. F. “Predictive Regressions.” Journal of Financial Economics 54, no. 3 (1999): 375–421.

Jansson M., and M. J. Moreira. “Optimal Inference in Regression Models with Nearly Integrated Regressors.” Econometrica 74, no. 3 (2006): 681–715.

V. GMM and Related Issues
19 GMM and simulated GMM *[Hamilton] Chapter 14.
*[DeJong and Dave] Chapter 7. [Canova] Chapter 5.

*Hansen, L. P. “Large Sample Properties of GMM Estimators.” Econometrica 50, no. 4 (1982): 1029–54.

*Hansen, L. P., and K. Singleton. “Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models.” Econometrica 50, no. 5 (1982): 1269–86. (corrigenda, 1984).

Mc Fadden, D. “A Method of Simulated Moments of Estimation for Discrete Response Models without Numerical Integration.” Econometrica 57, no. 5 (1989): 995-1026.

Pakes, A., and D. Pollard. “Simulation and the Asymptotics of Optimization Estimators.” Econometrica 57, no. 5 (1989): 1027–57.

*Lee, B., and B. Ingram. “Simulation Estimation of Time Series Models.” Journal of Econometrics 47, no. 2–3 (1991): 197–205.

Duffie, D., and K. Singleton. “Simulated Moments Estimation of Markov Models of Asset Prices.” Econometrica 61, no. 4 (1993): 929–52.

*Smith, A. “Estimation of Nonlinear Time Series Models Using Simulated VARs.” (PDF) Journal of Applied Econometrics 8 (1993): s63–84.

20 Weak IV Andrews, D. W. K., M. Moreira, et al. “Optimal Two-Sided Invariant Similar Tests for Instrumental Variables Regression.” Econometrica 74 (2006): 715–52.
Andrews, D. W. K., and J. H. Stock. “Inference with Weak Instruments.” In Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society. Vol. 3. Edited by R. Blundell, W. K. Newey, and T. Person. Cambridge University Press, 2007. ISBN: 9780521692106. [Preview with Google Books] Kleibergen, F. R., and S. Mavroeidis. “Weak Instrument Robust Tests in GMM and the New Keynesian Phillips Curve.” (PDF) Manuscript, Brown University (2008).

Staiger, D., and J. H. Stock “Instrumental Variables Regression with Weak Instruments.” Econometrica 65, no. 3 (1997): 557–86.

*Stock, J. H., and M. Yogo. “A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments.” Journal of Business and Economic Statistics 20 (2002): 518–29.

*Stock, J. H., and J. Wright. “GMM With Weak Identification.” Econometrica 68, no. 5 (2000): 1055–96.

Yogo, M. “Estimating the Elasticity of Intertemporal Substitution when the Instruments are Weak.” Review of Economics and Statistics 86, no. 3 (2004): 797–810.

VI. Likelihood Methods
21 Kalman filter and its applications *[Hamilton] Chapter 13.
[Canova] Chapter 6. *Hamilton, J. D. “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica 57, no. 2 (1989): 357–84.

22 ML estimation of DSGE [DeJong and Dave] Chapter 8.
[Canova] Chapter 6. Sargent, T. “Two Models of Measurements and the Investment Accelerator.” Journal of Political Economy 97, no. 2, (1989): 251–87.

Ingram, Kocherlakota, and Savin. “Explaining Business Cycles: A Multi-Shock Approach.” Journal of Monetary Economics 34, no. 3 (1994): 415–28.

Hansen, and Sargent. Recursive Linear Models of Dynamic Economies. Princeton University Press, 2013. ISBN: 9780691042770. [Preview with Google Books]

Ireland, P. “A Method for Taking Models to Data.” Journal of Economic Dynamics and Control 28, no. 4 (2004): 1205–26.

White, H. “Maximum Likelihood Estimation of Misspecified Models.” Econometrica 50, no. 1 (1982): 1–25.

23 Identification and weak identification of DSGE Canova, F., and L. Sala. “Back to Square One: Identification Issues in DSGE Models.” Journal of Monetary Economics 56, no. 4 (2009): 431–49.
Iskrev, N. “Evaluating the Strength of Identification in DSGE Models. An a Priori Approach.” Bank of Portugal working paper (2010). Komunjer, I., and S. Ng. “Dynamic Identification of DSGE Models.” _Forthcoming Econometrica _ 79, no. 6 (2011): 1995–2032.

Andrews, I., and A. Mikusheva. “Maximum Likelihood Inference in Weakly Identified DSGE Models.” Manuscript (2011).

Müller, U. “Measuring Prior Sensitivity and Prior Informativeness in Large Bayesian Models.” Manuscript (2011).

VII. Bayesian Methods
24 Bayesian concepts *[Hamilton] Section 12.3.
25 Markov Chain Monte Carlo (MCMC) *Chib, S., and E. Greenberg. “Understanding the Metropolist-Hastings Algorithm.” American Statistician 49 no. 4 (1995): 327–35.
*———. “Markov Chain Monte Carlo Simulation Methods in Econometrics.” Econometric Theory 12, no. 3 (1996): 409–31. *Chib, S. “Markov Chain Monte Carlo Methods: Computation and Inference.” Chapter 5 in Handbook of Econometrics. Vol. 5. Edited by J. J. Heckman, and E. Leamer. North Holland, 2001, pp. 3564–634. ISBN: 9780444823403.

Chib, S., F. Nardari, et al. “Markov Chain Monte Carlo methods for Stochastic Volatility Models.” Journal of Econometrics 108, no. 2 (2002): 281–316.

26 Estimation of DSGE models using Bayesian methods Del Negro, M., and F. Schorfheide. “Priors from General Equilibrium Models for VARs.” International Economic Review 45, no. 2 (2004): 643–73.
Del Negro, M. Schorfheide, et al. “On the Fit and Forecasting Performance of New Keynesian Models.” Journal of Business and Economic Statistics (2007). Rabanal, P., and J. Rubio-Ramirez. “Comparing New Keynesian Models of the Business Cycle: A Bayesian Approach.” Journal of Monetary Economics 52, no. 6 (2005): 1151–66.

Fernandez-Villaverde, J., and J. Rubio-Ramirez. “Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood.” Journal of Applied Econometrics 20, no. 7 (2005): 891–910.

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Fall 2013
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