ARE/ECN 240C
Department of Economics
University of California, Davis
Professor Òscar Jordà
ARE/ECN 240C
Instructor:
TIME SERIES
ANALYSIS
Winter 2006
Professor Òscar Jordà
1150 Social Sciences and Humanities Bldg.
Phone: 752 7021
e-mail: ojorda@ucdavis.edu
CLASS URL:
http://www.econ.ucdavis.edu/faculty/jorda/class/240c/240c.html
Class Meets:
Office Hours:
T – R, 2:10 – 4:00*pm. Room: WICKSN 1038
*Please notice the class will typically meet until 4:00pm, rather
than 3:30pm.
Mondays, 10-12; Wednesdays 1-2pm, or by appointment
Textbook: Hamilton, J. D. (1994) Time Series Analysis, Princeton University Press, New
Jersey. I will follow Hamilton's book rather closely. Regardless, this is a great book,
worth having in your library. I will provide additional references for specific topics but
these are easily available through the library or on the web.
Assignments: I am hoping to have 5 computer assignments that will involve the packages
EViews and GAUSS. The home page for the course has a number of links to brief
introductions to these programs. There will also be regular assignments of a more
theoretical nature that do not require the use of a computer.
Resources: Mostly, you should check the home page for the course. Additional readings
for each topic can be found at the end of this document.
Grading: There will be three components to your grade, assignments (30%), midterm/s
(30%) and final (40%).
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ARE/ECN 240C
Department of Economics
University of California, Davis
Professor Òscar Jordà
Course Outline:
TOPIC 0: REVIEW OF PROBABILITY THEORY
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Basic Definitions: sample space, -algebra, probability measure, probability space, random
variable, distribution function, Borel-measurable functions, expected value.
Modes of Convergence: convergence in probability, mean square, almost sure and convergence in
law.
o Mann-Wald Theorem
o Cramer-Wold Theorem
o Slutzky’s Theorem
Laws of Large Numbers:
o Kolmogorov: I and II
o Khinchine
Central Limit Theorems:
o Lindeberg-Levy
o Lindeberg-Fuller
The Delta Method
TOPIC 1: INTRODUCTION TO UNIVARIATE, STATIONARY TIME SERIES
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Introduction: cross-section vs. time-series
Preliminary Concepts:
o Lag Operators
o White noise, martingales and martingale difference sequences
o Autocovariances and autocorrelations
o Stationarity:
 Weak stationarity
 Strong stationarity
 Random walks
o Ergodicity and the Ergodic Theorem
o Uniform-mixing and strong-mixing
Central Limit Theorem for Martingale Difference Sequences
Central Limit Theorem for Dependent Processes
o The Beveridge-Nelson Decomposition
Basic ARMA models:
o MA, AR, and ARMA models
o Common transformations and identification
o Wold representation theorem
o State-space representation
TOPIC 2: LARGE SAMPLE ESTIMATION, HYPOTHESIS TESTING AND FORECASTING
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Maximum Likelihood, Extremum Estimation, Minimum Distance and GMM
o Consistency
o Asymptotic Normality
MLE for ARMA models
o AR ML: exact versus conditional likelihood
o MA ML: exact versus conditional likelihood
o ARMA ML
Review of Numerical Optimization Routines
o Newton’s Method
o Common algorithms: Newton-Raphson; Quadratic-Hill; Gauss-Newton/BHHH;
Marquardt; DFP.
o Elements of numerical optimization algorithms
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
Statistical Inference
o Wald, Likelihood Ratio and Lagrange Multiplier principles
o Non-standard tests:
 QMLE
 Unidentified parameters under the null
 Ljung-Box statistic
The Bootstrap
o Definition and Edgeworth Expansion
o The Classical Bootstrap
o Applications:
 Bias reduction
 Standard error estimation
 Hypothesis testing
 Confidence intervals
o Bootstrap variations
o Bootstrap for time series data
 Parametric bootstrap
 Block bootstrap
Forecasting
o ARMA models
o Nonlinear models: Methods
 Naïve
 Exact
 Monte Carlo
 Bootstrap
o Direct Forecasting
o Tests of predictive ability
TOPIC 3: UNIT ROOTS
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Detrending Methods: deterministic vs. stochastic trends
Asymptotic distribution of the simple trend model
Unit Roots
o Preliminaries: Brownian motion
o Functional central limit theorem:
 Convergence in law of random functions
 Convergence in probability of random functions
 Continuous mapping theorem
o The Dickey-Fuller distribution
o Functional central limit theorem for dependent processes
 The augmented Dickey-Fuller test: derivation
 The Phillips-Perron test: derivation
o Local-to-unity asymptotics
TOPIC 4: COVARIANCE STATIONARY VECTOR TIME SERIES
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The VAR(p)
o Presentation
o Stationarity
o Wold’s theorem and the VMA representation
Heteroskedasticity and Autocorrelation Variance Estimation
o Newey-West estimator
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
Granger causality and exogeneity
MLE of vector processes
Structural interpretation of VARs
o The impulse response function
o The variance decomposition
o Identification and Interpretation
Inference in VARs
Estimation and Inference of Impulse Responses by Local Projections
TOPIC 5: GENERALIZED METHOD OF MOMENTS
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Introduction: classical method of moments
GMM
o Formulation
o Optimal weighting matrix
o Asymptotic distribution
o Inference:
 The J-statistic
 Tests of subsets of orthogonality conditions
 LR tests
o MLE and GMM
 Wald tests
 LM tests
o Extensions
TOPIC 6: COINTEGRATION
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Motivation: spurious regressions
Definition:
o Properties
o Error correction representation
o Granger representation theorem
o Phillips triangular representation
o Stock-Watson common trends representation
Testing
o Engle-Granger 2-step cointegration test
 Corrections for serial correlation
Full Information Maximum Likelihood analysis of cointegrated systems
o Preliminaries: canonical correlations
o Johansen’s test
o Concentrating the likelihood
o Hypothesis testing
TOPIC 7: TIME SERIES MODELS FOR HIGHER MOMENTS AND TRANSITION DATA
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ARCH models
o Relation to ARMA
o MLE – GARCH
o Testing for ARCH
o Extensions
ACD models
o Specification
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
o Estimation
ACH models
o Presentation
o Relation to ACD
o Estimation
ACI models
o Presentation
o Relation to ACD
o Estimation
TOPIC 8: STATE SPACE MODELING AND THE KALMAN FILTER
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State Space Representation
Kalman Filter
o Overview
o Algorithm
o Forecasting
MLE with the Kalman filter
Asymptotic properties of MLE/QMLE
Additional Reading
Almost all the material for the class comes from Hamilton's book so you need not worry
about the reading list except when indicated in class. The references contained in Hamilton's
book are quite comprehensive if you ever need to go deeper into a topic. The references
below might be helpful if you have difficulty understanding the material. A * indicates
references I find particularly useful.
TOPIC 0: REVIEW OF PROBABILITY THEORY
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*Amemiya, T. (1985), Advanced Econometrics. Cambridge: Harvard University Press.
(Chapter 3). [Amemiya]
Davidson, James (1994) Stochastic Limit Theory, Oxford: Oxford University Press.
Davidson, Russell and James G. Mackinnon (1993), Estimation and Inference in
Econometrics. New York: Oxford University Press. (Chapter 4) [Davidson and Mackinnon]
*Hamilton, Chapter 7.
Hayashi, Fumio (2000) Econometrics. Princeton: Princeton University Press. (Chapter 2)
[Hayashi]
*White, Halbert (1999) Asymptotic Theory for Econometricians, Revised Edition. San
Diego: Academic Press. Chapters 2-5
TOPIC 1: INTRODUCTION TO UNIVARIATE, STATIONARY TIME SERIES
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Box, G.E.P. and G.M. Jenkins (1976), Time Series Analysis: Forecasting and Control, 2nd ed.
San Francisco: Holden Day.
Gourieroux C. and A. Monfort (1997), Time Series and Dynamic Models. Cambridge:
Cambridge University Press.
*Hamilton, Chapters 1-3.
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
*Sargent, T. J. (1987), Macroeconomic Theory. Boston: Academic Press. (Chapters 9-11).
TOPIC 2: ESTIMATION, INFERENCE AND FORECASTING
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*Amemiya, Chapter 4.
Berkowitz, Jeremy and Lutz Kilian (2000) “Recent Developments in Bootstrapping Time
Series,” Econometric Reviews, 19(1), 1-48.
*Cameron, A. Colin and Pravin Trivedi (2004) Microeconometrics: Methods and
Applications, Chapter 11: Bootstrap. U.C. Davis, mimeo.
Clements, Michael P. and David F. Hendry (1998) Forecasting Economic Time Series,
Cambridge: Cambridge University Press.
*Davidson, R. and J. G. MacKinnon, Chapter 8.
*Diebold, F. X. and R. S. Mariano (1995) “Comparing Predictive Accuracy,” Journal of
Economics and Business Statistics, 13, 253-263.
*Engle, R. F. (1984), "Wald, Likelihood Ratio, and Lagrange Multiplier Tests in
Econometrics," Ch. 13 in Handbook of Econometrics, Vol. II, eds. Z. Griliches and M.D.
Intrilligator, Amsterdam: North-Holland.
Giacomini, R. and H. White (2004) “Tests of Conditional Predictive Ability,” UCLA
manuscript.
*Granger, C. W. J. and P. Newbold, (1986) Forecasting Economic Time Series. Academic
Press.
*Granger, C. W. J. and Timo Terasvirta (1993) Modelling Nonlinear Economic
Relationships, Oxford: Oxford University Press, Chapter 8.
*Greene, W. H. (1997) Econometric Analysis, 4th Edition. New Jersey: Prentice Hall.
(Chapter 5).
Hall, Peter (1992) The Bootstrap and Edgeworth Expansions, New York: Springer-Verlag.
Horowitz, J. L. (2001) “The Bootstrap,” Handbook of Econometrics, volume 5. Amsterdam:
North-Holland.
*Hamilton, Chapters 4-5.
*Newey and McFadden (1994) “Large Sample Estimation and Hypothesis Testing,” in The
Handbook of Econometrics, v. 4. Amsterdam: North-Holland.
*West, K. D. (1996) “Asymptotic Inference about Predictive Ability,” Econometrica, 64,
1067-1084.
TOPIC 3: UNIT ROOTS
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Brockwell, P.J. and R. A. Davis. Time Series: Theory and Methods. Springer-Verlag.
Cavanagh, C. L., G. Elliott, and J. H. Stock (1995) “Inference in Models with Nearly
Nonstationary Regressors,” Econometric Theory, v11, 1131-1147.
* Davidson, J. (1994) Stochastic Limit Theory. Oxford; Oxford University Press.
Dickey, D. A. and W. A. Fuller (1979), " Distribution Estimators for Autoregressive Time
Series with a Unit Root," Journal of the American Statistical Association, 74, 437-431.
Fuller, W. A. Introduction to Statistical Time Series. Wiley series in Probability and
Statistics. John Wiley.
*Hamilton, Chapters 15-17.
Phillips, P.C.B. (1986), "Time Series Regression with Unit Roots," Econometrica, 55, 227302.
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
*Phillips, P.C.B. (1987) “Towards a Unified Asymptotic Theory for Autoregression,”
Biometrika, 74, 535-47
Phillips, P.C.B. (1998), "New Tools for Understanding Spurious Regressions,"
Econometrica, 66, 1299-1236.
*Stock, J. H. (1994), "Unit Roots, Structural Breaks, and Trends," in Handbook of
Econometrics, Vol. IV, eds. D. McFadden and R. F. Engle. Amsterdam: North-Holland.
* Tanaka, Katsuto, (1996) Time Series Analysis. New York: John Wiley (Chapter 3 and
Chapter 9).
TOPIC 4: COVARIANCE STATIONARY VECTOR TIME SERIES
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Demiralp S. and K. D. Hoover, (2003), “Searching for the Causal Structure of a Vector
Autoregression,” Oxford Bulletin of Economics and Statistics, 65(0), 745-67.
Den Haan, W. J. and A. Levin, (1997), " A Practioner's Guide to Robust Covariance Matrix
Estimation," Handbook of Statistics 15 (Chapter 12, 291-341)
*Den Haan, W. J. and A. Levin, (1996), " Inferences from Parametric and Non-Parametric
Covariance Matrix Estimation Procedures," NBER Technical Working Paper 195. Both
papers can be downloaded from: http://weber.ucsd.edu/~wdenhaan/papers.html
*Engle, R. F., D. F. Hendry, and J.-F. Richard, (1983), "Exogeneity," Econometrica, 51, 277305.
*Granger, C. W. J. (1980), "Testing for Causality: A Personal Viewpoint," Journal of
Economic Dynamics and Control, 2, 329-352
Granger, C. W. J. (1989), Modelling Economic Series, Oxford: Oxford University Press.
*Hamilton, Chapters 10-11.
*Hendry, D. F. (1995), Dynamic Econometrics, Oxford: Oxford University Press.
Hoover, K. D. and S. M. Sheffrin, (1992), " Causation, Spending, and Taxes: Sand in the
Sandbox or Tax Collector for the Welfare State?" American Economic-Review; 82(1), 22548.
*Jordà, O (2005) “Estimation and Inference of Impulse Responses by Local Projections,”
American Economic Review, March.
Newey W. N. and K. D. West (1987), "A Simple Positive Semi-Definite Heteroskedasticity
and Autocorrelation Consistent Covariance Matrix," Econometrica, 55, 703-708.
*Reinsel, G. C. (1993), Elements of Multivariate Time Series Analysis, New York: SpringerVerlag.
Swanson, N. R. and C. W. J. Granger, (1997), "Impulse Response Functions Based on a
Causal Approach to Residual Orthogonalization in Vector Autoregressions," Journal of the
American Statistical Association, 92(437), 357-367.
TOPIC 5: GENERALIZED METHOD OF MOMENTS
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*Hamilton, Chapter 14.
*Hayashi, Chapters 3-4.
*Wooldridge, Jeff (2002) Econometric Analysis of Cross Section and Panel Data, chapter 14
TOPIC 6: COINTEGRATION
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*Engle, R. F. and C. W. J. Granger, (1991), Long-Run Economic Relationships, Oxford:
Oxford University Press.
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ARE/ECN 240C
Department of Economics
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University of California, Davis
Professor Òscar Jordà
*Johansen, S. (1995), Likelihood-Based Inference in Cointegrated Vector-Autoregressive
Models, Oxford: Oxford University Press.
Hamilton, Chapters 19-20.
*Sims, C. A., J. H. Stock and M. W. Watson, (1990), "Inference in Time Series Models with
some Unit Roots," Econometrica, 58, 113-44.
Watson, M. W. (1994), "Vector Autoregressions and Cointegration," in Handboof of
Econometrics, Vol. IV, eds. D. McFadden and R. F. Engle. Amsterdam: North-Holland.
TOPIC 7: TIME SERIES MODELS FOR HIGHER MOMENTS AND TRANSITION DATA
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Bergin, Paul R. and Òscar Jordà (2001) “Measuring Monetary Policy Interdependence,”
Journal of International Money and Finance, v. 23, n. 5, 761-83..
Engle, Robert F. (1995) ARCH. Selected Readings, Oxford: Oxford University Press.
Engle, Robert F. and Jeffrey R. Russell (1998) “Autoregressive Conditional Duration: A New
Model for Irregularly Spaced Transaction Data,” Econometrica, V. 66, N. 5, 1127-1162.
*Hamilton, James D. and Òscar Jordà (2002) “A Model for the Federal Funds Target,”
Journal of Political Economy, vol. 110, n. 5, 1135-1167.
*Hamilton, Chapter 21.
Jordà, Oscar Holly Liu and Jeffrey Williams (2002) “Non-institutional Market Making
Behavior: The Dalian Futures Exchange,” U.C. Davis, mimeo.
*Marcellino, Massimiliano and Òscar Jordà (2002) “Modelling High-Frequency FX Data
Dynamics,” Macroeconomic Dynamics, v.7, 618-635.
TOPIC 8: STATE SPACE MODELING AND THE KALMAN FILTER
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*Hamilton, Chapter 13.
*Hamilton, J. D. (1994), "State-Space Models," in Handbook of Econometrics, Vol. IV, R. F.
Engle and D. L. McFadden, eds. Amsterdam: North-Holland.
Harvey, Andrew C. (1989), Forecasting Structural Time Series Models and the Kalman
Filter. Cambridge: Cambridge University Press.
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ARE/ECN 240C
Department of Economics
University of California, Davis
Professor Òscar Jordà
TIME SERIES ECONOMETRICIANS IN DAVIS
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Arthur M. Havenner, Professor, Department of Agricultural and Resource Economics.
o Web-site: http://www.agecon.ucdavis.edu/Faculty/Art.H/Havenner.html
o Research Interests: Econometrics, both classical and time series analysis,
involving any aspects of hypothesis testing, policy simulation / optimal control,
and forecasting. If you have any questions about State Space modeling and the
Kalman filter, he is your man.
o Book: Applications of Computer Aided Time Series Modeling, M. Aoki and A.
Havenner, (eds.). Berlin: Springer-Verlag, 1997.
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Prasad Naik, Assistant Professor, Graduate School of Management. Although professor
Naik is formally professor in Marketing, he has a lot of experience in time series
modeling, forecasting and control.
o Web-site: http://www.gsm.ucdavis.edu/Faculty/Profiles/Naik/
o Research Interests: advertising strategy, consumer choice
behavior, sales forecasting, dynamic market response models.
o Representative Publication: "Partial Least Squares Estimator for Single-Index
Models, with Chih-Ling Tsai, Journal of the Royal Statistical Society, Series B,
2000, Vol. 62, No. 4
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Aaron Smith, Assistant Professor, Department of Agricultural and Resource Economics.
o Web-site: http://asmith.ucdavis.edu
o Research Interests: In econometrics, forecasting time series processes using
nonlinear models and measuring the distance between unknown distributions. In
finance, studying excess volatility and bubbles in equity markets.
o Representative Publication: “Stochastic Permanent Breaks” (with Robert Engle),
Review of Economics and Statistics, 81(4): 553-574, 1999.
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Robert Shumway, Professor, Department of Statistics.
o Web-site: http://anson.ucdavis.edu/faculty/shumway.html
o Research Interests: applications of multivariate spectral analysis to high
dimensional problems in discrimination and clustering for multivariate time
series and to linear filter contrasts arising in a multivariate analysis of variance.
Analysis of incomplete data that can be modeled in terms of linear and nonlinear
state-space models.
o Books: Shumway, R.H. (1988). Applied Statistical Time Series Analysis.
Englewood Cliffs, New Jersey: Prentice Hall.
Shumway, R.H. & Stoffer, D. S. (2000). Time Series Analysis and Its
Applications. New York: Springer.
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Chih-Ling Tsai, Professor, Graduate School of Business.
o Web-site: http://www.gsm.ucdavis.edu/Faculty/Profiles/tsai/
o Research Interests: application of statistics in business, regression diagnostics,
model selection, optimal design.
o Books: Regression and Time Series Model Selection, with Allan McQuarrie,
World Scientific, 1998.
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ARE/ECN 240C
Department of Economics
University of California, Davis
Professor Òscar Jordà
TIME SERIES TOPICS
Here is a potpourri of other topics that we will not discuss in class. There is no particular order
and were possible, I enclose relevant references.
Time Series Topics in Finance
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Campbell, John Y., A. W. Lo, and A. C. MacKinlay, (1997), The Econometrics of
Financial Markets. Princeton University Press.
Mills, T. C. (1993), The Econometric Modelling of Financial Time Series. Cambridge
University Press.
ARCH Models and Stochastic Volatility Models
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Engle, R. F. (1995), ARCH Selected Readings, Oxford University Press
Shepard, N., S. Kim., and S. Chib (1998), “Stochastic Volatility: Likelihood
Inference and Comparison with ARCH Models,” Review of Economic Studies, 65,
361-393.
Continuous Time Models
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Bergstrom, A. R. (1990), Continuous Time Econometric Modelling, Oxford
University Press.
Neural Networks
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White, H. (1992), Artificial Neural Networks: Approximation and Learning Theory.
Blackwell Publishers.
Time Series Duration Models
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Engle, R. F. and J. R. Russell, (1998), “Autoregressive Conditional Duration: A New
Model for Irregularly Spaced Transaction Data,” Econometrica, V. 66, N. 5, 11271162.
Jorda, O. and M. Marcellino, (2000), “Stochastic Processes subject to Time Scale
Transformation: An Application to High Frequency FX Data,” Macroeconomic
Dynamics, v. 7, 618-635.
Long Memory Models
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Journal of Econometrics, (1996), Volume 73, Issue 1, July. This issue is a
monograph on Long Memory Models and is edited by Richard T. Baillie and
Maxwell L. King.
Time Series Topics in Macroeconomics
Nonlinear Time Series Models
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Granger, C. W. J. and T. Terasvirta (1993), Modelling Nonlinear Economic
Relationships, Oxford University Press.
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ARE/ECN 240C
Department of Economics
University of California, Davis
Professor Òscar Jordà
Markov Switching Regimes Models
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Hamilton, J. D. (1989), “A New Approach to the Economic Analysis of
Nonstationary Time Series and the Business Cycle,” Econometrica, 57(2), 357-384
Kim, C. J. and C. R. Nelson, (1999), State Space Models with Regime Switching,
MIT Press.
Threshold Autoregressive and Smooth Transition Models
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Tong, H. (1983), Threshold Models in Non-Linear Time Series Analysis, SpringerVerlag
Terasvirta, T. (1994), “Specification, Estimation and Evaluation of Smooth
Transition Autoregressive Models,” Journal of the American Statistical Association.
Structural Break Testing
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Bai, J. and P. Perron, (1998), “ Estimating and Testing Linear Models with Multiple
Structural Changes,” Econometrica, 66(6), 47-78.
Causality in Macroeconomics
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Demiralp, S. (2000), The Structure of Monetary Policy and Transmission
Mechanism, UC Davis Thesis.
Hoover, K. D. (2001), Causality in Macroeconomics, Cambridge University Press.
Other Topics
Time Aggregation
 Jorda, O. and M. Marcellino, (2004), “Time Scale Transformations of Discrete
Time Processes,” Journal of Time Series Analysis, v. 25, n. 6, 873-894.
Simulation Methods for Time Series Models
 Kim, C. J. and C. R. Nelson, (1999), State Space Models with Regime Switching,
MIT Press.
 Gourieroux, C. and A. Monfort, (1996), Simulation Based Econometric Methods,
Cambridge University Press.
Bootstrap in Time Series
 Berkowitz, J. and L. Kilian (2000), “Recent Developments in Bootstrapping
Time Series,” Econometric Reviews, 19(1), 1-48.
Spectral Analysis and Wavelets
 Percival, D. B. and A. T. Walden, (2000), Wavelet Methods for Time Series
Analysis, Cambridge University Press
Dynamic Panel Data Models
 Arellano, M. and B. Honore, (1999), “Panel Data Models: Some Recent
Developments,” prepared for the Handbook of Econometrics, Vol. 5.
Additional Resources
I recommend that you visit Eric Zivot’s home page. He has collected numerous interesting links if
you are interested in time series. This is his web-site:
http://faculty.washington.edu/ezivot/econ584/econ584.htm
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