General: Green, W., Econometric Analysis. Prentice-Hall.
General Time series: Hamilton, J. 1994. Time Series Analysis. Princeton University Press.
Classic on general asymptotics: Billingsley, Convergence of Probability Measures, 1968. Wiley:NY.
Time series, deeper theory: Brockwell, P. and Davis, R. 1998. Time Series: Theory and Methods.
Time series, classic on frequency domain: Brillinger, David. 2001. Time Series Data Analysis and Theory.
Society for Industrial and Applied Math.
Time series, finance: Campbell, J.Y., A.W. Lo and C.McKinlay. 1996. The Econometrics
of Financial Markets. Princeton University Press.
Time series, finanace: Cochrane, J. 2005. Asset Pricing. Princeton University Press.
General web texts/notes
There are lots of good (and even more bad) web resources.
In general, econphd.net is
might be worth a look. As for econometrics, the following are good:
Jonthan Wright's course and notes.
Dan McFadden: stat. tools and econometrics
Jim Stock and Mark Watson gave a great NBER minicourse in 2008 on `What's new in econometrics--Time Series,'
Bruce Hansen: Econometrics text
Guido Imbens: Prob. and Stats.
History of econometrics
Haavelmo, T. 1944. The Probability Approach in Econometrics,
Econometrica. Supplement, July. 12:1.
Heckman, J. 1992. Haavelmo and the birth of modern econometrics. A review of
“;The history of econometric ideas”; by Mary Martin. Journal of Economic
Hendry, D.F., Monetary Economic Myth and Econometric Reality,
Oxford Review of Economic Policy, vol. 1 no. 1, Spring 1985, pp. 72--84.
King, Robert, Quantitative Theory and Econometrics, Federal Reserve Bank
of Richmond Economic Quaterly, Summer 1995, pp.53--105.
Koopmans, T. 1953. Identification Problems in Economic Model Construction.
Studies in Econometric Method, (eds.) W.C. Hood and T.C.
Koopmans. New York: Wiley.
Leamer, E. 1983. Let's Take the Con Out of Econometrics
American Economic Review, 73:1, March, pp. 31--43
Lucas, R.E. 1979. Econometric Policy Evaluation: A Critique
Carnegie-Rochester Conference Series on Public Policy, 1976.
Sims, Christopher, Macroeconomics and Reality Econometrica,
vol. 48, iss. 1, 1980, pp.1--48. [download]
Faust, Jon, The new macro models: washing our hands and watching for icebergs, Riksbank Economic Review,
Faust, Jon. DSGE Models: I Smell a Rat (and It Smells Good). International Journal of Central Banking, March 2012.
Comments: I am fond of those last two, but they are certainly not classics. They are my attempt to distill and
apply the wisdom of the earlier papers in the context of DSGE modelling. The others are all mandatory reading
if you want to understand applied macro. Everyone should read the Haavelmo piece once in their
career, but …. If you read one short bit of history, Heckman's review of Mary Morgan's excellent
book gives a great summary.
King makes the case for quantitative macro instead of
macroeconometrics in a thoughtful and constructive way: if you are thinking
of dropping the class, this'll give you an excuse.
You really must read Lucas (1979) and Sims (1980). My papers listed at the
end give my spin on these topics.
There are a few good sets of lecture notes on the web. I recommend
the GMM chapter in Hansen's web text as a starting point.
As usual, McFadden's notes are clear and thorough.
The notes by Guido Imbens at Berkeley are very nice and give a nice
starting point in terms of applications.
Bruce Hansen: Econometrics text
Guido Imbens notes
Dan McFadden notes
A classic: Large Sample Properties of Generalized Method of Moments Estimators,
Lars Peter Hansen,
Econometrica, Vol. 50, No. 4. (Jul., 1982), pp. 1029-1054.
Nice background: Bruce E. Hansen and Kenneth D. West, "Generalized Method of Moments and Macroeconomics"
Journal of Business and Economic Statistics, 2002 , [download]
Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators,
Peter Hall; Joel L. Horowitz, Econometrica, Vol. 64, No. 4. (Jul., 1996), pp. 891-916.
Relevant sample size issues: JBES Special Section on Small-Sample Properties of Generalized Method of Moments (GMM)
Classic asset pricing application
The application: Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models
Lars Peter Hansen; Kenneth J. Singleton
Econometrica, Vol. 50, No. 5. (Sep., 1982), pp. 1269-1286.
Cautionary tale: On tests of representative consumer asset pricing models
Narayana R. Kocherlakota
Journal of Monetary Economics
Volume 26, Issue 2 , October 1990, Pages 285-304
Greater detail: Finite-Sample Properties of Some Alternative GMM Estimators
Lars Peter Hansen; John Heaton; Amir Yaron
Journal of Business & Economic Statistics, Vol. 14, No. 3. (Jul., 1996), pp. 262-280.
SMM: Simulated Moments Estimation of Markov Models of Asset Prices
Darrell Duffie; Kenneth J. Singleton
Econometrica, Vol. 61, No. 4. (Jul., 1993), pp. 929-952.
Two nice pedagogical exercises regarding moment and weight matrix selection
GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
Torben G. Andersen; Bent E. Sørensen
Journal of Business & Economic Statistics, Vol. 14, No. 3. (Jul., 1996), pp. 328-352.
Efficient Prediction of Excess Returns; Faust, Jon; Jonathan H. Wright; Review of Economics and Statistics, May 2011, 93:2, 647--659.
A macro example about optimal instruments involving the NKPC
Estimating Forward-Looking Euler Equations with GMM and Maximum Likelihood Estimators: An Optimal Instruments Approach,
Jeff Fuhrer and Giovanni Olivei,
Models and Monetary Policy: Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley; Faust, Orphanides, and Reifschneider, eds.
Karl Whelan's notes on the New Keynesian Phillips Curve
give a nice background regarding the issues discussed by
Fuhrer and Olivei. [download]
The main paper starting this discussion: Inflation dynamics: a structural econometric analysis, Jordi Gali and Mark Gertler,
Journal of Monetary Economics,44, 1999, pp. 195-222. [download]
Any good text will provide adequate background. Jonathan's notes and Hansen's web
text are nice on this.
Herman Bierens's notes
Good paper on inherent conflicts raised by model selection: Yang, (2005, Biometrika): Can the strengths of AIC and BIC be shared? A conflict between model indentification and regression estimation
Memorable, especially for the sensible perspective in the conclusion: Amemiya,Selection of Regressors,International Economic Review, Vol. 21, No. 2 (Jun., 1980), pp. 331-354
Around the late 1980s, when unit root discussions were beginning to dominate the profession, there
was an extended and mostly misguided discussion of whether shocks to growth in GDP tended to be offset as time goes by, and if
so, how quickly. In the data at that time, the SAMPLE autocorrelation function of quarterly US GDP growth had
a couple of strongly positive autocorrelations at the first few lags and then later came several very small negative
autocorrelations. Of course, negative autocorrelation is a symptom of earlier shocks being offset. The debate
became one about whether these negative autocorrelations were statistically significant. If one used a criterion
that strongly favored parsimony to select the AR model, the estimated model would not show these negative autocorrelations.
If one used an ARMA specification and a less parsimonious criterion, these negatvies did come through.
The Campbell and Mankiw paper is written by a couple of the smartest guys around, and has a very clear discussion of
model selection and estimation in the ARMA context. A very good read. Indeed, an entire traditional graduate
time series course is embedded in their exposition. Read this carefully. Ultimately, they were misguided, in my
view. The Gagnon paper does a nice job of explaining why. Two of my early papers were motivated by this
debate, which was raging while I was a grad. student. These papers are not important but might hint at a research
agenda. These two papers were motivated by asking two simple questions of the work in the literature:
What is the worst case scenario for the methods folks are using? (See the confidence level zero paper.) When would
the methods folks are using be optimal? (See the variance ratio test paper.) The lesson I took: stick with simple
but fundamental questions.
Campbell, J.Y. and N.G. Mankiw (1987): Are Output Fluctuations
Transitory?, Quarterly Journal of Economics, 102, pp.857-880.
Gagnon, Joeseph, Short-Run Models and Long-Run Forecasts: A
Note on the Permanence of Output Fluctuations The Quarterly Journal of
Economics, Vol. 103, No. 2 (May, 1988), pp. 415-424
When Are Variance Ratio Tests for Serial Dependence Optimal?; Faust, Jon; Econometrica, September 1992, v. 60, iss. 5, pp. 1215-26.
Conventional Confidence Intervals for Points on Spectrum Have Confidence Level Zero; Faust, Jon; Econometrica, May 1999, v. 67, iss. 3, pp. 629-37.
has a good short introduction.
Chapter 52, Handbook of Econometrics, 2001, vol. 5, pp 3159-3228. Excellent.
Books, general references
The Jackknife, the Bootstrap, and Other Resampling Plans, is a classic.
Efron and Tibshirani, An introduction to the bootstrap
The bootstrap and edgeworth expansion.
Time series issues in particular
Berkowitz, J. and Lutz Kilian, Recent Developments in Bootstrapping Time Series
Econometric Reviews (January 2000). download from Jeremy
Finite-Sample Properties of Percentile and Percentile-t Bootstrap Confidence Intervals for Impulse Responses,
The Review of Economics and Statistics, Vol. 81, No. 4. (Nov., 1999), pp. 652-660.
Small-Sample Confidence Intervals for Impulse Response Functions, The Review of Economics and Statistics, Vol. 80, No. 2. (May, 1998), pp. 218-230.
Chris Sims; Tao Zha,
Error Bands for Impulse Responses, Econometrica, 67(5), 1999, pp. 1113-1155. This is not
exactly on the bootstrap but sheds a good deal of light on related issues
regarding impulse response inference.
Testing for breaks: theory and practice
McConnell and Perez-Quiros did a nice job
highlighting for the profession what is now called the great moderation
and you should probably
read it. Of course, we will be focussing on changing co-movement, as opposed to
a fall in variability.
My 2005 paper with Brian Doyle came out recently
and provides a decent summary of the issues and earlier work on co-movement. Stock and Watson, as always, do a thorough and
interesting job on the issue.
You should read Hansen's paper, which is a nontechnical
introduction to testing for breaks. The Andrews, and Andrews and Ploberger,
papers provide key technical results in this area, but we won't get
far into the technical details.
Donald W. K. Andrews.
Tests for Parameter Instability and Structural Change With Unknown Change Point.
Econometrica, Vol. 61, No. 4 (Jul., 1993) , pp. 821-856
Donald W. K. Andrews, Werner Ploberger.
Optimal Tests when a Nuisance Parameter is Present Only Under the Alternative.
Econometrica, Vol. 62, No. 6 (Nov., 1994) , pp. 1383-1414
Doyle, B. and J. Faust. 2002.
``An Investigation of Co-movement Among the Growth Rates of the G-7 Countries,''
Federal Reserve Bulletin, vol. 88, pp. 427-437.
Doyle, B., and Faust J. 2005. Breaks in the variability and co-movement of G-7 economic growth; Doyle, Brian M.; Faust, Jon; Review of Economics and Statistics, 7(4), Nov. 721-740.
Hansen, Bruce. 2001. The New Econometrics of Structural Change: Dating Changes in U.S. Labor Productivity. Journal of Economic Perspectives, 15, 117-128.
International Monetary Fund. 2001. Business Cycle Linkages
Among Major Advanced Economies, in World Economic Outlook
October, pp. 65-79
McConnell, M.M. and G. Perez-Quiros. 2000. Output Fluctuations in
the United States: What Has Changed Since the Early 1980s?, American
Economic Review, 90, pp.1464-1476.
Romer, C.D. 1986. Is Stabilization of the Postwar Economy A
Figment of the Data? American Economic Review, 76, pp.314-334.
Stock, James and Mark Watson . 2002. ``Has the Business Cycle
Changed and Why?'' NBER Macroeconomics Annual 2002. MIT Press.
Stock, James and Mark Watson. 2003. Has the Business Cycle
Changed? Evidence and Explanations.
Monetary Policy and Uncertainty: Adapting to a Changing Economy.
Proceedings of Jackson Hole Symposium. Federal Reserve Bank of Kansas City.