Publications & Working Papers

2005
Stock J, Yogo M. Testing for Weak Instruments in Linear IV Regression. In: Andrews DWK Identification and Inference for Econometric Models. New York: Cambridge University Press ; 2005. pp. 80-108. Website PDF
2004
Stock J. Structural Stability and Models of the Business Cycle. 152. 2004;2 :197-209. PDF
Stock J, Watson M. Combination Forecasts of Output Growth in a Seven-Country Data Set. Journal of Forecasting. 2004;23 :405-430.Abstract

This paper uses forecast combination methods to forecast output growth in a
seven-country quarterly economic data set covering 1959 – 1999, with up to 73 predictors
per country. Although the forecasts based on individual predictors are unstable over time
and across countries, and on average perform worse than an autoregressive benchmark,
the combination forecasts often improve upon autoregressive forecasts. Despite the
unstable performance of the constituent forecasts, the most successful combination
forecasts, like the mean, are the least sensitive to the recent performance of the individual
forecasts. While consistent with other evidence on the success of simple combination
forecasts, this finding is difficult to explain using the theory of combination forecasting in
a stationary environment.

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Stock J, Hausman J, Yogo M. Asymptotic Properties of the Hahn-Hausman Test for Weak Instruments. Economics Letters. 2004;89 :333-342. PDF
2003
Marcellino M, Stock J, Watson M. Macroeconomic Forecasting in the Euro Area: Country Specific Versus Area-Wide Information. European Economic Review. 2003;47 :1-18. PDF
Stock J, Watson M. Has the Business Cycle Changed? Evidence and Explanations. 2003. PDF
Introduction to Econometrics
Stock J, Watson MW. Introduction to Econometrics. New York: Prentice Hall; 2003. Ch 1, 2, 3 Slides.doc Syllabus.doc Ch 4 Slides.doc Ch 5 Slides.doc Ch 6 Slides.doc Ch 7 Slides.doc Ch 8 Slides.doc Ch 9 Slides.doc Ch 10 Slides.doc Ch 11 Slides.doc Ch 12 Slides.doc Ch 13 Slides.doc
Stock J, Watson M. How Did Leading Indicator Forecasts Do During the 2001 Recession?. Economic Quarterly. 2003;89 (3) :71-90.Abstract

The 2001 recession differed from other recent recessions in its cause, severity, and scope.
This paper documents the performance of professional forecasters and forecasts based on
leading indicators as the recession unfolded. Professional forecasters found this recession
a difficult one to forecast. A few leading indicators (stock prices, term spreads,
unemployment claims) predicted that growth would slow, but none predicted the sharp
economic slowdown. Several previously reliable leading indicators (housing starts,
orders for new capital equipment, consumer sentiment) provided no early warning
signals. When combined, the leading indicator performed somewhat better than a
benchmark autoregressive forecasting model.

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Stock J, Watson M. Understanding Changes in International Business Cycle Dynamics. 2003. WebsiteAbstract

The volatility of economic activity in most G7 economies has moderated over the past 40 years.
Also, despite large increases in trade and openness, G7 business cycles have not become more
synchronized. After documenting these facts, we interpret G7 output data using a structural VAR
that separately identifies common international shocks, the domestic effects of spillovers from
foreign idiosyncratic shocks, and the effects of domestic idiosyncratic shocks. This analysis
suggests that, with the exception of Japan, a significant portion of the widespread reduction in
volatility is associated with a reduction in the magnitude of the common international shocks.
Had the common international shocks in the 1980s and 1990s been as large as they were in the
1960s and 1970s, G7 business cycles would have been substantially more volatile and more
highly synchronized than they actually were. (JEL: C3, E5)

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Stock J. The Econometric Analysis of Business Cycles. Medium Econometrisch Toepassingen. 2003;11 (1) :23-26. PDF
Stock J, Watson M. Forecasting Output and Inflation: The Role of Asset Prices. Journal of Economic Literature. 2003;41 :788-829. PDF
Stock J. Who Invented Instrumental Variable Regression?. Journal of Economic Perspectives. 2003;17 :177-197. PDF
Stock J, Kaufmann RA. Testing Hypotheses about Mechanisms for the Unknown Carbon Sink: A Time Series Analysis. Global Biogeochemical Cycles. 2003;17 (1072) :1-15. PDF
2002
Stock J, Watson MW. Macroeconomic Forecasting Using Diffusion Indexes. Journal of Business and Economic Statistics. 2002;20 (2) :147–162. PDF
Stock J, Watson MW. Forecasting Using Principal Components from a Large Number of Predictors. Journal of the American Statistical Association. 2002;97 :1167–1179. PDF
Onatski A, Stock J. Robust Monetary Policy Under Model Uncertainty in a Small Model of the U.S. Economy. Macroeconomic Dynamics. 2002;6 :85-110. PDF
Stock J. Instrumental Variables in Economics and Statistics. In: International Encyclopedia of the Social Sciences . Amsterdam: Elsevier ; 2002. pp. 7577-7582. Website PDF
Stock J. Time Series: Economic Forecasting. In: International Encyclopedia of the Social Sciences. Amsterdam: Elsevier ; 2002. pp. 15721-15724. Website PDF
Stock J, Watson M. Has the Business Cycle Changed and Why?. NBER Macroeconomics Annual. 2002 :159-230. PDF
Stock J, Yogo M, Wright J. A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments. Journal of Business and Economic Statistics. 2002;20 :518 – 529.Abstract

Weak instruments arise when the instruments in linear instrumental variables (IV) regression are weakly
correlated with the included endogenous variables. In generalized method of moments (GMM), more
generally, weak instruments correspond to weak identification of some or all of the unknown parameters.
Weak identification leads to GMM statistics with nonnormal distributions, even in large samples, so that
conventional IV or GMM inferences are misleading. Fortunately, various procedures are now available
for detecting and handling weak instruments in the linear IV model and, to a lesser degree, in nonlinear
GMM.

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