Person: Stock, James
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Stock, James
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Publication Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve(American Economic Association, 2014) Mavroeidis, Sophocles; Plagborg-Moller, Mikkel; Stock, JamesWe review the main identification strategies and empirical evidence on the role of expectations in the New Keynesian Phillips curve, paying particular attention to the issue of weak identification. Our goal is to provide a clear understanding of the role of expectations that integrates across the different papers and specifications in the literature. We discuss the properties of the various limited-information econometric methods used in the literature and provide explanations of why they produce conflicting results. Using a common dataset and a flexible empirical approach, we find that researchers are faced with substantial specification uncertainty, as different combinations of various a priori reasonable specification choices give rise to a vast set of point estimates. Moreover, given a specification, estimation is subject to considerable sampling uncertainty due to weak identification. We highlight the assumptions that seem to matter most for identification and the configuration of point estimates. We conclude that the literature has reached a limit on how much can be learned about the New Keynesian Phillips curve from aggregate macroeconomic time series. New identification approaches and new datasets are needed to reach an empirical consensus.Publication Dynamic Factor Models(Oxford University Press, 2011) Stock, James; Watson, MarkThis article surveys work on a class of models, dynamic factor models (DFMs), that has received considerable attention in the past decade because of their ability to model simultaneously and consistently data sets in which the number of series exceeds the number of time series observations. The aim of this survey is to describe the key theoretical results, applications, and empirical findings in the recent literature on DFMs. The article is organized as follows. The first issue at hand for the econometrician is to estimate the factors and to ascertain how many factors there are; these two topics are covered in Sections 2 and 3. Once one has reliable estimates of the factors, there are a number of things one can do with them beyond using them for forecasting, including using them as instrumental variables, estimating factor-augmented vector autoregressions, and estimating dynamic stochastic general equilibrium models; these applications are covered in Section 4. Section 5 discusses some extensions.Publication Reconciling anthropogenic climate change with observed temperature 1998–2008(Proceedings of the National Academy of Sciences, 2011) Kaufmann, R. K.; Kauppi, H.; Mann, M. L.; Stock, JamesGiven the widely noted increase in the warming effects of rising greenhouse gas concentrations, it has been unclear why global surface temperatures did not rise between 1998 and 2008. We find that this hiatus in warming coincides with a period of little increase in the sum of anthropogenic and natural forcings. Declining solar insolation as part of a normal eleven-year cycle, and a cyclical change from an El Nino to a La Nina dominate our measure of anthropogenic effects because rapid growth in short-lived sulfur emissions partially offsets rising greenhouse gas concentrations. As such, we find that recent global temperature records are consistent with the existing understanding of the relationship among global surface temperature, internal variability, and radiative forcing, which includes anthropogenic factors with well known warming and cooling effects.Publication Consistent Factor Estimation in Dynamic Factor Models with Structural Instability(Elsevier BV, 2013) Bates, Brandon J.; Plagborg-Møller, Mikkel; Stock, James; Watson, Mark W.This paper considers the estimation of approximate dynamic factor models when there is temporal instability in the factor loadings. We characterize the type and magnitude of instabilities under which the principal components estimator of the factors is consistent, and find that these instabilities can be larger than earlier theoretical calculations suggest. We further characterize the rate of convergence of the estimated factors as a function of the magnitude of the time variation in the factor loadings for general types of parameter instability, and provide numerical evidence that this consistency rate is tight in the special case of random walk parameter variation. We also discuss implications of these results for the robustness of regressions based on the estimated factors and of estimates of the number of factors in the presence of parameter instability.Publication Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression(The Econometric Society, 2008) Stock, James; Watson, MarkThe conventional heteroskedasticity-robust (HR) variance matrix estimator for cross-sectional regression (with or without a degrees-of-freedom adjustment), applied to the fixed-effects estimator for panel data with serially uncorrelated errors, is inconsistent if the number of time periods T is fixed (and greater than 2) as the number of entities n increases. We provide a bias-adjusted HR estimator that is √nT-consistent under any sequences (n T ) in which n and/or T increase to ∞. This estimator can be extended to handle serial correlation of fixed order.Publication Estimating turning points using large data sets(Elsevier BV, 2014) Stock, James; Watson, Mark W.Dating business cycles entails ascertaining economy-wide turning points. Broadly speaking, there are two approaches in the literature. The first approach, which dates to Burns and Mitchell (1946), is to identify turning points individually in a large number of series, then to look for a common date that could be called an aggregate turning point. The second approach, which has been the focus of more recent academic and applied work, is to look for turning points in a few, or just one, aggregate. This paper examines these two approaches to the identification of turning points. We provide a nonparametric definition of a turning point (an estimand) based on a population of time series. This leads to estimators of turning points, sampling distributions, and standard errors for turning points based on a sample of series. We consider both simple random sampling and stratified sampling. The empirical part of the analysis is based on a data set of 270 disaggregated monthly real economic time series for the US, 1959–2010.Publication The Evolution of National and Regional Factors in U.S. Housing Construction(Oxford University Press, 2008) Stock, James; Watson, MarkThis paper presents and describes a newly available data set on monthly building permits for U.S. states from 1969-2007. These data are used to estimate regions of common housing construction activity. Building permits exhibit substantial comovement across states, and these comovements are modeled as being associated with a national factor, a regional factor, and a state-specific disturbance. When stochastic volatility is added to this state building permit dynamic factor model, the decline in the volatility in state permits is found to be associated with a sharp decline in the mid-1980s in the volatility of the national factor and with a slow, steady decline in the volatility of the state-specific component, with these two sources contributing approximately equally for a typical state. The timing of the sharp reduction in volatility of the national component coincides with break dates previously identified for the Great Moderation in U.S. economic activity.Publication Does temperature contain a stochastic trend? Evaluating conflicting statistical results(Springer Science + Business Media, 2009) Kaufmann, Robert K.; Kauppi, Heikki; Stock, JamesWe evaluate the claim by Gay et al. (Clim Change 94:333–349, 2009) that “surface temperature can be better described as a trend stationary process with a one-time permanent shock” than efforts by Kaufmann et al. (Clim Change 77:249–278, 2006) to model surface temperature as a time series that contains a stochastic trend that is imparted by the time series for radiative forcing. We test this claim by comparing the in-sample forecast generated by the trend stationary model with a one-time permanent shock to the in-sample forecast generated by a cointegration/error correction model that is assumed to be stable over the 1870– 2000 sample period. Results indicate that the in-sample forecast generated by the cointegration/error correction model is more accurate than the in-sample forecast generated by the trend stationary model with a one-time permanent shock. Furthermore, Monte Carlo simulations of the cointegration/error correction model generate time series for temperature that are consistent with the trend-stationary-with-a-break result generated by Gay et al. (Clim Change 94:333–349, 2009), while the time series for radiative forcing cannot be modeled as trend stationary with a one-time shock. Based on these results, we argue that modeling surface temperature as a time series that shares a stochastic trend with radiative forcing offers the possibility of greater insights regarding the potential causes of climate change and efforts to slow its progression.Publication Indicators for Dating Business Cycles: Cross-History Selection and Comparisons(American Economic Association, 2010) Stock, James; Watson, Mark WPublication The Other Transformation in Econometric Practice: Robust Tools for Inference(American Economic Association, 2010) Stock, JamesAngrist and Pischke highlight one aspect of the research that has positively transformed econometric practice and teaching. They emphasize the rise of experiments and quasi-experiments as credible sources of identification in microeconometric studies, which they usefully term "design-based research." But in so doing, they miss an important part of the story: a second research strand aimed at developing tools for inference that are robust to subsidiary modeling assumptions. My first aim in these remarks therefore is to highlight some key developments in this area. I then turn to Angrist and Pischke's call for adopting experiments and quasi-experiments in macroeconometrics; while sympathetic, I suspect the scope for such studies is limited. I conclude with some observations on the current debate about whether experimental methods have gone too far in abandoning economic theory.