Person:

Stock, James

This 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.

Angrist 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.

We 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.

We 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.

This chapter assesses forecasts constructed using dynamic factor models for their reliability in the face of structural breaks. Dynamic factor models have had notable empirical forecasting successes, but there has been little work to date on the performance of factor-based macroeconomic forecasts under structural instability. In factor models, even if factor loadings are unstable, if the instability is sufficiently independent across series then the estimated factors could be well estimated even if individual relations between the observable series and the factors are unstable. This chapter first lays out the implications for forecasting of different types of structural instability in dynamic factor models, provides a new empirical investigation (using US data for 144 quarterly macroeconomic time series) of factor-based forecasting with potential instability, and investigates separately the effects of structural change on the estimation of the factors and on the use of those factors for forecasting.

The business cycle analysis of Burns and Mitchell and the National Bureau of Economic Research presumed that aggregate economic variables evolve on a time scale defined by business cycle turning points rather than by months or quarters. Do macroeconomic variables appear to evolve on an economic rather than a calendar time scale? Evidence presented here suggests that they do. However, the estimated economic time scales are only weakly related to business cycle time scales, providing evidence against the view underlying traditional business cycle analysis.

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.

The 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.

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.