شناسایی شوک سیاست پولی و تجدید نظر داده شده

Monetary policy research using time-series methods has been criticized for using more information than the Federal Reserve had available. To quantify the role of this criticism, we estimate VARs with real-time data while accounting for the latent nature of many economic variables, such as output. Our estimated monetary policy shocks are closely correlated with typically estimated measures. The impulse response functions are broadly similar across estimation methods. Our evidence suggests that the use of revised data in VAR analyses of monetary policy shocks may not be a serious limitation for recursively identified systems, but presents more challenges for simultaneous systems.

Empirical research with vector autoregressions (VARs) typically ignores issues associated with data revisions and economic agents’ access to only real-time data releases. An example of this is the literature on monetary policy shocks in VARs (for example, Bernanke and Blinder, 1992, Sims, 1992, Christiano et al., 1996, Christiano et al., 1999, Sims and Zha, 1996 and Bernanke and Mihov, 1998). Each of these studies is based upon some data series that were not known to anyone during the period of the empirical analysis. Specifically, the data used in these studies, as well as virtually all other macroeconomic time-series research, have been revised relative to the data known at that time. Since government agencies and private sources do not provide these data conveniently, these shortcuts are rarely questioned.1 The real-time data collected by Croushore and Stark (2001), however, allow researchers to explore the empirical robustness of many existing macroeconomic results to this issue. Armed with the original data releases that were known at that time to business analysts, market participants, policymakers, and the rest of the interested universe, the econometrician can answer the question, how much of a difference does this make to empirical analyses of monetary policy shocks? Addressing this question is complicated by the fact that some data are always revised, and hence the true underlying economic concept is never observed fully. For example, aggregate economic activity in the United States is not directly observable, but data on real GDP are reported and revised by the Bureau of Economic Analysis. The monetary policy shock literature has focused on how real GDP , for example, is affected by an exogenous shock to monetary policy. This is an interesting question when real GDPGDP is taken to be an accurate measure of aggregate economic activity, but the focus should instead be on the impact of monetary policy shocks on economic activity. Consequently, when data revisions are accounted for in empirical VAR analyses, the unobserved true variable must be modeled. 2 In standard OLS estimates of autoregressions, this will induce errors-in-variables biases. Errors-in-variables issues raise another econometric problem for identified VAR analyses, not simply the literature on monetary policy. Structural shocks are identified based upon the covariance structure of the VAR innovations. The standard method of estimating VAR innovations from the residuals, however, will include data revisions (or measurement noises). In general, the revision components will be correlated across the equations in the system. Identifying the economic shocks from the measurement noises requires more structure on the measurement process. In our empirical example, conditional on having the complete data set, the identification and estimation of the monetary policy equation is simpler than for other equations because the policy instrument is set based on observable data. This paper considers two approaches to addressing the fact that econometricians’ macroeconomic data sets are changing over time because of data revisions. The first approach is to assess the sensitivity of VAR estimates across different data vintages. For example, how do monetary policy reaction function estimates change when the sample period is fixed at 1960–1983, but the data are drawn from different vintages, with different base years, or different methodologies (for example, some vintages that use fixed-weighted data and others that use chain-weighted data)? A strength of this vintage robustness analysis is that it corresponds to typical analyses within the literature. However, this approach does not explicitly consider how the data revision process takes place, side-stepping a true real-time analysis. Our second approach considers a statistical model of data revisions and implements an alternative, real-time estimation strategy to overcome the errors-in-variables biases. Our method assumes that output, the price level, and monetary aggregates are latent variables that the data collection agency never measures precisely. Given a standard set of restrictions to identify policy and non policy shocks in the absence of measurement noises, our analysis with these noises is able to identify the shocks and compute impulse responses. Our empirical analysis of the recursively identified Christiano et al. (1996) system suggests that many results from the VAR literature on monetary policy are robust to these issues of real-time data availability. Specifically, our analysis of the 1960–1983 estimation period using alternative data vintages (Section 3) uncovers only minor differences in monetary policy shock measures and impulse responses. Our real-time analysis of the 1968–1991 period (Section 5) also finds only small differences in the estimated policy shocks between the real-time estimates and 1998-vintage estimates. The estimated effects of monetary policy shocks on variables in the system are somewhat smaller in the real-time system, but qualitatively are remarkably similar. The estimated effects of other orthogonalized shocks are also similar in the real-time system for the first three to five years of responses. After this length of time, however, the price variables in the real-time system exhibit trending behavior, while the 1998-vintage responses seem to revert to zero. So, estimated impulse responses may be sensitive to data revisions. Our analysis of Galí's (1992) identification strategy indicates that real-time data issues present more difficulties in fully simultaneous VAR systems. When monetary policy and financial market data respond to data revisions, the Galí IS, monetary policy, and money demand shocks are not identified separately from the data revisions without additional restrictions. Galí's Supply shock is identified by long-run restrictions, and this identification is not affected by the transitory noise in data revisions. Our estimated impulse response functions following a Supply shock are qualitatively similar across both the real-time and a 1998-vintage system. The paper is organized as follows. Section 2 discusses the relationship between the VAR literature on monetary policy and real-time information sets. Section 3 investigates the robustness of two VAR studies to using alternative data vintages in the estimation over the period 1960–1983. Section 4 discusses difficulties raised by real-time data issues in an example, two-variable autoregression, and proposes an estimation strategy. Section 5 reports empirical results for this method applied to the Christiano et al. (1996) system. Section 6 examines the difficulties of identification in the nonrecursive system of Galí (1992). Section 7 relates our findings to other studies. Section 8 concludes.

Empirical VAR and time series research often ignores issues associated with data revisions and economic agents’ access to only real-time data releases. Since government agencies and private sources do not provide these data conveniently, these shortcuts are rarely questioned. The real-time data collected by Croushore and Stark (2001) allows researchers to explore the empirical robustness of many existing macroeconomic results to this issue, but additional structure must be placed on the data revision process and assumptions regarding the information that economic agents have access to. Our empirical analyses indicate that accounting for data revisions has only a modest effect quantitatively on the recursively identified monetary policy shock measures and impulse responses we consider. Similarly robust findings were obtained for a particular long-run identification. All of these results are conditional on our assumptions about data revisions and the latent structure of the economy. A negative finding of this analysis revealed that many fully-simultaneous VAR systems that are identified when real-time data issues are ignored are actually not completely identified when vintage measurement issues are considered. Further research that allows for alternative measurement noise and data revision processes is needed to shed more light on the role of data revisions.