Christopher A. Sims

INTERPRETING THE MACROECONOMIC TIME SERIES FACTS: THE EFFECTS OF MONETARY POLICY

Existing theory and evidence on the effects of monetary policy are reviewed. Substantial room for disagreement among economists remains. New evidence, based on multivariate time series studies of several countries, is presented. While certain patterns in the data consistent with effective monetary policy are strikingly similar across countries, others, particularly the tendency of interest rate increases to predict high inflation, are harder to reconcile with effective monetary policy.

Implications of rational inattention

Abstract A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like familiar dynamic rational expectations theories to suggest that it might be useful and practical, while the implications for policy are different enough to be interesting.

Forecasting and conditional projection using realistic prior distributions

This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. The procedure is applied to ten macroeconomic variables and is shown to improve out-of-sample forecasts relative to univariate equations. Although cross-variables responses are damped by the prior, considerable interaction among the variables is shown to be captured by the estimates.We provide unconditional forecasts as of 1982:12 and 1983:3.We also describe how a model such as this can be used to make conditional projections and to analyze policy alternatives. As an example, we analyze a Congressional Budget Office forecast made in 1982:12.While no automatic causal interpretations arise from models like ours, they provide a detailed characterization of the dynamic statistical interdependence of a set of economic variables, which may help inevaluating causal hypotheses, without containing any such hypotheses themselves.

Solving Linear Rational Expectations Models

We describe methods for solving general linear rational expectations models in continuous or discrete timing with or without exogenous variables. The methods are based on matrix eigenvalue decompositions.

What Does Monetary Policy Do

This paper uses a single time frame and data set to present and analyze the results that have emerged from the recent empirical literature on the effects of monetary policy. It uses statistical methods that allow the analysis of larger models than appear previously in this literature. Monetary policy actions are shown to be largely systematic responses to the state of the economy. Consequently, there is more uncertainty about the effects of monetary policy than might be thought on the basis of simple graphical or narrative approaches to assessing the evidence. JEL Classifications: E3, E4, E5  1996 by THE BROOKINGS INSTITUTION. This document may be freely reproduced for educational and research purposes provided that i) this copyright notice is included with each copy, ii) no changes are made in the document, and iii) copies are not sold, but retained for individual use or distributed free. 1 Indiana University, Yale University, and Federal Reserve Bank of Atlanta, respectively. A draft of this paper is available by ftp from ftp://ftp.econ.yale.edu/pub/sims/bpea or by http from http://ezinfo.ucs.indiana.edu/~eleeper/home.htm. The authors would like to acknowledge what they have learned about the implementation of monetary policy from conversations with Lois Berthaume, Will Roberds, and Mary Rosenbaum of the Atlanta Fed, Charles Steindel of the New York Fed, Marvin Goodfriend of the Richmond Fed, and Sheila Tschinkel. David Petersen of the Atlanta Fed helped both in locating data and in discussions of the operation of the money markets.

Error bands for impulse responses

We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating error bands on impulse responses in dynamic linear models. The Bayesian intervals have a firmer theoretical foundation in small samples, are easier to compute, and are about as good in small samples by classical criteria as are the best bootstrap intervals. Bootstrap intervals based directly on the simulated small-sample distribution of an estimator, without bias correction, perform very badly. We show that a method that has been used to extend to the overidentified case standard algorithms for Bayesian intervals in reduced form models is incorrect, and we show how to obtain correct Bayesian intervals for this case.

Bayesian Methods for Dynamic Multivariate Models

If dynamic multivariate models are to be used to guide decisionmaking, it is important that probability assessments of forecasts or policy projections be provided. When identified Bayesian vector autoregression (VAR) models are presented with error bands in the existing literature, both conceptual and numerical problems have not been dealt with in an internally consistent way. In this paper, the authors develop methods to introduce prior information in both reduced-form and structural VAR models without introducing substantial new computational burdens. Their approach makes it feasible to use a single, large dynamic framework (for example, twenty-variable models) for tasks of policy projections. Copyright 1998 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Policy Analysis with Econometric Models

RECENTLY the rational expectations school has mounted an attack on the conventional use of simultaneous equations models for policy analysis. One might go further and say that among academic macroeconomists the conventional methods have not just been attacked, they have been discredited. The practice of using econometric models to project the likely effects of different policy choices, then choosing the best from among the projected outcomes, is widely believed to be unjustifiable or even the primary source of recent problems of combined high inflation and low economic activity. Instead, it is claimed, policy analysis should be formulated as choice among rules of behavior for the policy authorities and estimates should be made of the stochastic properties of the economy under each proposed rule to choose the best. This point of view has gained such wide acceptance in part because of its association with Lucass theoretical demonstration that a Phillips curve could emerge in an economy in which such an association between inflation and real activity was not a usable menu for policy choice. Because users of conventional simultaneous equations models sometimes presented the Phillips curve as just such a menu, and because it became apparent in the 1970s that this menu was not helpful, an analysis that provided a cogent explanation for why the menu was chimerical had great appeal. As in most revolutions, the old regime toppled by the rational expectations revolution was corrupt and in some sense deserved its fate. However, as is often the case, the revolution itself has had its excesses, destroying or discarding much that was valuable in the name of utopian

DOES MONETARY POLICY GENERATE RECESSIONS

The issue of uncovering the effects of monetary policy is far short of resolution. In the identified VAR literature, restrictions have been imposed to identify the effects of unpredictable monetary policy disturbances. We offer critical views on the unreasonable assumptions in the existing work and argue for careful economic argument about identifying assumptions. We display a structural stochastic equilibrium model in which our VAR identification would produce correct results while drawing attention to the serious lack of time series fit in most of the DSGE literature.

Bayesian skepticism on unit root econometrics

This paper examines several grounds for doubting the value of much of the special attention recently devoted to unit root econometrics. Unit root hypotheses are less well connected to economic theory than is often suggested or assumed; distribution theory for tests of other hypotheses in models containing unit roots are less often affected by the presence of unit roots than has been widely recognized; and the Bayesian inferential theory for dynamic models is largely unaffected by the presence of unit roots. The paper displays an example to show that when Bayesian probability statements and classical marginal significance levels diverge as they do for unit root models, the marginal significance levels are misleading. The paper shows how to carry out Bayesian inference when discrete weight is given to the unit root null hypothesis in a univariate model.