Search Results
Working Paper
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 ...
Journal Article
Are forecasting models usable for policy analysis?
In this article, Christopher A. Sims argues the answer to his title is yes. Sims explains that any decisionmaking model must incorporate some identifying assumptions to enable it to forecast the effects of alternative decisions. He argues that although all identifying assumptions in econometric policymaking models are of uncertain validity, those incorporated in vector autoregression (VAR) forecasting models have the advantage of allowing their uncertainty to be measured. Sims concludes by demonstrating a method for identifying a small macroeconomic VAR model so that it can be used to analyze ...
Conference Paper
Inflation expectations, uncertainty, the Phillips curve, and monetary policy
As with many important theories, the long run value of Phillips curve theories may lie in the new flames that are emerging from its dying embers.
Report
Forecasting and conditional projection using realistic prior distribution
This paper develops a forecasting procedure based on a Bayesian method for estimating vector autoregressions. We apply the procedure to 10 macroeconomic variables and show that it produces more accurate out-of-sample forecasts than univariate equations do. Although cross-variable responses are damped by the prior, our estimates capture considerable interaction among the variables. ; 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 analyze policy alternatives. As an example, we analyze a ...
Working Paper
Bayesian methods for dynamic multivariate models
If multivariate dynamic models are to be used to guide decision-making, it is important that it be possible to provide probability assessments of their results. Bayesian VAR models in the existing literature have not commonly (in fact, not at all as far as we know) been presented with error bands around forecasts or policy projections based on the posterior distribution. In this paper we show that it is possible to introduce prior information in both reduced form and structural VAR models without introducing substantial new computational burdens. With our approach, identified VAR analysis of ...
Discussion Paper
Modeling trends
Models of low-frequency behavior of time series may have strongly conflicting substantive implications while fitting the data nearly equally well. We should develop methods which display the resulting uncertainty rather than adopt modeling conventions which hide it. One step toward this goal may be to consider overparameterized stationary ARMA models.
Conference Paper
Fiscal consequences for Mexico of adopting the dollar
Discussion Paper
Understanding unit rooters: a helicopter tour