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Author:McCracken, Michael W. 

Working Paper
Binary Conditional Forecasts

While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR [Dueker (2005)], whose joint VAR-probit structure allows us to form conditional forecasts of the latent variable which can then be used to form probabilistic forecasts of the binary variable. We apply ...
Working Papers , Paper 2019-29

Journal Article
A Macroeconomic News Index for Constructing Nowcasts of U.S. Real Gross Domestic Product Growth

Analyzing the performance of the economy in real time is a challenge for those who must forecast macroeconomic variables such as inflation or employment. A key aspect of this challenge is evaluating the incoming flow of information contained in economic announcements. In this article, the authors develop a simple-to-read index of these announcements that they then use to construct nowcasts. The index tracks whether key economic data have come in stronger, weaker, or as expected during the current quarter relative to a baseline consensus forecast. Specifically, the data releases are weighted ...
Review , Volume 98 , Issue 4 , Pages 277-296

Working Paper
Averaging forecasts from VARs with uncertain instabilities

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combining various models of instability in improving VAR forecasts made with ...
Working Papers , Paper 2008-030

Working Paper
Averaging forecasts from VARs with uncertain instabilities

A body of recent work suggests commonly?used VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. These methods include using different approaches to lag selection, different observation windows for estimation, (over-) differencing, intercept correction, stochastically time?varying parameters, break dating, discounted least squares, Bayesian shrinkage, and detrending of inflation and interest rates. Although each ...
Research Working Paper , Paper RWP 06-12

Working Paper
In-sample tests of predictive ability: a new approach

This paper presents analytical, Monte Carlo, and empirical evidence linking in-sample tests of predictive content and out-of-sample forecast accuracy. Our approach focuses on the negative effect that finite-sample estimation error has on forecast accuracy despite the presence of significant population-level predictive content. Specifically, we derive simple-to-use in-sample tests that test not only whether a particular variable has predictive content but also whether this content is estimated precisely enough to improve forecast accuracy. Our tests are asymptotically non-central chi-square or ...
Research Working Paper , Paper RWP 09-10

Working Paper
Combining forecasts from nested models

Motivated by the common finding that linear autoregressive models forecast better than models that incorporate additional information, this paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining forecasts from nested models. In our analytics, the unrestricted model is true, but as the sample size grows, the DGP converges to the restricted model. This approach captures the practical reality that the predictive content of variables of interest is often low. We derive MSE-minimizing weights for combining the restricted and unrestricted forecasts. In the ...
Research Working Paper , Paper RWP 06-02

Working Paper
Binary Conditional Forecasts

While conditional forecasting has become prevalent both in the academic literature and in practice (e.g., bank stress testing, scenario forecasting), its applications typically focus on continuous variables. In this paper, we merge elements from the literature on the construction and implementation of conditional forecasts with the literature on forecasting binary variables. We use the Qual-VAR [Dueker (2005)], whose joint VAR-probit structure allows us to form conditional forecasts of the latent variable which can then be used to form probabilistic forecasts of the binary variable. We apply ...
Working Papers , Paper 2019-029

Journal Article
Should food be excluded from core CPI?

The greater a component?s SNR, the more useful the component should be in forecasting headline CPI.
Economic Synopses

Working Paper
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors

We estimate uncertainty measures for point forecasts obtained from survey data, pooling information embedded in observed forecast errors for different forecast horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon specification of stochastic volatility. We apply our method to forecasts for various macroeconomic variables from the Survey of Professional Forecasters. Compared to constant variance approaches, our stochastic volatility model improves the accuracy of uncertainty measures for survey forecasts. Our method can also be applied to ...
Working Papers , Paper 17-15R

Working Paper
Improving forecast accuracy by combining recursive and rolling forecasts

This paper presents analytical, Monte Carlo, and empirical evidence on the effectiveness of combining recursive and rolling forecasts when linear predictive models are subject to structural change. We first provide a characterization of the bias-variance tradeoff faced when choosing between either the recursive and rolling schemes or a scalar convex combination of the two. From that, we derive pointwise optimal, time-varying and data-dependent observation windows and combining weights designed to minimize mean square forecast error. We then proceed to consider other methods of forecast ...
Research Working Paper , Paper RWP 04-10

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