Search Results
Working Paper
Reconsidering the Fed’s Forecasting Advantage
Previous studies show the Fed has a forecast advantage over the private sector, either because it devotes more resources to forecasting or because it has an informational advantage in knowing the path of future monetary policy. We evaluate the Fed’s forecast advantage to determine how much of it results from the Fed’s knowledge of the conditioning path. We develop two tests—an instrumental variable encompassing test and a path-dependent encompassing test—to equalize the Fed’s information set with the private sector’s. We find that, generally, the Fed does not encompass the private ...
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.
Working Paper
Tests of Conditional Predictive Ability: Existence, Size, and Power
We investigate a test of conditional predictive ability described in Giacomini and White (2006; Econometrica). Our main goal is simply to demonstrate existence of the null hypothesis and, in doing so, clarify just how unlikely it is for this hypothesis to hold. We do so using a simple example of point forecasting under quadratic loss. We then provide simulation evidence on the size and power of the test. While the test can be accurately sized we find that power is typically low.
Working Paper
Evaluating Conditional Forecasts from Vector Autoregressions
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though conditional forecasting is common, there has been little work on methods for evaluating conditional forecasts. This paper provides analytical,Monte Carlo, and empirical evidence on tests of predictive ability for conditional forecasts from estimated models. In the empirical analysis, we consider forecasts of growth, unemployment, and inflation from a VAR, based on conditions on the short-term interest rate. Throughout ...
Journal Article
Real-time forecast averaging with ALFRED
This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED (ArchivaL Federal Reserve Economic Data) real-time database. We consider averages over a variety of bivariate vector autoregressive models. These models are distinguished from one another based on at least one of the following factors: (i) the choice of variables used as predictors, (ii) the number of lags, (iii) use of all available data or only data after the Great Moderation, (iv) the observation window used to estimate the model parameters and construct averaging weights, and (v) for the forecast ...
Working Paper
Forecast-based model selection in the presence of structural breaks
This paper presents analytical, Monte Carlo, and empirical evidence on the effects of structural breaks on tests for equal forecast accuracy and forecast encompassing. The forecasts are generated from two parametric, linear models that are nested under the null. The alternative hypotheses allow a causal relationship that is subject to breaks during the sample. With this framework, we show that in-sample explanatory power is readily found because the usual F-test will indicate causality if it existed for any portion of the sample. Out-of-sample predictive power can be harder to find because ...
Are Continued Jobless Claims a Useful Gauge of Labor Market Conditions?
Economists have used initial claims for jobless benefits to gauge U.S. labor market conditions. Can continued claims also be used as a predictor of employment growth?
The St. Louis Fed's Financial Stress Index, Version 2.0
The St. Louis Fed's financial stress index has been recalibrated to better capture evolving stresses in financial markets.
Working Paper
Nested forecast model comparisons: a new approach to testing equal accuracy
This paper develops bootstrap methods for testing whether, in a finite sample, competing out-of-sample forecasts from nested models are equally accurate. Most prior work on forecast tests for nested models has focused on a null hypothesis of equal accuracy in population basically, whether coefficients on the extra variables in the larger, nesting model are zero. We instead use an asymptotic approximation that treats the coefficients as non-zero but small, such that, in a finite sample, forecasts from the small model are expected to be as accurate as forecasts from the large model. Under that ...
Journal Article
How accurate are forecasts in a recession?