Disagreement at the FOMC: the dissenting votes are just part of the story

Recently released data on economic forecasts made by voting and nonvoting members of the FOMC suggest that there is more disagreement than the voting record indicates.Federal Open Market Committee ; Monetary policy

Using stock market liquidity to forecast recessions

Market participants rebalance their portfolios in advance of a recession.Recessions ; Economic indicators

Uncertainty about when the Fed will raise interest rates

It's hard to make a firm prediction as to when the Fed will raise interest rates.Interest rates ; Monetary policy - United States

Housing's role in a recovery

Housing tends to contribute significantly to an economic recovery.Housing - Finance ; Economic conditions

Should food be excluded from core CPI?

The greater a component’s SNR, the more useful the component should be in forecasting headline CPI.Consumer price indexes ; Food prices

How accurate are forecasts in a recession?

Recessions ; Economic forecasting

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 individual method could be useful, the uncertainty inherent in any single representation of instability could mean that combining forecasts from the entire range of VAR estimates will further improve forecast accuracy. Focusing on models of U.S. output, prices, and interest rates, this paper examines the effectiveness of combination in improving VAR forecasts made with real–time data. The combinations include simple averages, medians, trimmed means, and a number of weighted combinations, based on: Bates-Granger regressions, factor model estimates, regressions involving just forecast quartiles, Bayesian model averaging, and predictive least squares–based weighting. Our goal is to identify those approaches that, in real time, yield the most accurate forecasts of these variables. We use forecasts from simple univariate time series models and the Survey of Professional Forecasters as benchmarks.Economic forecasting ; Vector autoregression

Combining forecasts from nested models

Motivated by the common finding that linear autoregressive models often 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 a subset of the coefficients are treated as being local-to-zero. 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. Monte Carlo and empirical analyses verify the practical e effectiveness of our combination approach.Econometric models ; Economic forecasting

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 real-time data.Econometric models ; Economic forecasting

Tests of Equal Forecast Accuracy and Encompassing for Nested Models

We examine the asymptotic and finite-sample properties of tests for equal forecast accuracy and encompassing applied to 1-step ahead forecasts from nested parametric models. We first derive the asymptotic distributions of two standard tests and one new test of encompassing. Tables of asymptotically valid critical values are provided. Monte Carlo methods are then used to evaluate the size and power of the tests of equal forecast accuracy and encompassing. The simulations indicate that post-sample tests can be reasonably well sized. Of the post-sample tests considered, the encompassing test proposed in this paper is the most powerful. We conclude with an empirical application regarding the predictive content of unemployment for inflation.