Search Results
Working Paper
Evaluating the accuracy of forecasts from vector autoregressions
This paper surveys recent developments in the evaluation of point and density forecasts in the context of forecasts made by Vector Autoregressions. Specific emphasis is placed on highlighting those parts of the existing literature that are applicable to direct multi-step forecasts and those parts that are applicable to iterated multi-step forecasts. This literature includes advancements in the evaluation of forecasts in population (based on true, unknown model coefficients) and the evaluation of forecasts in the finite sample (based on estimated model coefficients). The paper then examines in ...
Working Paper
Consistent testing for structural change at the ends of the sample
In this paper we provide analytical and Monte Carlo evidence that Chow and Predictive tests can be consistent against alternatives that allow structural change to occur at either end of the sample. Attention is restricted to linear regression models that may have a break in the intercept. The results are based on a novel reparameterization of the actual and potential break point locations. Standard methods parameterize both of these locations as fixed fractions of the sample size. We parameterize these locations as more general integer valued functions. Power at the ends of the sample is ...
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 ...
Working Paper
Forecasting with small macroeconomic VARs in the presence of instabilities
Small-scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real time forecasting. We use forecasts from univariate time series models, the Survey of ...
What Do Components of Key Inflation Measures Say about Future Inflation?
A new analysis suggests that the food expenditures category of the consumer price index could be a useful signal of future headline inflation.
Working Paper
Advances in forecast evaluation
This paper surveys recent developments in the evaluation of point forecasts. Taking West?s (2006) survey as a starting point, we briefly cover the state of the literature as of the time of West?s writing. We then focus on recent developments, including advancements in the evaluation of forecasts at the population level (based on true, unknown model coefficients), the evaluation of forecasts in the finite sample (based on estimated model coefficients), and the evaluation of conditional versus unconditional forecasts. We present original results in a few subject areas: the optimization of power ...
Working Paper
Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR
We use a mixed-frequency vector autoregression to obtain intraquarter point and density forecasts as new, high frequency information becomes available. This model, delineated in Ghysels (2016), is specified at the lowest sampling frequency; high frequency observations are treated as different economic series occurring at the low frequency. As this type of data stacking results in a high-dimensional system, we rely on Bayesian shrinkage to mitigate parameter proliferation. We obtain high-frequency updates to forecasts by treating new data releases as conditioning information. The same ...
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.
Journal Article
Factor-based prediction of industry-wide bank stress
This article investigates the use of factor-based methods for predicting industry-wide bank stress. Specifically, using the variables detailed in the Federal Reserve Board of Governors? bank stress scenarios, the authors construct a small collection of distinct factors. We then investigate the predictive content of these factors for net charge-offs and net interest margins at the bank industry level. The authors find that the factors do have significant predictive content, both in and out of sample, for net interest margins but significantly less predictive content for net charge-offs. ...
Core Inflation Revisited: Forecast Accuracy across Horizons
How far out can you forecast inflation? This analysis examines the accuracy of core inflation in predicting headline inflation for periods ranging from three to 24 months in the future.