Forecasting through the rear-view mirror: data revisions and bond return predictability
Real-time macroeconomic data reflect the information available to market participants, whereas final data?containing revisions and released with a delay?overstate the information set available to them. We document that the in-sample and out-of-sample Treasury return predictability is significantly diminished when real-time as opposed to revised macroeconomic data are used. In fact, much of the predictive information in macroeconomic time series is due to the data revision and publication lag components.
Real-time inflation forecasting in a changing world
This paper revisits the accuracy of inflation forecasting using activity and expectations variables. We apply Bayesian-model averaging across different regression specifications selected from a set of potential predictors that includes lagged values of inflation, a host of real activity data, term structure data, nominal data, and surveys. In this model average, we can entertain different channels of structural instability by incorporating stochastic breaks in the regression parameters of each individual specification within this average, allowing for breaks in the error variance of the ...
The mismeasured personal saving rate is still useful: using real-time data to improve forecasting
People make decisions based on information. Often, with hindsight, they could have made better choices. Economics faces a similar problem: Economic data, when first released, are often inaccurate and may subsequently be revised. In "The Mismeasured Personal Saving Rate Is Still Useful: Using Real-Time Data to Improve Forecasting," Leonard Nakamura uses the U.S. personal saving rate - a statistic that has often been initially low, then substantially revised upward - to discuss how modern economic statistical techniques can improve forecasting.
Tests of equal predictive ability with real-time data
This paper examines the asymptotic and finite-sample properties of tests of equal forecast accuracy applied to direct, multi-step predictions from both non-nested and nested linear regression models. In contrast to earlier work in the literature, our asymptotics take account of the real-time, revised nature of the data. Monte Carlo simulations indicate that our asymptotic approximations yield reasonable size and power properties in most circumstances. The paper concludes with an examination of the real-time predictive content of various measures of economic activity for inflation.
Real-time forecast averaging with ALFRED
This paper presents empirical evidence on the efficacy of forecast averaging using the ALFRED real-time database. We consider averages taken over a variety of different bivariate VAR models that are distinguished from one another based upon at least one of the following: which variables are used as predictors, the number of lags, using all available data or data after the Great Moderation, the observation window used to estimate the model parameters and construct averaging weights, and for forecast horizons greater than one, whether or not iterated- or direct-multistep methods are used. A ...
Forecasting Economic Activity with Mixed Frequency Bayesian VARs
Mixed frequency Bayesian vector autoregressions (MF-BVARs) allow forecasters to incorporate a large number of mixed frequency indicators into forecasts of economic activity. This paper evaluates the forecast performance of MF-BVARs relative to surveys of professional forecasters and investigates the influence of certain specification choices on this performance. We leverage a novel real-time dataset to conduct an out-of-sample forecasting exercise for U.S. real gross domestic product (GDP). MF-BVARs are shown to provide an attractive alternative to surveys of professional forecasters for ...
Forecasting of small macroeconomic VARs in the presence of instabilities
Small-scale VARs have come to be widely used in macroeconomics, for purposes ranging from forecasting output, prices, and interest rates to modeling expectations formation in theoretical models. However, a body of recent work suggests such VAR models 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, observation windows for estimation, (over-) differencing, intercept correction, stochastically ...
Implications of real-time data for forecasting and modeling expectations
This note extends the analysis in Stark and Croushore (2001) with an emphasis on the importance of data vintage for survey forecasts and modeling expectations. For both of these types of empirical exercises, results suggest that the choice of latest available or real-time data is critical for variables subject to large level revisions, but almost irrelevant for variables subject to only small revisions. Other forecasting practices were examined, with some surprising results.
Season’s greetings and seasonal adjustments
Have you every wondered what the term "seasonally adjusted" means in relation to economic data? What does the change of seasons have to do with economics? The January 2009 Newsletter explains the term and shows its effect on economic data.
Economic data—appearances can be deceiving