A summary of the conference on real-time data analysis.

The conference focused on five topics: data revisions, forecasting, policy analysis, financial research, and macroeconomic research. In "A Summary of the Conference on Real-Time Data Analysis," Tom Stark reviews the papers presented at the conferenceMacroeconomics

Macroeconomic forecasts and microeconomic forecasters in the Survey of Professional Forecasters

Do professional forecasters distort their reported forecasts in a way that compromises accuracy? New research in the theory of forecasting suggests such a possibility. In a recent paper, Owen Lamont finds that forecasters in the Business Week survey make more radical forecasts as they gain experience. In this paper, the authors uses forecasts from the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters to test the robustness of Lamont's results. The author's results contradict Lamont's. However, careful examination of a methodological difference in the two surveys suggests a more general theory of forecasting that accounts for both sets of results.Forecasting

A Bayesian vector error corrections model of the U.S. economy

This paper presents a small-scale macroeconometric time-series model that can be used to generate short-term forecasts for U.S. output, inflation, and the rate of unemployment. Drawing on both the Bayesian VAR and vector error corrections (VEC) literature, the author specifies the baseline model as a Bayesian VEC. The author documents the model's forecasting ability over various periods, examines its impulse responses, and considers several reasonable alternative specifications. Based on a root-mean-square-error criterion, the baseline model works best, and the author concludes that this model holds promise as a workhorse forecasting tool.Forecasting ; Time-series analysis

Realistic evaluation of real-time forecasts in the Survey of Professional Forecasters

Economic indicators

A real-time data set for macroeconomists

This paper presents the concept and uses of a real-time data set that can be used by economists for testing the robustness of published econometric results, for analyzing policy, and for forecasting. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, examines key empirical papers in macroeconomics and investigates their robustness to different vintages, looks at how policy analysis may be affected by data revisions, and shows how forecasts can be affected by data revisions.Forecasting ; Macroeconomics

Forecasting with a real-time data set for macroeconomists

This paper discusses how forecasts are affected by the use of real-time data rather than latest-available data. The key issue is this: In the literature on developing forecasting models, new models are put together based on the results they yield using the data set available to the model’s developer. But those are not the data that were available to a forecaster in real time. How much difference does the vintage of the data make for such forecasts? The authors explore this issue with a variety of exercises designed to answer this question. In particular, they find that the use of real-time data matters for some forecasting issues but not for others. It matters for choosing lag length in a univariate context. Preliminary evidence suggests that the span—or number—of forecast observations used to evaluate models may also be critical: the authors find that standard measures of forecast accuracy can be vintage-sensitive when constructed on the short spans (five years of quarterly data) of data sometimes used by researchers for forecast evaluation. The differences between using real-time and latest-available data may depend on what is being used as the “actual” or realization, and we explore several alternatives that can be used. Perhaps of most importance, we show that measures of forecast error, such as root-mean-squared error and mean absolute error, can be deceptively lower when using latest-available data rather than real-time data. Thus, for purposes such as modeling expectations or evaluating forecast errors of survey data, the use of latest-available data is questionable; comparisons between the forecasts generated from new models and benchmark forecasts, generated in real time, should be based on real-time data.Forecasting

A real-time data set for macroeconomists: does data vintage matter for forecasting?

This paper describes a real-time data set for macroeconomists that can be used for a variety of purposes, including forecast evaluation. The data set consists of quarterly vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, and provides an example showing how data revisions can affect forecasts.Forecasting

Forecasting coin demand.

Shortages of coins in 1999 and 2000 motivated the authors to develop models for forecasting coin demand. A variety of models were developed, tested, and used in realtime forecasting. This paper describes the models that were developed and examines the forecast errors from the models both in quasi-ex-ante forecasting exercises and in realtime use. Tests for forecast efficiency are run on each model. Real-time forecasts are examined. The authors conclude with suggestions for further refinements of the models.Coinage

Is macroeconomic research robust to alternative data sets?

This paper uses a real-time data set to analyze data revisions and to test the robustness of published econometric results. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, examines the properties of several of the variables in the data set across vintages, and examines key empirical papers in macroeconomics, investigating their robustness to different vintages.Macroeconomics

Benchmark revisions and the U.S. personal saving rate.

Initially published estimates of the personal saving rate from 1965 Q3 to 1999 Q2, which averaged 5.3 percent, have been revised up 2.8 percentage points to 8.1 percent, as we document. We show that much of the initial variation in the personal saving rate across time was meaningless noise. Nominal disposable personal income has been revised upward an average of 8.4 percent: one dollar in 12 was originally missing! We use both conventional and real-time estimates of the personal saving rate to forecast real disposable income, gross domestic product, and personal consumption and show that the personal saving rate in real-time almost invariably makes forecasts worse. Thus, while the personal saving rate may have some forecasting power once we know the true saving rate, as Campbell (1987) and Ireland (1995) have argued, as a practical matter it is useless to forecasters.Saving and investment