Dean Croushore

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 with a Real-Time Data Set for Macroeconomists

This paper discusses how forecasts may be 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 developer. But those arent the data that were available to a forecaster in real time. How much difference does the vintage of the data make for such forecasts? We explore this issue with a variety of exercises designed to answer this question. In particular, we find that real-time data matters for some forecasting issues but not for others. It matters for choosing lag length in a univariate context. It may matter considerably for a short-horizon forecast, though is less important for longer-horizon forecasts. Preliminary evidence suggests that the span--or number--of forecast observations used to evaluate models may also be critical: we find that standard measures of forecast accuracy can be vintage-sensitive when constructed on the short spans (5 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 whats 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, developing a model using latest-available data is questionable; model development may be much better if its based on real-time data.

Frontiers of Real-Time Data Analysis

This paper describes the existing research (as of February 2008) on real-time data analysis, divided into five areas: (1) data revisions; (2) forecasting; (3) monetary policy analysis; (4) macroeconomic research; and (5) current analysis of business and financial conditions. In each area, substantial progress has been made in recent years, with researchers gaining insight into the impact of data revisions. In addition, substantial progress has been made in developing better real-time data sets around the world. Still, additional research is needed in key areas, and research to date has uncovered even more fruitful areas worth exploring.

Forecasting with Real-Time Macroeconomic Data

Forecasts are only as good as the data behind them. But macroeconomic data are revised, often significantly, as time passes and new source data become available and conceptual changes are made. How is forecasting influenced by the fact that data are revised? To answer this question, we begin with the example of the index of leading economic indicators to illustrate the real-time data issues. Then we look at the data that have been developed for U.S. data revisions, called the Real-Time Data Set for Macroeconomists and show their basic features, illustrating the magnitude of the revisions and thus motivating their potential influence on forecasts and on forecasting models. The data set consists of a set of data vintages, where a data vintage refers to a date at which someone observes a time series of data; so the data vintage September 1974 refers to all the macroeconomic time series available to someone in September 1974. Next, we examine experiments using that data set by Stark and Croushore (2002), Journal of Macroeconomics 24, 507-531, to illustrate how the data revisions could have affected reasonable univariate forecasts. In doing so, we tackle the issues of what variables are used as actuals in evaluating forecasts and we examine the techniques of repeated observation forecasting, illustrate the differences in U.S. data of forecasting with real-time data as opposed to latest-available data, and examine the sensitivity to data revisions of model selection governed by various information criteria. Third, we look at the economic literature on the extent to which data revisions affect forecasts, including discussions of how forecasts differ when using first-available compared with latest-available data, whether these effects are bigger or smaller depending on whether a variable is being forecast in levels or growth rates, how much influence data revisions have on model selection and specification, and evidence on the predictive content of variables when subject to revision. Given that data are subject to revision and that data revisions influence forecasts, what should forecasters do? Optimally, forecasters should account for data revisions in developing their forecasting models. We examine various techniques for doing so, including state-space methods. The focus throughout this chapter is on papers mainly concerned with model development - trying to build a better forecasting model, especially by comparing forecasts from a new model to other models or to forecasts made in real time by private-sector or government forecasters.

Do Consumer Confidence Indexes Help Forecast Consumer Spending in Real Time

Could a researcher or policy analyst use data reported from surveys of consumer confidence to improve forecasts of consumer spending? This issue has been examined in the literature previously, which reached the conclusion that consumer confidence helped improve the forecasts slightly. But that research was based on final, revised data and thus did not use the data that would have been available to forecasters in real time. This paper remedies that shortcoming, using the Real-Time Data Set for Macroeconomists to analyze the quality of forecasts made with indexes of consumer confidence. The main finding is that the indexes of consumer confidence are not of significant value in forecasting consumer spending. In fact, in some cases, they make the forecasts significantly worse.

Data Revisions and the Identification of Monetary Policy Shocks

Monetary policy research using time series methods has been criticized for using more information than the Federal Reserve had available in setting policy. To quantify the role of this criticism, we propose a method to estimate a VAR with real-time data while accounting for the latent nature of many economic variables, such as output. Our estimated monetary policy shocks are closely correlated with a typically estimated measure. The impulse response functions are broadly similar across the methods. Our evidence suggests that the use of revised data in VAR analyses of monetary policy shocks may not be a serious limitation.

Money in the utility function: Functional equivalence to a shopping-time model

Abstract Does the money-in-the-utility-function (MUF) model provide an adequate micro-foundation for monetary theory? Feenstra (1986) argues that it does by showing an equivalence between the MUF model and other microfoundations-of-money models. This paper raises two difficulties with Feenstras approach. It then uses his analysis to show that a shopping-time model of money is equivalent to an MUF model and overcomes these two objections.

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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 models 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? We explore this issue with a variety of exercises designed to answer this question. In particular, we 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: we 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.

Revisions to PCE Inflation Measures: Implications for Monetary Policy

This paper examines the characteristics of the revisions to the inflation rate as measured by the personal consumption expenditures price index both including and excluding food and energy prices. These data series play a major role in the Federal Reserve’s analysis of inflation. ; The author examines the magnitude and patterns of revisions to both PCE inflation rates. The first question he poses is: What do data revisions look like? The author runs a variety of tests to see if the data revisions have desirable or exploitable properties. The second question he poses is related to the first: Can we forecast data revisions in real time? The answer is that it is possible to forecast revisions from the initial release to August of the following year. Generally, the initial release of inflation is too low and is likely to be revised up. Policymakers should account for this predictability in setting monetary policy.

The marginal cost of funds with nonseparable public spending

This article provides new calculations of the welfare effects of fiscal changes when the publicly provided good is nonseparable in utility and production so that it affects economic agents marginal decisions. The authors results show that these nonseparabilities significantly alter the marginal cost of funds (MCF) that previous studies have calculated. The authors also report estimates of the nonseparable marginal benefits (NSMB) associated with aggregate government purchases. The net marginal cost offunds (NMCF ), which is equal to MCF - NSMB, is in general positive over a wide range of parameter values that encompass empirically relevant specifications. Thus the nonseparable benefits by themselves are not sufficient for a marginal increase in aggregate government purchases of goods and services to be worthwhile.