Real-time forecasting in practice: the U.S. treasury staff's real-time GDP forecast system

Business Economics, Oct, 2003 by John Kitchen, Ralph Monaco

This paper outlines a method for making effective use of monthly indicators to develop a current-quarter GDP forecast. Estimates and projections of real GDP growth are usually used to describe how the economy is doing. But estimates of GDP are only available quarterly, and the first GDP estimate for a quarter is released late in the month following the end of the quarter. The lack of a timely, comprehensive economic picture may mean that policymakers and business planners may be as much as four months behind in recognizing a significant slowdown or acceleration in the economy. This problem is especially important around business cycle peaks or troughs, where there may be some evidence that the economy is changing direction.

There are many less-comprehensive, but higher-frequency data series about the economy, however. The chief difficulty with using the multiple indicators is that different indicators can give different signals, and there is no agreed-upon way for aggregating the statistics to give a single-valued answer.

In this paper, we describe the approach we have adopted at the Treasury Department to use a broad variety of high-frequency incoming data to construct "real-time" estimates of quarterly real GDP growth. We draw on the recent work by Stock and Watson and others and describe the indicators, the techniques, and the recent performance of the system.

Policymakers and economists often turn to real GDP growth to assess how the economy is performing since GDP is a well-known, comprehensive measure that covers the economy as a whole, rather than a single sector or market (however broad) like manufacturing or employment. Further, GDP growth is a key variable in many policy or strategy analyses. For example, at the Treasury Department, projections of nominal GDP serve as the basis for projections of taxable incomes and the resulting projections of government tax receipts. The GDP metric enjoys wide recognition and usage as the nation's economic barometer.

In practice, however, a significant problem arises because GDP estimates are not timely enough for many needs. GDP estimates are only available quarterly, and the Bureau of Economic Analysis' (BEA) first estimate of GDP for a quarter is released late in the month following the end of the quarter. For policymakers as well as business planners, the lack of a timely, comprehensive picture of the economy can present a critical problem: they may be as much as four months behind in recognizing a significant slowdown or acceleration in the economy. Such a lag in timely information is an important part of the "recognition lag" that economists have identified as a major impediment to the successful implementation of discretionary counter-cyclical policies. This problem is especially important around business cycle peaks or troughs, where there may be some evidence that the economy is changing direction. (1)

At the other extreme, high-frequency data about specific industries and markets abound and these can be--and are--often used to inform judgments about the economy's current performance or where it may be headed. A wide variety of monthly, weekly, daily--even intradaily--data are available to alert policymakers and analysts to changes in the course of the economy. Various problems arise in attempting to use such high-frequency data, however. Such data are inherently "noisy," and it is often difficult to identify the underlying "signal" information. In addition, the very multiplicity of the data--although providing additional potential sources of information--presents a problem for analysts. The chief difficulty with using multiple indicators is that they can, and usually do, provide conflicting signals; and there is no agreed-upon way for aggregating the statistics to give a single-valued answer. For example, it is difficult to decide how to "add up" the Bureau of the Census' housing starts and the Institute of Supply Management Purchasing Managers Index (PMI) to give a single-valued answer. Without some way to aggregate these pieces into a consistent picture, it is often difficult to separate the signal in the statistics from the very short-run noise.

In this paper, we describe the approach we have adopted at the U.S. Treasury to use the broad variety of incoming data to construct "real-time" estimates of quarterly real GDP growth. For us, "real time" refers to the effort to conduct continuous, contemporaneous analyses of incoming information that allow forecasters to make continual and instantaneous updates to their forecasts as new data become available. The real time forecasting system (RTFS) is the result of our efforts to produce a fluid, data-based forecast of contemporaneous real GDP growth that is subject to continual updating the instant new data become available.

An alternative strand of the "real-time" data and analysis literature has received much attention in recent years in economics research. This research has focused on the important issue of how using contemporaneous "vintage" data for historical sample periods in empirical analysis can yield different estimation results than the "last-available vintage" data that are typically used (see, for example, Croushore and Stark (1999, 2001) and Orphanides (2001)). (2) The sensitivity of observed historical relationships to the vintage of the data used potentially can affect the ability to forecast (e.g., Dynan and Elmendorf (2001), Koenig, Dolmas, and Piger (2001)). While we believe this is an important issue, in this paper we focus on the rationale for a coherent forecasting system and our efforts to construct one. Now that the system is largely in place, we look forward to conducting future research that examines the sensitivity of near-term projections to the use of alternative data vintages for estimating the system.