Academic journal article Journal of Money, Credit & Banking

Data Revisions Are Not Well Behaved

Academic journal article Journal of Money, Credit & Banking

Data Revisions Are Not Well Behaved

Article excerpt

MOST MACROECONOMIC VARIABLES are substantially revised by statistical agencies in the months after their initial announcements. These revisions generally reflect the arrival of new information that was not available at the time of the initial announcement. Users of data understand the uncertainty surrounding the initial announcement and make their decisions accordingly. If revisions are "well behaved," by which we loosely mean that they are rational forecast errors, then the arrival of a new revision is not relevant for them. In this paper, however, we will argue that revisions are not, in fact, "well behaved."

To facilitate the discussion, we will use the following notation. Let [y.sup.t+1.sub.t] denote a statistical agency's initial announcement of a variable that was realized at time t and [y.sup.f.sub.t] denote the final or true value of the same variable. The two objects will be related by the following identity

[y.sup.f.sub.t] [equivalent to] [y.sup.t+1.sub.t] + [r.sup.f.sub.t],

where [r.sup.f.sub.t] is the final revision that is potentially never observed.

From a statistical point of view, we expect the final revision to satisfy three properties in order to consider it well behaved. First, we expect its mean to be zero. This would imply that the initial announcement of the statistical agency is an unbiased estimate of the final value. Second, we expect the variance of the final revision to be small, compared to the variance of the final value. Finally, we expect the final revision to be unpredictable given the information set at the time of the initial announcement. When the final revision is predictable, the initial announcement of the statistical agency is not an optimal forecast of the final value and a better forecast, one with a lower forecast error variance, can be obtained. We summarize these three properties as follows:

(P1): E([r.sup.f.sub.t]) = 0

(P2): var([r.sup.f.sub.t]) is small

(P3): E([r.sup.f.sub.t] / [I.sub.t+1]) = O,

where [I.sub.t+1] is the information set at the time of the initial announcement. Our goal in this paper is to investigate the validity of these properties for revisions to some major macroeconomic variables in the United States.

We are certainly not the first to analyze the statistical properties of data revisions. Indeed, that macroeconomic data are revised is well understood by economists and various aspects of data revisions have been studied for decades. An important part of the literature considers the question we devote most of this paper to, the predictability of data revisions. Mankiw, Runkle, and Shapiro (1984) assess whether the preliminary announcements of money stock are rational forecasts of the final announcements (news hypothesis) or are observations of the revised series, measured with error (noise hypothesis). A similar analysis was applied to gross national product (GNP) data by Mankiw and Shapiro (1986, henceforth MS). The conclusion from these two studies is that while the revisions to GNP are news, those of money stock data are better characterized as noise. In other words, they find evidence of predictability for the revisions to the money stock data while revisions to GNP data seems to be unpredictable. Mork (1987) and Mork (1990) consider the same question and find predictability in both GNP and money stock revisions using a slightly different methodology.

In a recent paper Faust, Rogers, and Wright (2005) look at the revisions to the gross domestic product (GDP) growth rates for the G-7 countries and find that while for the United States, revisions are only slightly predictable, for Italy, Japan, and United Kingdom, about half the variability of subsequent revisions can be accounted for by information available at the time of the preliminary announcement by using methods similar to Mankiw, Runkle, and Shapiro (1984) and MS.

A recent paper by economists at the Bureau of Economic Analysis (BEA), Fixler and Grimm (2002), analyzes the reliability of National Income Product Account (NIPA) data for the period 1983-2000. …

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