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The empirical properties of benchmark revisions to key US macroeconomic aggregates are examined. News versus noise impact of revisions is interpreted via the cointegration property of successive benchmark revisions. Cointegration breaks down in the last two years before a benchmark revision. Hence, we conclude that there is some information content in benchmark revisions. This last point is illustrated by reporting that inflation forecasts could be improved by the addition of a time series that reflects benchmark revisions to real GDP. Standard backward- and forward-looking Phillips curves are used to explore the statistical significance of benchmark revisions.

Weak-form rationality of fixed-event forecasts implies that forecast revisions should not be correlated. However, significant positive correlations between consecutive forecast revisions were found in most USDA forecasts for U.S. corn, soybeans, wheat, and cotton. This study developed a statistical procedure for correction of this inefficiency which takes into account the issue of outliers, the impact of forecast size and direction, and the stability of revision inefficiency. Findings suggest that the adjustment procedure has the highest potential for improving accuracy in corn, wheat, and cotton production forecasts.

Soon after beginning operations, the Federal Reserve established a nationwide network for collecting information about the economy. In 1919, the Fed began tabulating data by about retail sales, which it viewed as a fundamental measure of consumption. From 1920 until 1929, the Federal Reserve published data about retail sales each month by Federal Reserve district, but ceased to do so after 1929. It continued to compile monthly data on retail sales by reserve district, but this data remained in house. We collected these in-house reports from the archives of the Board of Governors and constructed a consistent series on retail trade at the district level. The new series enhances our understanding of economic trends during the Roaring ‘20s and Great Depression.

This paper discusses the nominal and real convergence regarding Greece being a country-member of the European Union (EU), and of the Economic and Monetary Union (EMU). We argued that nominal convergence is relative to Maastricht criteria when real convergence has been investigated through six different axes: (1) the five Maastricht Criteria, (2) the GDP per capita in PPP prices, (3) the real GDP growth rates, (4) the minimum wages, (5) the HDI index development, and (6) the unemployment rates. We concluded for the case of Greece that by utilizing alternative indicators, such as the Maastricht criteria, and the above criteria only nominal convergence exists while real convergence appears to be a long-term target with many obstacles. In particular, Greece has managed to achieve the criteria proposed by the EMU (Maastricht Criteria) for membership, decisively different levels of unemployment, wages, and GDP growth rate/GDP per capita in PPP prices, and different human development indexes appear for the case of Greece.

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.

This chapter examines the impacts of scheduled announcements of 14 widely followed macroeconomic news on the corporate bond market from July 2002 to June 2017 and documents several new findings. First, good (bad) macroeconomic news tends to have a negative (positive) effect on IG bond returns and a positive (negative) effect on high-yield (HY) bond returns. Second, nonfarm payroll (NFP) appears to be the “King of announcements” for the corporate bond market. Third, while information about revisions of prior releases is incorporated into bond prices on announcement days, future revisions fail to be priced in. Fourth, the news information is thoroughly and quickly reflected in bond prices on the announcement day. Finally, corporate bond volatility increases on announcement days, whereas the Zero Lower Bound (ZLB) policy has little effect on conditional volatility.

Nonfinancial information is becoming more readily available to investors, and thus, relative to annual financial reports, is having an increasing influence on investors' stock pricing decisions. Using Hogarth and Einhorn's (1992) belief-adjustment model, we examine how task familiarity (high, medium, and low) influences nonprofessional investor stock price decisions when these investors are presented with a stream of both positive and negative nonfinancial news. We find that task familiarity negatively correlates with reaction size for both positive and negative information, which creates arbitrage opportunities for those with more task familiarity. However, we find that assurance mitigates this effect, leveling the playing field for less task-familiar investors in most cases. These findings are important as the volume and variety of information types increase, and as more nonfinancial information enters the marketplace in discrete sound bites (e.g., social media, press releases, daily reports). Findings suggest that assurance is one way to lessen the biases exhibited by investors with less task familiarity. These results enhance our understanding of nonprofessional investor behavior through the lens of belief revision.

We conduct an experiment to analyze the impact of a well-established psychological construct, need for cognition, in an audit-related decision context. By simulating a basic audit sampling task, we determine whether the desire to engage in a cognitive process influences decisions made during that task. Specifically, we investigate whether an individual's need for cognition influences the quantity of data collected, the revision of a predetermined sampling plan, and the time taken to make a decision. Additionally, we examine the impact of cost constraints during the decision-making process.

Contrary to results in previous studies, we find those with a higher need for cognition sought less data than those with a lower need for cognition to make an audit sampling decision. In addition, we find that the need for cognition had no relationship to sampling plan revisions or the time needed to make an audit sampling decision. Previous studies regarding the need for cognition did not utilize incremental costs for additional decision-making information. Potentially, these costs provided cognitive challenges that influenced decision outcomes.

The problem of measurement errors in the national accounts has been recognized for a long time. The error chiefly arises from various source data and the timing of the flow of data received from providers. This chapter first discusses the type of measurement errors confronted by statistical agencies. Second, it presents a model of their behavior that illustrates the trade-offs that must be made in dealing with such errors. Third, the chapter discusses how the quality of the estimates can be gauged given measurement error and the inability to conduct standard statistical tests. Although the focus is on the production of U.S. Gross Domestic Product, the principles are applicable to all national statistical agencies.

In this volume, we have studied the cyclical behavior of numerous business cycle indicators from the U.S. transportation sector and studied how they are related to those of the overall economy. Our study began with the conceptualization of what constitutes the transportation services sector, identifying relevant monthly indicators from the private sector and the government, and finally putting them together to construct a monthly measure of output of the transportation services sector. The challenge was to develop an indicator that will be available promptly soon after a month with other widely reported monthly indicators such as the index of industrial production, Institute for Supply Management (ISM) surveys, CPI, index of leading indicators, etc. and is not subject to much data revisions. Since monthly activity measures of major transportation services sectors such as trucking and railroads are produced by private membership organizations, use of these data in the production of official statistics in the public sector needed skillful persuasion of government officials. Bureau of Transportation Statistics (BTS) releases the preliminary number for the latest month and replaces the number for the oldest preliminary month with a revised number. All other revisions are held until an annual comprehensive revision of the transportation services output (TSI). It is gratifying to see that the arrangement of cooperation between the transportation department and these private service organizations are working out seamlessly, and TSI continues to get the media attention.