Abstract: In this paper, we examine the aggregate data on after-tax corporate profits in the national income and product accounts. We look at real-time data on both domestic corporate profits and national corporate profits and how they have changed over time, focusing on benchmark revisions that have substantially changed the measures. (Note: Domestic profits are those earned by U.S. and foreign firms from their U.S. operations, while national profits are those earned by U.S. firms anywhere in the world.) We also compare the overall measures of corporate profits to the measures after adjusting for capital consumption and inventory valuation, which is the preferred economic measure of profits. We will test whether the early releases of the growth rate of real national and domestic corporate profits, both before and after adjustment, are efficient forecasts of later releases. We will illustrate the four different concepts of profits and explore the reasons for the very large revisions to the data, especially every five years during benchmark revisions of the National Income and Product Accounts.

Keywords: Corporate Profits, Data Revisions, Forecasting, Real-Time Data.

Abstract: We review the 25-year history of conferences on real-time data analysis, illustrating the contributions made to the economics literature

Keywords: Real-Time Data.

Abstract: The National Income and Product Accounts (NIPAs) produced by the U.S. Bureau of Economic Analysis (BEA) provide key measures of U.S. economic activity, including gross domestic product (GDP) and gross domestic income (GDI). Although conceptually equivalent, GDP and GDI often diverge due to differences in source data and revision timing. This study focuses on corporate profits, a small but volatile component of GDI that is frequently revised, especially during the BEA’s annual (A1) and benchmark (C1) revisions. I examine how revisions to corporate profits influence GDI revisions and whether they can improve real-time estimates of GDI. First, I document the size and timing of corporate profit revisions, which are consistently larger than those of other GDI components. Second, I find that while corporate profits make up only about 6% of GDI, they account for almost twice of its revisions. Third, regression analysis shows that corporate profit revisions, especially from the A1 vintage, help predict final GDI more effectively than earlier estimates. Lastly, I compare GDI, GDP, and corporate profit vintages, finding that A1 is the most informative for forecasting final GDI. These results highlight the outsized role of corporate profits in driving GDI revisions and the value of tracking revision patterns over time. Understanding these dynamics can improve real-time forecasting and help bridge gaps between GDP and GDI, especially during periods of economic uncertainty.

Keywords: GDP, GDI, Corporate Profits, Data Revisions, Forecasting, Real-Time Data.