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Journal Article
Real GDP in annual revisions to the U.S. National accounts: 1966-2011
On July 29, 2011, the U.S. Bureau of Economic Analysis (BEA) released a flexible annual revision to the U.S. national income and product accounts. Real GDP growth was subject to large downward revisions. I use the Philadelphia Fed's real-time data set to compare the size of the recent revision with that of past annual revisions since 1966.
Working Paper
Evaluating McCallum's rule for monetary policy
Working Paper
A Bayesian vector error corrections model of the U.S. economy
This paper presents a small-scale macroeconometric time-series model that can be used to generate short-term forecasts for U.S. output, inflation, and the rate of unemployment. Drawing on both the Bayesian VAR and vector error corrections (VEC) literature, the author specifies the baseline model as a Bayesian VEC. The author documents the model's forecasting ability over various periods, examines its impulse responses, and considers several reasonable alternative specifications. Based on a root-mean-square-error criterion, the baseline model works best, and the author concludes that this ...
Journal Article
First quarters in the national income and product accounts
Prompted by their expectations of an initial estimate of a marked slowdown in U.S. real gross domestic product growth in the first quarter of 2015, commentators and analysts have drawn attention to an apparent ?first-quarter effect? in the U.S. national income and product accounts
Working Paper
Does data vintage matter for forecasting?
This paper illustrates the use of a real-time data set for forecasting. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, and shows how forecasts can be affected by data revisions.
Working Paper
Do Phillips curves conditionally help to forecast inflation?
The Phillips curve has long been used as a foundation for forecasting inflation. Yet numerous studies indicate that over the past 20 years or so, inflation forecasts based on the Phillips curve generally do not predict inflation any better than a univariate forecasting model. In this paper, the authors take a deeper look at the forecasting ability of Phillips curves from both an unconditional and a conditional view. Namely, they use the test results developed by Giacomini and White (2006) to examine the forecasting ability of Phillips curve models. The authors' main results indicate that ...
Working Paper
Do Phillips curves conditionally help to forecast inflation?
This paper reexamines the forecasting ability of Phillips curves from both an unconditional and conditional perspective by applying the method developed by Giacomini and White (2006). We find that forecasts from our Phillips curve models tend to be unconditionally inferior to those from our univariate forecasting models. We also find, however, that conditioning on the state of the economy sometimes does improve the performance of the Phillips curve model in a statistically significant manner. When we do find improvement, it is asymmetric -- Phillips curve forecasts tend to be more accurate ...
Working Paper
Macroeconomic forecasts and microeconomic forecasters in the Survey of Professional Forecasters
Do professional forecasters distort their reported forecasts in a way that compromises accuracy? New research in the theory of forecasting suggests such a possibility. In a recent paper, Owen Lamont finds that forecasters in the Business Week survey make more radical forecasts as they gain experience. In this paper, the authors uses forecasts from the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters to test the robustness of Lamont's results. The author's results contradict Lamont's. However, careful examination of a methodological difference in the two surveys ...