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Author:Stark, Tom 

Journal Article
A summary of the conference on real-time data analysis.

The conference focused on five topics: data revisions, forecasting, policy analysis, financial research, and macroeconomic research. In "A Summary of the Conference on Real-Time Data Analysis," Tom Stark reviews the papers presented at the conference
Business Review , Issue Q 1 , Pages 5-11

Journal Article
Activist monetary policy for good or evil? The new Keynesians vs. the new Classicals

Business Review , Issue Mar , Pages 17-25

Journal Article
The relationship between capacity utilization and inflation

There's a common belief among economists that when there?s slack in the economy ? that is, when labor and capital are not fully employed ? the economy can expand without an increase in inflation. One measure of the intensity with which labor and capital are used in producing output is the capacity utilization rate. According to some economists, when capacity utilization is low, firms can increase employment and their use of capital without incurring large increases in the costs of production. So firms will not be forced to raise prices in order to make profits on additional output. But this ...
Business Review , Issue Q2 , Pages 8-17

Journal Article
Evaluating McCallum's rule for monetary policy

Business Review , Issue Jan , Pages 3-14

Working Paper
The effects of permanent and transitory output shocks on poverty

Working Papers , Paper 96-20

Working Paper
Evaluating McCallum's rule for monetary policy

Working Papers , Paper 94-26

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 ...
Working Papers , Paper 98-12

Working Paper
Benchmark revisions and the U.S. personal saving rate.

Initially published estimates of the personal saving rate from 1965 Q3 to 1999 Q2, which averaged 5.3 percent, have been revised up 2.8 percentage points to 8.1 percent, as we document. We show that much of the initial variation in the personal saving rate across time was meaningless noise. Nominal disposable personal income has been revised upward an average of 8.4 percent: one dollar in 12 was originally missing! We use both conventional and real-time estimates of the personal saving rate to forecast real disposable income, gross domestic product, and personal consumption and show that the ...
Working Papers , Paper 05-6

Working Paper
A real-time data set for marcoeconomists: does the data vintage matter?

This paper presents a real-time data set that can be used by economists for testing the robustness of published econometric results, for analyzing policy, and for forecasting. The data set consists of vintages, or snapshots, of the major macroeconomic data available at quarterly intervals in real time. The paper illustrates why such data may matter, explains the construction of the data set, examines the properties of several of the variables in the data set across vintages, and examines key empirical papers in macroeconomics, investigating their robustness to different vintages.
Working Papers , Paper 99-21

Working Paper
A real-time data set for macroeconomists: does data vintage matter for forecasting?

This paper describes a real-time data set for macroeconomists that can be used for a variety of purposes, including forecast evaluation. The data set consists of quarterly 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 provides an example showing how data revisions can affect forecasts.
Working Papers , Paper 00-6

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