In the first chapter we document the empirical properties of revisions to major macroeconomic variables in the U.S., over the period 1966–2000. We find that these revisions do not have a zero mean, which indicates that the initial announcements by statistical agencies are biased. We also find that the revisions are quite large compared to the original variables. They are predictable using the information set at the time of the initial announcement, which means that the initial announcements of statistical agencies are not rational forecasts. We also provide some evidence that professional forecasters ignore this predictability. Our findings suggest that data revisions in the U.S. do not satisfy simple desirable statistical properties. In th...
© 2019 Dr. Trung Duc TranThis dissertation provides three chapters that study uncertainty and its ma...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time....
In the first chapter we document the empirical properties of revisions to major macroeconomic variab...
We document the empirical properties of revisions to major macroeconomic vari-ables in the United St...
We document the empirical properties of revisions to major macroeconomic variables in the United Sta...
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revis...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
Revisions to macroeconomic variables are a significant part of the process by which researchers, the...
This dissertation contains three essays in macroeconomics and finance. Chapter 1 estimates the relat...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
We develop an unobserved components approach to study surveys of forecasts containing multiple forec...
We show that professional forecasters are able to anticipate the \u85rst but not the second revision...
Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-t...
Master in Economics: Empirical Applications and Policies. Academic Year: 2019-2020This paper conside...
© 2019 Dr. Trung Duc TranThis dissertation provides three chapters that study uncertainty and its ma...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time....
In the first chapter we document the empirical properties of revisions to major macroeconomic variab...
We document the empirical properties of revisions to major macroeconomic vari-ables in the United St...
We document the empirical properties of revisions to major macroeconomic variables in the United Sta...
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revis...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
Revisions to macroeconomic variables are a significant part of the process by which researchers, the...
This dissertation contains three essays in macroeconomics and finance. Chapter 1 estimates the relat...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
We develop an unobserved components approach to study surveys of forecasts containing multiple forec...
We show that professional forecasters are able to anticipate the \u85rst but not the second revision...
Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-t...
Master in Economics: Empirical Applications and Policies. Academic Year: 2019-2020This paper conside...
© 2019 Dr. Trung Duc TranThis dissertation provides three chapters that study uncertainty and its ma...
A recent revision to the preliminary measurement of GDP(E) growth for 2003Q2 caused considerable pre...
GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time....