Forecasting real-time data allowing for data revisions

Abstract This paper investigates the differences between parameters estimated using real-time and those estimated with revised data. The models used are New Keynesian DSGE models of the Czech, Polish, Hungarian, Swiss, and Swedish small open economies in interaction with the euro area. The paper also offers an analysis of data revisions of GDP growth and inflation and trend revisions of interest rates. Data revisions are found to be unbiased and not autocorrelated in all countries. Inflation is usually measured more accurately in real-time than GDP growth, but this is not the case in the euro area. The results of the core analysis suggest that there are significant differences between parameter estimates using real-time data and those estimated using revised data. The model parameters that are most prone to significant differences between real-time and revised estimations are habit in consumption and persistence of domestic supply, of demand, and of world-wide technology shocks. The impulse response analysis suggests that the model behavior based on real-time and revised data is different.

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Frontiers of Real-Time Data Analysis

Journal of Economic Literature ◽

10.1257/jel.49.1.72 ◽

2011 ◽

Vol 49 (1) ◽

pp. 72-100 ◽

Cited By ~ 143

Author(s):

Dean Croushore

Keyword(s):

Data Analysis ◽

Real Time ◽

Data Sets ◽

Current Analysis ◽

Time Data ◽

The Past ◽

Data Revisions ◽

The World ◽

Real Time Data ◽

The Impact

In the past ten years, researchers have explored the impact of data revisions in many different contexts. Researchers have examined the properties of data revisions, how structural modeling is affected by data revisions, how data revisions affect forecasting, the impact of data revisions on monetary policy analysis, and the use of real-time data in current analysis. This paper summarizes many of the questions for which real-time data analysis has provided answers. In addition, researchers and institutions have developed better real-time data sets around the world. Still, additional research is needed in key areas and research to date has uncovered even more fruitful areas worth exploring. (JEL C52, C53, C80, E01)

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DATA REVISIONS IN THE ESTIMATION OF DSGE MODELS

Macroeconomic Dynamics ◽

10.1017/s1365100515000024 ◽

2016 ◽

Vol 20 (7) ◽

pp. 1683-1716 ◽

Cited By ~ 7

Author(s):

Miguel Casares ◽

Jesús Vázquez

Keyword(s):

Interest Rates ◽

Real Time ◽

High Variability ◽

Dsge Models ◽

Time Data ◽

Time Consumption ◽

Data Revisions ◽

The Standard Model ◽

Real Time Data

Revisions of U.S. macroeconomic data are persistent, correlated with real-time data, and with high variability (around 80% of U.S. real-time data volatility). This paper adapts a DSGE-style model to accommodate both real-time and revised data from the U.S. economy. The results show a lesser role of both habit formation and price indexation than in the standard model. In the simulations, revision shocks to both output and inflation are expansionary because the Fed reacts by cutting interest rates. Consumption revisions, in contrast, are countercyclical, consumption mirrors the observed reduction in real-time consumption. In the variance decomposition, data revisions explain 9.3% of output changes.

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Real-time data revisions and the PCE measure of inflation

Economic Modelling ◽

10.1016/j.econmod.2011.03.008 ◽

2011 ◽

Vol 28 (4) ◽

pp. 1763-1773 ◽

Cited By ~ 3

Author(s):

Heather L.R. Tierney

Keyword(s):

Real Time ◽

Time Data ◽

Data Revisions ◽

Real Time Data

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Real-time probability forecasts of UK macroeconomic events

National Institute Economic Review ◽

10.1177/0027950108089680 ◽

2008 ◽

Vol 203 ◽

pp. 78-90

Author(s):

Anthony Garratt ◽

Kevin Lee ◽

Shaun Vahey

Keyword(s):

Real Time ◽

Aggregate Demand ◽

Time Data ◽

Probability Forecast ◽

The Past ◽

Data Revisions ◽

Real Time Data ◽

Probability Forecasts ◽

Density Forecasts ◽

Release Data

An overview is provided of the issues raised in the recent literature on the use of real-time data in the context of nowcasting and forecasting UK macroeconomic events. The ideas are illustrated through two specific applications using UK real-time data available over 1961-2006 and providing probability forecasts that could have been produced in real time over the past twenty years. In the first, we consider the reliability of first-release data on the components of UK aggregate demand by looking at forecasts of the probability of substantial data revisions. In the second, we consider the estimation of the output gap, illustrating the uncertainty surrounding its measurement through density forecasts and focusing on its interpretation in terms of inflationary pressure through an event probability forecast.

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