C53

Forecasting and Other Model Applications

Evaluating forecasts of a vector of variables: a German forecasting competition

JEL codes: 
C53, E27, E37
Version Date: 
Jul 2014
Author/s: 
Abstract: 

In this paper we present an evaluation of forecasts of a vector of variables of the German economy made by different institutions. Our method permits one to evaluate the forecasts for each year and then if one is interested to combine the years.

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The Additional Information Content of Survey Data. Evidence from Second Moments

JEL codes: 
C53, D83, D84
Version Date: 
Dec 2012
Author/s: 
Abstract: 

Disagreement across forecasters is a well-established fact as widespread is the use of forecasting errors as a proxy of uncertainty.

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GDP Trend Deviations and the Yield Spread: the Case of Five E.U. Countries

JEL codes: 
C53, E43, E44, E52
Version Date: 
Aug 2010
Author/s: 
Abstract: 

Several studies have established the predictive power of the yield curve in terms of real economic activity. In this paper we use data for a variety of E.U. countries: both EMU (Germany, France, Italy) and non-EMU members (Sweden and the U.K.). The data used range from 1991:Q1 to 2009:Q1.

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Bayesian Doubly Adaptive Elastic-Net Lasso for VAR Shrinkage

JEL codes: 
C11, C32, C53
Version Date: 
Jan 2012
Author/s: 
Abstract: 

We develop a novel Bayesian doubly adaptive elastic-net Lasso (DAELasso) approach for
VAR shrinkage. DAELasso achieves data selection and coefficients shrinkage in a data based manner.
It constructively deals with the explanatory variables that tend to be highly collinear by encouraging

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Measuring Output Gap Uncertainty

JEL codes: 
C32, C53, E37
Version Date: 
Mar 2010
Abstract: 

We propose a methodology for producing density forecasts for the output gap in real time using a large number of vector autoregessions in inflation and output gap measures. Density combination utilizes a linear mixture of experts framework to produce potentially non-Gaussian ensemble densities for the unobserved output gap.

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A Defence of the FOMC

JEL codes: 
C53, E52, E58
Version Date: 
Sep 2009
Author/s: 
Abstract: 

We defend the forecasting performance of the FOMC from the recent criticism of Christina and David Romer. Our argument is that the FOMC forecasts a
worst-case scenario that it uses to design decisions that will work well enough (are robust) despite possible misspecification of its model. Because these

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GDP Growth Predictions through the Yield Spread. Time-Variation and Structural Breaks

JEL codes: 
C22, C32, C53, E37, E43, E47
Version Date: 
Feb 2011
Author/s: 
Abstract: 

We use TVP models and real-time data to describe the evolution of the leading properties of the yield spread for output growth in five European economies and in the US over the last decades and until the third quarter of 2010.

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Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases

JEL codes: 
C33, C53, E52
Version Date: 
Jul 2005
Abstract: 

This paper formalizes the process of updating the nowcast and forecast on output and inflation as new releases of data become available. The marginal contribution of a particular release for the value of the signal and its precision is evaluated by computing 'news' on the basis of an evolving conditioning information set.

Evaluating An Estimated New Keynesian Small Open Economy Model

JEL codes: 
C11, C53, E17
Version Date: 
Dec 2006
Abstract: 

This paper estimates and tests a new Keynesian small open economy model in the tradition of Christiano, Eichenbaum, and Evans (2005) and Smets and Wouters (2003) using Bayesian estimation techniques on Swedish data. To account for the switch to an inflation targeting regime in 1993 we allow for a discrete break in the central bank's instrument rule.

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Forecasting Economic Aggregates by Disaggregates

JEL codes: 
C51, C53, E31
Version Date: 
Jan 2006
Author/s: 
Abstract: 

We explore whether forecasting an aggregate variable using information on its disaggregate components can improve the prediction mean squared error over first forecasting the disaggregates and then aggregating those forecasts, or, alternatively, over using only lagged aggregate information in forecasting the aggregate.

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