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FORECASTING DISCONNECTED EXCHANGE RATES (replication data)
The inability of empirical models to forecast exchange rates has given rise to the belief that exchange rates are disconnected from macroeconomic fundamentals. This paper... -
CONSTRUCTING OPTIMAL DENSITY FORECASTS FROM POINT FORECAST COMBINATIONS (repl...
Decision makers often observe point forecasts of the same variable computed, for instance, by commercial banks, IMF and the World Bank, but the econometric models used by such... -
Forecasting interest rates with shifting endpoints (replication data)
We consider forecasting the term structure of interest rates with the assumption that factors driving the yield curve are stationary around a slowly time-varying mean or... -
ESTIMATING FISCAL LIMITS: THE CASE OF GREECE (replication data)
This paper uses Bayesian methods to estimate a real business cycle model that allows for interactions among fiscal policy instruments, the stochastic fiscal limit and sovereign... -
Bayesian VARs: Specification Choices and Forecast Accuracy (replication data)
In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt... -
A Theoretical Foundation for the Nelson-Siegel Class of Yield Curve Models (r...
Yield curve models within the popular Nelson-Siegel class are shown to arise from formal low-order Taylor approximations of the generic Gaussian affine term structure model.... -
The Contribution of Structural Break Models to Forecasting Macroeconomic Seri...
This paper compares the forecasting performance of models that have been proposed for forecasting in the presence of structural breaks. They differ in their treatment of the... -
Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting (re...
Factor models have been applied extensively for forecasting when high-dimensional datasets are available. In this case, the number of variables can be very large. For instance,... -
Local Adaptive Multiplicative Error Models for High-Frequency Forecasts (repl...
We propose a local adaptive multiplicative error model (MEM) accommodating time-varying parameters. MEM parameters are adaptively estimated based on a sequential testing... -
The Measurement and Characteristics of Professional Forecasters' Uncertainty ...
Several statistical issues that arise in the construction and interpretation of measures of uncertainty from forecast surveys that include probability questions are considered,... -
Anticipating Long-Term Stock Market Volatility (replication data)
We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that... -
The Measurement and Behavior of Uncertainty: Evidence from the ECB Survey of ...
We examine matched point and density forecasts of output growth, inflation and unemployment from the ECB Survey of Professional Forecasters. We construct measures of uncertainty... -
Growth Empirics in Panel Data Under Model Uncertainty and Weak Exogeneity (re...
This paper considers panel growth regressions in the presence of model uncertainty and reverse causality concerns. For this purpose, my econometric framework combines Bayesian... -
Forecast Rationality Tests in the Presence of Instabilities, with Application...
This paper proposes a framework to implement regression-based tests of predictive ability in unstable environments, including, in particular, forecast unbiasedness and... -
Forecasting with Bayesian Vector Autoregressions Estimated Using Professional...
We propose a Bayesian shrinkage approach for vector autoregressions (VARs) that uses short-term survey forecasts as an additional source of information about model parameters.... -
Replicating the Results in ‘A New Model of Trend Inflation’ Using Particle Ma...
An article by Chan et al. (2013) published in the Journal of Business and Economic Statistics introduces a new model for trend inflation. They allow the trend inflation to... -
Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Cho...
We consider modeling and forecasting large realized covariance matrices by penalized vector autoregressive models. We consider Lasso-type estimators to reduce the dimensionality... -
How to Identify and Forecast Bull and Bear Markets? (replication data)
Because the state of the equity market is latent, several methods have been proposed to identify past and current states of the market and forecast future ones. These methods... -
Inside the Crystal Ball: New Approaches to Predicting the Gasoline Price at t...
Appropriate real-time forecasting models for the US retail price of gasoline yield substantial reductions in the mean-squared prediction error (MSPE) at horizons up to 2 years... -
Spotting the Danger Zone: Forecasting Financial Crises With Classification Tr...
This paper introduces classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out-of-sample forecasting...