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Combining density forecasts using focused scoring rules (replication data)
We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual... -
Loss functions for predicted click-through rates in auctions for online adver...
We characterize the optimal loss functions for predicted click-through rates in auctions for online advertising. Whereas standard loss functions such as mean squared error or... -
Efficient estimation of Bayesian VARMAs with timeâvarying coefficients (repli...
Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general... -
Model selection with estimated factors and idiosyncratic components (replicat...
This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big... -
Anchoring the yield curve using survey expectations (replication data)
The dynamic behavior of the term structure of interest rates is difficult to replicate with models, and even models with a proven track record of empirical performance have... -
Have Standard VARS Remained Stable Since the Crisis? (replication data)
Small vector autoregressions are commonly used in macroeconomics for forecasting and evaluating shock transmission. This requires VAR parameters to be stable over the evaluation... -
Out-of-Sample Return Predictability: A Quantile Combination Approach (replica...
This paper develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed... -
Density Forecasts With Midas Models (replication data)
We propose a parametric block wild bootstrap approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. First, Monte Carlo simulations... -
Euromind-D: A Density Estimate of Monthly Gross Domestic Product for the Euro...
EuroMInd- D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom-up approach, pooling the density estimates of 11 GDP components, by... -
Tests of Predictive Ability for Vector Autoregressions Used for Conditional F...
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though... -
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... -
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... -
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... -
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...
