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Marginalized Predictive Likelihood Comparisons of Linear Gaussian State-Space...
The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of... -
State Prices of Conditional Quantiles: New Evidence on Time Variation in the ...
We develop a set of statistics to represent the option-implied stochastic discount factor and we apply them to S&P 500 returns between 1990 and 2012. Our statistics, which... -
Modeling Financial Sector Joint Tail Risk in the Euro Area (replication data)
We develop a novel high-dimensional non-Gaussian modeling framework to infer measures of conditional and joint default risk for numerous financial sector firms. The model is... -
Estimation of Poverty Transition Matrices with Noisy Data (replication data)
This paper investigates measurement error biases in estimated poverty transition matrices. We compare transition matrices based on survey expenditure data to transition matrices... -
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... -
Forecasting Tail Risks (replication data)
This paper presents an early warning system as a set of multi-period forecasts of indicators of tail real and financial risks obtained using a large database of monthly US data... -
Anticipation, Tax Avoidance, and the Price Elasticity of Gasoline Demand (rep...
Least-squares estimates of the response of gasoline consumption to a change in the gasoline price are biased toward zero, given the endogeneity of gasoline prices. A seemingly... -
Average and Marginal Returns to Upper Secondary Schooling in Indonesia (repli...
This paper estimates average and marginal returns to schooling in Indonesia using a semiparametric selection model. Identification of the model is given by geographic variation... -
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...
