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Evaluating Point and Density Forecasts of DSGE Models (replication data)
This paper investigates the accuracy of forecasts from four dynamic stochastic general equilibrium (DSGE) models for inflation, output growth and the federal funds rate using a... -
When Does Government Debt Crowd Out Investment? (replication data)
We examine when government debt crowds out investment for the US economy using an estimated New Keynesian model with detailed fiscal specifications and accounting for monetary... -
Using OLS to Estimate and Test for Structural Changes in Models with Endogeno...
We consider the problem of estimating and testing for multiple breaks in a single-equation framework with regressors that are endogenous, i.e. correlated with the errors. We... -
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.... -
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... -
Relative Risk Aversion and Power-Law Distribution of Macroeconomic Disasters ...
The coefficient of relative risk aversion is notoriously difficult to estimate. Recently, Barro and Jin (On the size distribution of macroeconomic disasters, Econometrica 2011;... -
Cointegration in Panel Data with Structural Breaks and Cross-Section Dependen...
The power of standard panel cointegration statistics may be affected by misspecification errors if structural breaks in the parameters generating the process are not considered.... -
Econometric Regime Shifts and the US Subprime Bubble (replication data)
Using aggregate quarterly data for the period 1975:Q1-2010:Q4, I find that the US housing market changed from a stable regime with prices determined by fundamentals, to a highly...
