University of Pennsylvania
Department of Economics
3718 Locust Walk
Philadelphia, PA 19104-6297
Institutional Affiliation: University of Pennsylvania
NBER Working Papers and Publications
|September 2009||Methods versus Substance: Measuring the Effects of Technology Shocks on Hours|
with José-Víctor Ríos-Rull, Frank Schorfheide, Cristina Fuentes-Albero, Raül Santaeulàlia-Llopis: w15375
In this paper, we employ both calibration and modern (Bayesian) estimation methods to assess the role of neutral and investment-specific technology shocks in generating fluctuations in hours. Using a neoclassical stochastic growth model, we show how answers are shaped by the identification strategies and not by the statistical approaches. The crucial parameter is the labor supply elasticity. Both a calibration procedure that uses modern assessments of the Frisch elasticity and the estimation procedures result in technology shocks accounting for 2% to 9% of the variation in hours worked in the data. We infer that we should be talking more about identification and less about the choice of particular quantitative approaches.
Published: \Methods versus Substance: Measuring the Eects of Technology Shocks on Hours" joint with Frank Schorfheide, Cristina Fuentes-Albero, Raul Santaeulalia-Llopis and Maxym Kryshko. Journal of Monetary Economics Vol. 59, Issue 8, December 2012, pp. 826-46.
|April 2009||DSGE Model-Based Forecasting of Non-modelled Variables|
with Frank Schorfheide, Keith Sill: w14872
This paper develops and illustrates a simple method to generate a DSGE model-based forecast for variables that do not explicitly appear in the model (non-core variables). We use auxiliary regressions that resemble measurement equations in a dynamic factor model to link the non-core variables to the state variables of the DSGE model. Predictions for the non-core variables are obtained by applying their measurement equations to DSGE model-generated forecasts of the state variables. Using a medium-scale New Keynesian DSGE model, we apply our approach to generate and evaluate recursive forecasts for PCE inflation, core PCE inflation, the unemployment rate, and housing starts along with predictions for the seven variables that have been used to estimate the DSGE model.
Published: Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010.
"DSGE model-based forecasting of non-modelled variables,"
International Journal of Forecasting,
Elsevier, vol. 26(2), pages 348-373, April.
citation courtesy of