CV - Job Market Paper  
 

Odendahl, Florens

Job market candidate

Contact information

Tel. +34 93 542 2685

florens.odendahl@upf.edu

 

Available for Interviews at :

Simposio de la Asociación Española de Economía (SAEe), December 14-16, Barcelona, Spain

Allied Social Science Associations (ASSA), January 5-7, Philadelphia, US

 

Research interests

Time Series Econometrics. Applied Macroeconometrics. Forecasting. Statistics.

Placement officer

Filippo Ippolito
filippo.ippolito@upf.edu
 

References

Geert Mesters
geert.mesters@upf.edu

Barbara Rossi (Advisor)
barbara.rossi@upf.edu

Tatevik Sekhposyan
tsekhposyan@tamu.edu

Christian Brownlees
christian.brownlees@upf.edu

Research

"Survey-Based Multivariate Density Forecasts"(Job Market Paper)
We present a methodology to estimate multivariate density forecasts based on marginal densities from survey data. Survey-based predictions are often competitive to time series models in terms of their forecasting performance, but have a univariate focus. Our methodology exploits the information in the survey marginal densities for the estimation of the multivariate densities. We demonstrate the importance of the multivariate aspect for new measures of the state of the economy and a novel measure of joint macroeconomic uncertainty. A stronger distributional dependence between the variables has different implications for the two types of measures. It tends to increase the probability of "recession-type" events and reduces uncertainty. Empirical results based on SPF data from the euro area and the U.S. show that the survey-based joint density forecasts are competitive to current benchmark econometric models. When considering joint macroeconomic uncertainty, the dependence has sizeable effects on uncertainty in the aftermath of the Great Recession, a feature of the data that existing measures do not capture.

“Comparing Model Forecasting Performances Under Markov Switching”, with B Rossi and T. Sekhposyan
We propose novel tests to compare models forecasting ability when the relative forecasting performance of the models changes over time according to a Markov switching process. Existing tests focus on constant out-of-sample performances or use non-parametric techniques robust to time-variation; consequently, they may lack power against the alternative of discrete and weakly dependent parametric time-variation. The tests we propose have better power than existing tests in selecting the best forecasting model when the relative performance of the models follows a Markov switching process. Monte Carlo results suggest that the proposed test statistics performs well in finite samples. We illustrate the empirical usefulness of our procedure by applying our tests to compare competing forecasts of US industrial output using different macroeconomics predictors.