Il giorno 6 giugno a partire dalle ore 16.00, presso l'auletta del Dipartimento di Studi Geeconomici, Linguistici Statistici, Storici per l'Analisi Regionale (IV piano), Facolta' di Economia, Universita' di Roma "La Sapienza", si svolgeranno i seguenti seminari:
-Professor JOHN GEWEKE (University of Iowa, USA):
"Bayesian Cross-Sectional Analysis of the
Conditional Distribution of Earnings of Men in
the United States, 1967-1996" (1)
-Professor GIANNI AMISANO (Universita' di Brescia, Italy):
"Comparing Density Forecasts via Weighted Likelihood Ratio Tests" (2)
Abstract (1):
"Bayesian Cross-Sectional Analysis of the
Conditional Distribution of Earnings of Men in
the United States, 1967-1996":
This study develops practical methods for Bayesian nonparametric inference
in regression models. The emphasis is on extending a nonparametric treatment
of the regression function to the full conditional distribution. It
applies these methods to the relationship of earnings of men in the United States to their age and education over the period 1967 through 1996. Principal findings include increasing returns to both education and experience over this period, rising
variance of earnings conditional on age and education, a negatively
skewed and leptokurtic conditional distribution of log earnings, and steadily increasing inequality with asymmetric and changing impacts on high- and low-wage earners.
These results are insensitive to several alternative nonparametric
specifications of distribution of earnings conditional on age and education.
Abstract (2):
"Comparing Density Forecasts via Weighted Likelihood Ratio Tests":
We propose a test for comparing the out-of-sample accuracy of competing
density forecasts of a variable. The test is valid under general
conditions: the data can be heterogeneous and the forecasts can be based
on (nested or non-nested) parametric models or produced by
semi-parametric, non-parametric, Bayesian estimation techniques. The
evaluation makes use of scoring rules, which are loss functions defined
over the density forecast and the realizations of the variable. We
restrict attention to the logarithmic scoring rule and propose an
asymptotic `weighted likelihood ratio' test that compares weighted
averages of the scores for the competing forecasts. The user-defined
weights are a way to focus attention on different regions of the
distribution of the variable. For a uniform weight function, the test
can be interpreted as an extension of Vuong (1989)'s likelihood ratio
test to time series data and to an out-of-sample testing framework. A
Monte Carlo simulation explores the size and power properties of our
test in finite samples. We conclude with an application on the Phillips
curve alternative specifications and we find out that a Markov Switching
regression tends to outperforms the usual linear regression specification
Lea Petrella