Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Davide Pettenuzzo"'
Publikováno v:
Journal of Econometrics. 227:513-517
We revisit the estimation algorithm of Pettenuzzo and Timmermann (2011) and show how to apply the posterior simulation test of Geweke (2004) to locate and correct an error in the original posterior sampling algorithm. The main modification for the ne
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
Journal of Econometrics.
Publikováno v:
Economics Letters. 224:111024
Publikováno v:
The Journal of Finance. 75:2221-2270
We develop a new approach to modeling dynamics in cash flows extracted from daily firm‐level dividend announcements. We decompose daily cash flow news into a persistent component, jumps, and temporary shocks. Empirically, we find that the persisten
Publikováno v:
Journal of Empirical Finance. 55:200-217
We develop a novel method to impose constraints on univariate predictive regressions of stock returns. Unlike the previous approaches in the literature, we implement our constraints directly on the predictor, setting it to zero whenever its value fal
Autor:
Riccardo Sabbatucci, Davide Pettenuzzo
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN Electronic Journal.
The outbreak of the Covid-19 pandemic massively increased uncertainty about firms’ cash flows and their access to financial markets. We examine its effect on firms’ strategies for preserving cash by suspending dividends and buybacks and raising n
Autor:
Dimitris Korobilis, Davide Pettenuzzo
Bayesian inference in economics is primarily perceived as a methodology for cases where the data are short, that is, not informative enough in order to be able to obtain reliable econometric estimates of quantities of interest. In these cases, prior
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1374c43805ff2392b4223ab6767693fd
https://eprints.gla.ac.uk/223797/1/223797.pdf
https://eprints.gla.ac.uk/223797/1/223797.pdf
Autor:
Davide Pettenuzzo, Dimitris Korobilis
As the amount of economic and other data generated worldwide increases vastly, a challenge for future generations of econometricians will be to master efficient algorithms for inference in empirical models with large information sets. This Chapter pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f23184d4a8788141b5e3eb449a7a4ee
http://arxiv.org/abs/2004.11486
http://arxiv.org/abs/2004.11486