Gabriele Fiorentini
Biographic Data
| ID | 8920329 |
|---|---|
| NAME | Gabriele Fiorentini |
| GIVEN NAMES | Gabriele |
| FAMILY NAME | Fiorentini |
| SIGNATURE | FIORENTINI G |
| AFFILIATIONS | University of Florence |
| ORCID | 0000-0003-4059-546X |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
GDP Solera
We use the information in the successive vintages of GDE and GDI to obtain an improved timely measure of U.S. aggregate output by exploiting cointegration between the different measures taking seriously their monthly release calendar. We also combine all existing overlapping comprehensive revisions to achieve further improvements. We pay particular attention to the Great Recession and the COVID-19 pandemic, which, despite producing dramatic fluct…
Bayesian Analysis of the Output Gap
Our objective is to build output gap estimates that benefit from information provided by Phillips curve theory and business cycle studies. For this, we develop a Bayesian analysis of the bivariate Phillips curve model proposed by Kuttner for estimating potential output. Given our priors, we obtain samples from parameters and state variables joint posterior distribution following a Gibbs sampling strategy. We sample the state variables given param…
Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With Student t Innovations
We provide numerically reliable analytical expressions for the score, Hessian, and information matrix of conditionally heteroscedastic dynamic regression models when the conditional distribution is multivariatet. We also derive one-sided and two-sided Lagrange multiplier tests for multivariate normality versus multivariate t based on the first two moments of the squared norm of the standardized innovations evaluated at the Gaussian pseudo-maximum…
Overcoming Nonadmissibility in Arima-Model-Based Signal Extraction
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to…
No prominent works on this page.
Overcoming Nonadmissibility in Arima-Model-Based Signal Extraction
We analyze the situation in which the decomposition of a time series into orthogonal balanced components as performed by the AR IMA-model-based (AMB) method is nonadmissible. We show that considering top-heavy models for the components can solve the problem. The top-heavy decomposition is derived and the improvement achieved is illustrated by an application to a class of models often encountered in practice. Two empirical applications allow us to…
Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With Student t Innovations
We provide numerically reliable analytical expressions for the score, Hessian, and information matrix of conditionally heteroscedastic dynamic regression models when the conditional distribution is multivariatet. We also derive one-sided and two-sided Lagrange multiplier tests for multivariate normality versus multivariate t based on the first two moments of the squared norm of the standardized innovations evaluated at the Gaussian pseudo-maximum…
Bayesian Analysis of the Output Gap
Our objective is to build output gap estimates that benefit from information provided by Phillips curve theory and business cycle studies. For this, we develop a Bayesian analysis of the bivariate Phillips curve model proposed by Kuttner for estimating potential output. Given our priors, we obtain samples from parameters and state variables joint posterior distribution following a Gibbs sampling strategy. We sample the state variables given param…
GDP Solera
We use the information in the successive vintages of GDE and GDI to obtain an improved timely measure of U.S. aggregate output by exploiting cointegration between the different measures taking seriously their monthly release calendar. We also combine all existing overlapping comprehensive revisions to achieve further improvements. We pay particular attention to the Great Recession and the COVID-19 pandemic, which, despite producing dramatic fluct…
Econometrics (4 works) · Mathematics (4 works) · Statistics (3 works) · Economics (2 works) · Market Dynamics and Volatility (2 works) · Monetary Policy and Economic Impact (2 works) · Advanced Statistical Methods and Models (1 works) · Applied Mathematics (1 works) · Autoregressive conditional heteroskedasticity (1 works) · Autoregressive integrated moving average (1 works)