Matteo Barigozzi
Biographic Data
| ID | 8879537 |
|---|---|
| NAME | Matteo Barigozzi |
| GIVEN NAMES | Matteo |
| FAMILY NAME | Barigozzi |
| SIGNATURE | BARIGOZZI M |
| AFFILIATIONS | University of Bologna |
| ORCID | 0000-0001-9090-5708 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
The Econometric Theory Interview
Marco Lippi was born in Rome in 1943. An indefatigable and inspiring pedagogue, he has been teaching mathematics, economics, the history of economic thought, and econometrics to generations of students at the Universities of Perugia, Rome (La Sapienza, Tor Vergata, and LUISS), Modena, the Scuola Superiore Sant’Anna in Pisa, and the European Center for Advanced Research in Economics and Statistics (ECARES) in Brussels. As a fellow of the Einaudi I…
Factoring in the Micro
This paper analyzes the export volatility sources estimating a dynamic factor model on transaction‐level data. Utilizing an exhaustive dataset of French export transactions from 1993 to 2017, we reconstruct the latent factors space associated with global and destination‐specific macroeconomic shocks through a Quasi‐Maximum likelihood approach which allows accommodating both the high share of missing values and the high dimensionality of the micro…
Factor Network Autoregressions
We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the…
Fnets
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS co…
Measuring the Output Gap using Large Datasets
We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (comovements, nonstationarity, and the slow drift in long-run output growth over time). We find that (1) from the mid-1990s to 2008, the U.S. economy operated above its potential and (2) in 2018:Q4, the labor market was tighter than t…
Testing for Common Trends in Nonstationary Large Datasets
We propose a testing-based procedure to determine the number of common trends in a large nonstationary dataset. Our procedure is based on a factor representation, where we determine whether there are (and how many) common factors (i) with linear trends, and (ii) with stochastic trends. Cointegration among the factors is also permitted. Our analysis is based on the fact that those largest eigenvalues of a suitably scaled covariance matrix of the d…
No prominent works on this page.
Testing for Common Trends in Nonstationary Large Datasets
We propose a testing-based procedure to determine the number of common trends in a large nonstationary dataset. Our procedure is based on a factor representation, where we determine whether there are (and how many) common factors (i) with linear trends, and (ii) with stochastic trends. Cointegration among the factors is also permitted. Our analysis is based on the fact that those largest eigenvalues of a suitably scaled covariance matrix of the d…
Measuring the Output Gap using Large Datasets
We propose a new measure of the output gap based on a dynamic factor model that is estimated on a large number of U.S. macroeconomic indicators and which incorporates relevant stylized facts about macroeconomic data (comovements, nonstationarity, and the slow drift in long-run output growth over time). We find that (1) from the mid-1990s to 2008, the U.S. economy operated above its potential and (2) in 2018:Q4, the labor market was tighter than t…
Fnets
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under a factor-adjusted vector autoregressive (VAR) model which, after accounting for pervasive co-movements of the variables by common factors, models the remaining idiosyncratic dynamic dependence between the variables as a sparse VAR process. Network estimation of FNETS co…
Factoring in the Micro
This paper analyzes the export volatility sources estimating a dynamic factor model on transaction‐level data. Utilizing an exhaustive dataset of French export transactions from 1993 to 2017, we reconstruct the latent factors space associated with global and destination‐specific macroeconomic shocks through a Quasi‐Maximum likelihood approach which allows accommodating both the high share of missing values and the high dimensionality of the micro…
Factor Network Autoregressions
We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the…
The Econometric Theory Interview
Marco Lippi was born in Rome in 1943. An indefatigable and inspiring pedagogue, he has been teaching mathematics, economics, the history of economic thought, and econometrics to generations of students at the Universities of Perugia, Rome (La Sapienza, Tor Vergata, and LUISS), Modena, the Scuola Superiore Sant’Anna in Pisa, and the European Center for Advanced Research in Economics and Statistics (ECARES) in Brussels. As a fellow of the Einaudi I…
Computer Science (5 works) · Econometrics (5 works) · Dynamic factor (3 works) · Economics (3 works) · Factor analysis (3 works) · Mathematics (3 works) · Statistics (3 works) · Complex Network Analysis Techniques (2 works) · Data mining (2 works) · Monetary Policy and Economic Impact (2 works)