Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Matteo Barigozzi"'
Publikováno v:
Econometrics, Vol 8, Iss 1, p 3 (2020)
Large-dimensional dynamic factor models and dynamic stochastic general equilibrium models, both widely used in empirical macroeconomics, deal with singular stochastic vectors, i.e., vectors of dimension r which are driven by a q-dimensional white noi
Externí odkaz:
https://doaj.org/article/d188f3f8a9024f93bac99801955c10cd
Publikováno v:
Journal of the American Statistical Association. :1-17
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a4e75aeafd603f3367bde071df5b035
https://doi.org/10.1080/01621459.2022.2128807
https://doi.org/10.1080/01621459.2022.2128807
Autor:
Matteo Farnè, Matteo Barigozzi
Publikováno v:
Journal of the American Statistical Association. :1-13
We propose a new estimator of high-dimensional spectral density matrices, called ALgebraic Spectral Estimator (ALSE), under the assumption of an underlying low rank plus sparse structure, as typically assumed in dynamic factor models. The ALSE is com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02d201be4c6436d87bc3bafde2062290
https://doi.org/10.1080/01621459.2022.2126780
https://doi.org/10.1080/01621459.2022.2126780
We provide the asymptotic distributional theory for the so-called General or Generalized Dynamic Factor Model (GDFM), laying the foundations for an inferential approach in the GDFM analysis of high-dimensional time series. By exploiting the duality b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c4fd6a2a47ffadc914652fe906ef8679
http://hdl.handle.net/10044/1/102910
http://hdl.handle.net/10044/1/102910
Autor:
Matteo Barigozzi, Matteo Luciani
Publikováno v:
The Review of Economics and Statistics. :1-45
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 (co-movements, non-stationarity, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fe45a157f38bf902f2d7d5ae99f4ed0
https://doi.org/10.1162/rest_a_01119
https://doi.org/10.1162/rest_a_01119
Publikováno v:
Journal of Econometrics. 221:455-482
We study a large-dimensional Dynamic Factor Model where: (i) the vector of factors F t is I ( 1 ) and driven by a number of shocks that is smaller than the dimension of F t ; and, (ii) the idiosyncratic components are either I ( 1 ) or I ( 0 ) . Unde
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b18668db7509d2d4a41415412f33b783
https://doi.org/10.1016/j.jeconom.2020.05.004
https://doi.org/10.1016/j.jeconom.2020.05.004
Autor:
Lorenzo Trapani, Matteo Barigozzi
We develop a monitoring procedure to detect changes in a large approximate factor model. Letting r be the number of common factors, we base our statistics on the fact that the r + 1 -th eigenvalue of the sample covariance matrix is bounded under the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e870b5454611ed8caf654ec29e39b0e
https://hdl.handle.net/11585/746493
https://hdl.handle.net/11585/746493
Publikováno v:
Journal of Applied Econometrics. 34:347-364
This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non zero long run partial correlati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e65aa3f9e6f0592de814a201e42b637
https://doi.org/10.1002/jae.2676
https://doi.org/10.1002/jae.2676
This volume presents a unique collection of original research contributions by leading experts in several modern fields of econometrics and statistics, including high-dimensional, nonparametric and robust statistics, time series analysis and factor m
Autor:
Lorenzo Trapani, Matteo Barigozzi
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 tren
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9628e5266c4be32dfbe3be2e4194f2a5