Zobrazeno 1 - 10
of 44
pro vyhledávání: '"LORENZO TRAPANI"'
Publikováno v:
Journal of the American Statistical Association. :1-17
Publikováno v:
Journal of Money, Credit and Banking.
Autor:
Lajos Horváth, Lorenzo Trapani
Publikováno v:
Statistics & Probability Letters. :109829
Autor:
Lorenzo Trapani
Publikováno v:
Journal of Econometrics. 220:474-503
© 2020 Elsevier B.V. This paper provides an estimation and testing framework to assess the presence and the extent of slope heterogeneity and cointegration when the units are a mixture of spurious and/or cointegrating regressions. We propose two mom
Autor:
Lajos Horváth, Lorenzo Trapani
We propose a family of CUSUM-based statistics to detect the presence of changepoints in the deterministic part of the autoregressive parameter in a Random Coefficient Autoregressive (RCA) sequence. Our tests can be applied irrespective of whether the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::09f0d59ab8a52dbc96c92ff63813b900
Autor:
Daniele Massacci, Lorenzo Trapani
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN Electronic Journal.
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:
Econometrics and Statistics. 11:63-82
The estimation in a stationary heterogeneous panel model where unknown common factors are present is considered. A two-stage estimator is proposed and compared to existing alternative methods for the estimation of slope parameters in panels with a mu
Autor:
Lajos Horváth, Lorenzo Trapani
Publikováno v:
Journal of Econometrics. 209:338-352
We propose a test to discern between an ordinary autoregressive model, and a random coefficient one. To this end, we develop a full-fledged estimation theory for the variances of the idiosyncratic innovation and of the random coefficient, based on a