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pro vyhledávání: '"Korobilis, Dimitris"'
We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows f
Externí odkaz:
http://arxiv.org/abs/2305.09563
When agents' information is imperfect and dispersed, existing measures of macroeconomic uncertainty based on the forecast error variance have two distinct drivers: the variance of the economic shock and the variance of the information dispersion. The
Externí odkaz:
http://arxiv.org/abs/2302.01621
This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can
Externí odkaz:
http://arxiv.org/abs/2212.10301
Autor:
Korobilis, Dimitris
A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven by a few common factors and structural identification restrictions can
Externí odkaz:
http://arxiv.org/abs/2206.06892
Autor:
Korobilis, Dimitris, Shimizu, Kenichi
In all areas of human knowledge, datasets are increasing in both size and complexity, creating the need for richer statistical models. This trend is also true for economic data, where high-dimensional and nonlinear/nonparametric inference is the norm
Externí odkaz:
http://arxiv.org/abs/2112.11751
Autor:
Korobilis, Dimitris
This paper proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility and exogenous predictors, as an e
Externí odkaz:
http://arxiv.org/abs/2004.11485
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:
http://arxiv.org/abs/2004.11486
Akademický článek
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Autor:
Gambetti, Luca, Görtz, Christoph, Korobilis, Dimitris, Tsoukalas, John D., Zanetti, Francesco
Publikováno v:
Essays in Honour of Fabio Canova