Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Andriy Norets"'
Autor:
Andriy Norets, Justinas Pelenis
Publikováno v:
Journal of Econometrics. 230:62-82
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5d5dec9f73e752d6ca06a1c0bf2cba84
https://doi.org/10.1016/j.jeconom.2021.11.004
https://doi.org/10.1016/j.jeconom.2021.11.004
Autor:
Andriy Norets, Justinas Pelenis
Publikováno v:
Econometrica. 90:1355-1377
We consider nonparametric estimation of a mixed discrete‐continuous distribution under anisotropic smoothness conditions and a possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive low
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b437801d694401c1ef069f34d50ec0a7
https://doi.org/10.3982/ecta17884
https://doi.org/10.3982/ecta17884
Autor:
Ulrich K. Müller, Andriy Norets
Publikováno v:
Econometrica. 84:2183-2213
Confidence intervals are commonly used to describe parameter uncertainty. In nonstandard problems, however, their frequentist coverage property does not guarantee that they do so in a reasonable fashion. For instance, confidence intervals may be empt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33179fa1b2467a6deef1e5e78c120a19
https://doi.org/10.3982/ecta14023
https://doi.org/10.3982/ecta14023
Autor:
Andriy Norets
Publikováno v:
Journal of Econometrics. 185:409-419
This paper studies large sample properties of a semiparametric Bayesian approach to inference in a linear regression model. The approach is to model the distribution of the regression error term by a normal distribution with the variance that is a fl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0182c2915ef85be7f2152dd48f6940d0
https://doi.org/10.1016/j.jeconom.2014.12.006
https://doi.org/10.1016/j.jeconom.2014.12.006
Autor:
Andriy Norets, Xun Tang
Publikováno v:
The Review of Economic Studies. 81:1229-1262
We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a0be2ccc9f073c6e96a75eb1a73d70d
https://doi.org/10.1093/restud/rdt050
https://doi.org/10.1093/restud/rdt050
Autor:
Justinas Pelenis, Andriy Norets
Publikováno v:
Econometric Theory. 30:606-646
This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider fini
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78df05cf805e98cb6211bf199f6aabc0
https://doi.org/10.1017/s026646661300042x
https://doi.org/10.1017/s026646661300042x
Autor:
Andriy Norets, Satoru Takahashi
Publikováno v:
Quantitative Economics. 4:149-155
This note considers a standard multinomial choice model. It is shown that if the distribution of additive utility shocks has a density, then the mapping from de- terministic components of utilities to choice probabilities is surjective. In other word
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbc91c97d25265feec4db6fd9f3611fa
https://doi.org/10.3982/qe252
https://doi.org/10.3982/qe252
Autor:
Justinas Pelenis, Andriy Norets
Publikováno v:
Journal of Econometrics. 168:332-346
In this paper, we study a Bayesian approach to exible modeling of conditional distributions. The approach uses a exible model for the joint distribution of the dependent and independent variables and then extracts the conditional distributions of int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5ef48f91d21858999c3bbbe51bee47d
https://doi.org/10.1016/j.jeconom.2012.02.001
https://doi.org/10.1016/j.jeconom.2012.02.001
Autor:
Andriy Norets
Publikováno v:
Econometric Reviews. 31:84-106
I propose a method for inference in dynamic discrete choice models (DDCM) that utilizes Markov chain Monte Carlo (MCMC) and artificial neural networks (ANNs). MCMC is intended to handle high-dimensional integration in the likelihood function of richl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb24872ba6f9b6227ed5e738184fb648
https://doi.org/10.1080/07474938.2011.607089
https://doi.org/10.1080/07474938.2011.607089