Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand models

F Dunker, S Hoderlein, H Kaido - arxiv preprint arxiv:2201.06140, 2022 - arxiv.org
This paper studies nonparametric identification in market level demand models for
differentiated products with heterogeneous consumers. We consider a general class of …

Nonparametric identification of random coefficients in aggregate demand models for differentiated products

F Dunker, S Hoderlein, H Kaido - The Econometrics Journal, 2023 - academic.oup.com
This paper studies nonparametric identification in market-level demand models for
differentiated products with heterogeneous consumers. We consider a general class of …

A sliced Wasserstein and diffusion approach to random coefficient models

K Lim, T Ye, F Han - arxiv preprint arxiv:2502.04654, 2025 - arxiv.org
We propose a new minimum-distance estimator for linear random coefficient models. This
estimator integrates the recently advanced sliced Wasserstein distance with the nearest …

Changes in risk appreciation, and short memory of house buyers when the market is hot, a case study of Christchurch, New Zealand

E Mendoza, F Dunker, M Reale - Journal of Property Research, 2024 - Taylor & Francis
In this paper, house prices in Christchurch are analysed over three distinct periods of time:
post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown. The first …

A Novel Approach to Statistical Problems Without Identifiability

AD Adams - 2024 - search.proquest.com
DISSERTATION A NOVEL APPROACH TO STATISTICAL PROBLEMS WITHOUT
IDENTIFIABILITY Submitted by Addison D. Adams Department of Statis Page 1 …

Nonparametric estimation of the random coefficients model in python

E Mendoza, F Dunker, M Reale - arxiv preprint arxiv:2108.03582, 2021 - arxiv.org
We present $\textbf {PyRMLE} $, a Python module that implements Regularized Maximum
Likelihood Estimation for the analysis of Random Coefficient models. $\textbf {PyRMLE} $ is …