Microeconometrics with partial identification

F Molinari - Handbook of econometrics, 2020 - Elsevier
This chapter reviews the microeconometrics literature on partial identification, focusing on
the developments of the last thirty years. The topics presented illustrate that the available …

The econometrics of shape restrictions

D Chetverikov, A Santos… - Annual Review of …, 2018 - annualreviews.org
We review recent developments in the econometrics of shape restrictions and their role in
applied work. Our objectives are threefold. First, we aim to emphasize the diversity of …

Inference on breakdown frontiers

MA Masten, A Poirier - Quantitative Economics, 2020 - Wiley Online Library
Given a set of baseline assumptions, a breakdown frontier is the boundary between the set
of assumptions which lead to a specific conclusion and those which do not. In a potential …

Shape constraints in economics and operations research

AL Johnson, DR Jiang - Statistical Science, 2018 - JSTOR
Shape constraints, motivated by either application-specific assumptions or existing theory,
can be imposed during model estimation to restrict the feasible region of the parameters …

Nonparametric Approaches to Empirical Welfare Analysis

D Bhattacharya - Journal of Economic Literature, 2024 - aeaweb.org
Welfare analysis of policy interventions is ubiquitous in economic research. It plays an
important role in merger analysis and antitrust litigation, design of tax and subsidies, and …

The empirical content of binary choice models

D Bhattacharya - Econometrica, 2021 - Wiley Online Library
An important goal of empirical demand analysis is choice and welfare prediction on
counterfactual budget sets arising from potential policy interventions. Such predictions are …

Granular neural networks: The development of granular input spaces and parameters spaces through a hierarchical allocation of information granularity

M Song, Y **g - Information Sciences, 2020 - Elsevier
The issue of granular output optimization of neural networks with fixed connections within a
given input space is explored. The numeric output optimization is a highly nonlinear problem …

Uncertain interval data EFCM-ID clustering algorithm based on machine learning

Y Mao, Y Liu, MA Khan, J Wang, D Mao… - Journal of robotics and …, 2019 - jstage.jst.go.jp
In clustering problems based on fuzzy c-means (FCM) for uncertain interval data, points
within the interval are usually assumed to have uniform distribution, resulting in the difficulty …

The two‐sample linear regression model with interval‐censored covariates

D Pacini - Journal of Applied Econometrics, 2019 - Wiley Online Library
There are surveys that gather precise information on an outcome of interest, but measure
continuous covariates by a discrete number of intervals, in which case the covariates are …