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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 developments of the last thirty years. The topics presented illustrate that the available …
The econometrics of shape restrictions
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 …
applied work. Our objectives are threefold. First, we aim to emphasize the diversity of …
Inference on breakdown frontiers
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 …
of assumptions which lead to a specific conclusion and those which do not. In a potential …
Shape constraints in economics and operations research
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 …
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 …
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 …
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 …
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 …
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 …
continuous covariates by a discrete number of intervals, in which case the covariates are …