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A New Kind of Two-Stage Least Squares Based on Shapley Value Regression

Mishra, Sudhanshu K. - 2018

The Two-Stage Least squares method for obtaining the estimated structural coefficients of a simultaneous linear equations model is a celebrated method that uses OLS at the first stage for estimating the reduced form coefficients and obtaining the expected values in the arrays of current...

Persistent link: https://www.econbiz.de/10012931295

Individual Weighted Excess and Least Square Values

Zhang, Xia - 2020

rise to lower and upper bounds for the core payoff vectors and, using these bounds, we define the weighted super core as a … polyhedron that contains the core. It turns out that the least square values can be seen as a center of this weighted super core …, giving a third new characterization of the least square values. Finally, these lower and upper bounds for the core inspire us …

Persistent link: https://www.econbiz.de/10012830606

A Note on Computing the Standard Errors of Estimate of Composite Index

Mishra, Sudhanshu K. - 2017

This short note proposes working out of the standard errors of estimate of composite indices when they are constructed by using intrinsically derived weights. It illustrates the proposed method, using the jackknife re-sampling technique, by an example that relates to crime of different types in...

Persistent link: https://www.econbiz.de/10012944216

Semiparametric Estimation of First-Price Auction Models

Aryal, Gaurab - 2019

In this paper we propose a two-step semiparametric procedure to estimate first-price auction models. In the first-step, we estimate the bid density and distribution using local polynomial method, and recover a sample of (pseudo) private values. In the second-step, we apply the method of moments...

Persistent link: https://www.econbiz.de/10012904820

Parametric inference with universal function approximators

Joseph, Andreas - 2020

Universal function approximators, such as artificial neural networks, can learn a large variety of target functions arbitrarily well given sufficient training data. This flexibility comes at the cost of the ability to perform parametric inference. We address this gap by proposing a generic...

Persistent link: https://www.econbiz.de/10012849896