• Corpus ID: 238743896

Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators

@article{Chen2021EfficientEI,
  title={Efficient Estimation in NPIV Models: A Comparison of Various Neural Networks-Based Estimators},
  author={Jiafeng Chen and Xiaohong Chen and Elie Tamer},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.06763}
}
We investigate the computational performance of Artificial Neural Networks (ANNs) in seminonparametric instrumental variables (NPIV) models of high dimensional covariates that are relevant to empirical work in economics. We focus on efficient estimation of and inference on expectation functionals (such as weighted average derivatives) and use optimal criterion-based procedures (sieve minimum distance or SMD) and novel efficient score-based procedures (ES). Both these procedures use ANN to… 
Automatic Debiased Machine Learning in Presence of Endogeneity†
Recent advances in machine learning literature provide a series of new algorithms that both address endogeneity and can be applied in high-dimensional environments, we call them MLIV. This paper

References

SHOWING 1-10 OF 43 REFERENCES
Large sample sieve estimation of semi-nonparametric models. Handbook of econometrics
  • 2007
Adaptive estimation and uniform confidence
  • metrics,
  • 2021
Locally robust semiparametric estimation
We give a general construction of debiased/locally robust/orthogonal (LR) moment functions for GMM, where the derivative with respect to first step nonparametric estimation is zero and equivalently
Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation
This paper develops a new method for estimating a demand function and the welfare consequences of price changes. The method is applied to gasoline demand in the U.S. and is applicable to other goods.
16 Efficient estimation of models with conditional moment restrictions
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Some Invariance Principles and Central Limit Theorems for Dependent Heterogeneous Processes
Building on work of McLeish, we present a number of invariance principles for doubly indexed arrays of stochastic processes which may exhibit considerable dependence, heterogeneity, and/or trending
Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression
This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and functionals of h0. First,
...
1
2
3
4
5
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