Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data

58 Pages Posted: 26 Mar 2018 Last revised: 19 Nov 2018

See all articles by Nir Billfeld

Nir Billfeld

University of Haifa

Moshe Kim

University of Haifa - Department of Economics

Date Written: November 15, 2018

Abstract

We show that when data are endogenously truncated the widely-used IV fails to render the relationship causal as well as introduces bias into the exogenous covariates. We offer a newly-introduced semiparametric biorthogonal wavelet-based JPEG IV estimator and its associated symmetry preserving kernel, which is closely related to object recognition methods in Artificial Intelligence. The newly-introduced enriched JPEG algorithm is a denoising tool amenable for identifying redundancies in a sequence of irregular noisy data points which also accommodates a reference-free criterion function for optimal denoising. This is suitable for situations where the original data distribution is unobservable such as in the case of endogenous truncation. This estimator corrects both biases, the one generated by endogenous truncation and the one generated by endogenous covariates, by means of denoising. We introduce a multifocal variant of the local GMM (MFGMM) estimator to establish jointly the entire parameter set asymptotic properties. Using Monte Carlo simulations attest to very high accuracy of our offered semiparametric JPEG IV estimator as well as high efficiency as reflected by √n consistency. These results have emerged from utilizing 2,000,000 different distribution functions, generating 100 million realizations to construct the various data sets.

Keywords: Endogenous truncation, Semiparametric wavelet-based JPEG IV, Lifting scheme, Denoising, multifocal GMM (MFGMM)

JEL Classification: C14

Suggested Citation

Billfeld, Nir and Kim, Moshe, Semiparametric Wavelet-Based JPEG IV Estimator for Endogenously Truncated Data (November 15, 2018). Available at SSRN: https://ssrn.com/abstract=3145149 or http://dx.doi.org/10.2139/ssrn.3145149

Nir Billfeld

University of Haifa ( email )

Mount Carmel
Haifa, 31905
Israel

Moshe Kim (Contact Author)

University of Haifa - Department of Economics ( email )

Haifa 31905
Israel
(972) 4 8240115 (Phone)
(972)4-8240059 (Fax)

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