# David Tomás Jacho-ChávezEmory University | EU · Department of Economics

David Tomás Jacho-Chávez

Ph.D.

## About

55

Publications

4,609

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576

Citations

Introduction

Econometrics, Economics, Statistics

**Skills and Expertise**

Additional affiliations

September 2020 - present

September 2013 - August 2020

June 2011 - August 2013

## Publications

Publications (55)

This paper studies the identification and estimation of social parameters in a general version of the Linear-in-Means model commonly fitted in the Social Sciences with multilayered network data. A Monte Carlo exercise showcases its good small-sample properties while an empirical application to Canadian consumers’ credit usage demonstrates its appli...

Stata is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata's recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release,...

This paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class t...

This paper proposes five pointwise consistent and asymptotic normal estimators of the asymptotic variance function of the Nadaraya-Watson kernel estimator for nonparametric regression. The proposed estimators are constructed based on the first-stage nonparametric residuals, and their asymptotic properties are established under the assumption that t...

This paper proposes a new semiparametric estimator of models where the response random variable is a fraction. The estimator is constructed by optimizing a semiparametric quasi-maximum likelihood that utilizes kernel smoothing. Under suitable conditions, the consistency and asymptotic normality of the proposed estimator is established allowing for...

We propose a functional principal components method that accounts for stratified random sample weighting and time dependence in the observations to understand the evolution of distributions of monthly micro-level consumer prices for the United Kingdom (UK). We apply the method to publicly available monthly data on individual-good prices collected i...

In this paper we study the own‐price elasticity for gasoline in demand systems involving three expenditure categories in the transportation sector in Canada: gasoline, local transportation, and intercity transportation for Canadian households from 1997 to 2009. In particular, we conduct a replication of Chang and Serletis, 2014 (The demand for gaso...

In this paper, we study a credit risk (collateral) management scheme for the Canadian retail payment system designed to cover the exposure of a defaulting member. We estimate ex ante the size of a collateral pool large enough to cover exposure for a historical worst-case default scenario. The parameters of the distribution of the maxima are estimat...

Tail dependence of crude oil price returns between four major benchmark markets are analyzed through the lenses of nonparametric copula models. This paper illustrates that nonparametric copula is flexible to incorporate important empirical patterns of tail dependence and provides better goodness-of-fit to the data than the optimal parametric copula...

The problem of testing for the correct specification of semiparametric models with time series data is considered. Two general classes of M test statistics that are based on the generalized empirical likelihood method are proposed. A test for omitted covariates in a semiparametric time series regression model is then used to showcase the results. M...

This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized estimating equations models with weakly dependent data. The paper proposes new estimation methods based on smoothed two-step versions of the generalised method of moments and generalised empirical likelihood methods. An important aspect of the paper is...

Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or i...

Acemoglu et al. (American Economic Review 2008; 98: 808–842) find no effect of income on democracy when controlling for fixed effects in a dynamic panel model. Work by Moral-Benito and Bartolucci (Economics Letters 2012; 117: 844–847) and Cervellati et al. (American Economic Review 2014; 104: 707–719) suggests that the original model might have bee...

We studied labour productivity growth in Ecuador from 1998 to 2006 by using firm-level data from the annual survey of manufacturing and mining. This period is characterised by the economic crisis in 1999 and important economic reforms. During the crisis, there was a two percent annual decrease in productivity in 1998–2000, but the recovery was stro...

We document rich variation across observed firms’ characteristics, and the accompanying macroeconomic volatility, often related to political turmoil for Ukrainian manufacturing firms. We use a unique annual firm-level data for the period from 2001 to 2009 compiled from the Derzhkomstat. To understand the evolution of distributions we utilize functi...

This study addresses self-selection and heterogeneity issues inherent in measuring the efficacy of voluntary training programs. We exploit data collected from Indiana University’s introductory microeconomics course. In conjunction with their class, undergraduates were given the choice to participate in a voluntary training program called Collaborat...

This paper develops quasi-likelihood estimation for generalized varying coefficient par-tially linear models when the response is not always observable. The paper considers two estimation methods and shows that under the assumption of selection on the observables the resulting estimators are asymptotically normal. As an application of these results...

Zimmer (‘The role of copulas in the housing crisis’, Review of Economics and Statistics 2012; 94: 607–620) provides an interesting case study of the pitfalls of using parametric copulas to understand the US housing crisis in the latter part of 2000s. The original study by Zimmer (2012) employs a finite-mixture copula to illustrate that the symmetry...

We establish the consistency and asymptotic normality for a class of estimators that are linear combinations of a set of \$$sqrt \$$-consistent nonlinear estimators whose cardinality increases with sample size. The method can be compared with the usual approaches of combining the moment conditions (GMM) and combining the instruments (IV), and achie...

SUMMARYcrs is a library for R written by Jeffrey S. Racine (Maintainer) and Zhenghua Nie. This add-on package provides a collection of functions for spline-based nonparametric estimation of regression functions with both continuous and categorical regressors. Currently, the crs package integrates data-driven methods for selecting the spline degree,...

This paper investigates the evolution of firm distributions for entrant manufacturing firms in Canada using nonparametric methods. These nonparametric methods use functional principal components to describe these densities over time. This method is applied to a novel administrative firm-level database from Statistics Canada to investigate the evolu...

A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This result is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidth, random trimming, and estimated weights. An extension allows for generated r...

This paper studies the limiting behavior of general functionals of order statistics and their multivariate concomitants for weakly dependent data. The asymptotic analysis is performed under a conditional moment-based notion of dependence for vector-valued time series. It is argued, through analysis of various examples, that the dependence condition...

This paper considers the problem of estimation and inference for smooth semiparametric models with weakly dependent data using two-step smoothed Generalized Method of Moment, Generalized Empirical Likelihood and Exponentially Tilted methods. The resulting estimators are asymptotically normal whereas the asymptotic distribution of the proposed test...

The paper introduces a root-n consistent estimator of the probability density function of the response variable in a nonparametric regression model. The proposed estimator is shown to have a (uniform) asymptotic normal distribution, and it is computationally very simple to calculate. A Monte Carlo experiment confirms our theoretical results, and an...

This paper uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. Th...

This paper proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal...

This paper considers the problem of estimating expected values of functions that are inversely weighted by an unknown density using the k-Nearest Neighbour (k-NN) method. It establishes the √T-consistency and the asymptotic normality of an estimator that allows for strictly stationary time-series data. The consistency of the Bartlett estimator of t...

This paper investigates the evolution of firm distributions for entrant manufacturing firms in Canada using functional principal components analysis. This methodology describes the dynamics of firms by examining production variables, size and labour productivity, and a financial variable, leverage (debt-to-asset ratio). We adapt the original method...

We explore the dynamics of firm size distributions through the lens of Functional Principal Component Analysis as proposed by Kneip and Utikal (2001). Using samples of UK firms from Geroski et al. (2003) we apply the methodology to their balanced panel sample, present in the sample for all 31 years. We extend the analysis to an unbalanced panel sam...

Let r(x,z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses the identification and consistent estimation of the unknown functions H, M, G and F, where r(x,z)=H[M(x,z)], M(x,z)=G(x)+F(z), and H is strictly monotonic. An estimation algorithm is proposed for each of the model's unkno...

We investigate the properties of a kernel-type multivariate regression estimator first proposed by Mack and Müller (Sankhya 51:59–72, 1989) in the context of univariate derivative estimation. Our proposed procedure, unlike theirs, assumes that bandwidths of the same order are used throughout; this gives more realistic asymptotics for the estimation...

A numerical approximation of the critical values of Cramér-von Mises (CvM) tests is proposed for testing the correct specification of general conditional location parametric functionals. These specifications include conditional mean and quantile models. This method is based on estimation of the eigenelements of the covariance operator associated wi...

This paper characterizes the bandwidth value (h) that is optimal for estimating parameters of the form , where the conditional density of a scalar continuous random variable V, given a random vector U, , is replaced by its kernel estimator. That is, the parameter inversely weighted by , and it is the building block of various semiparametric estimat...

The effectiveness of voluntary training programs is inherently difficult to measure due to the issue of selection bias. Random assignment is suggested as a method to ameliorate this bias. We construct our own randomization experiment to gain insight into this question by utilizing a structured platform. In this study, we investigate the efficacy of...

We investigate the efficacy of Collaborative Learning Recitation Sessions on Students Outcome. A quasi-randomized experiment is conducted on two large introductory microeconomics class sections at Indiana University’s Bloomington-Indiana campus in the Fall 2009 semester. Program evaluation methods are used to compute the treatment effect of student...

This article considers empirical likelihood in the context of efficient semiparametric estimators of average treatment effects. It shows that the empirical likelihood ratio converges to a nonstandard distribution, and proposes a corrected test statistic that is asymptotically chi-squared. A small Monte Carlo experiment suggests that the corrected e...

This paper investigates the evolution of firm distributions for entrant manufacturing firms in Canada using nonparametric methods. These nonparametric methods allow a flexible method based on functional principal components or dynamic densities to characterize how these densities evolve over time. This method is applied to a novel administrative fi...

A new way of constructing efficient semiparametric instrumental variable estimators is proposed. The method involves the combination of a large number of possibly inefficient estimators rather than combining the instruments into an optimal instrument function. The consistency and asymptotic normality is established for a class of estimators that ar...

Consider the unconditional moment restriction E[m(y ν w;π0)/fV|w (ν|w) s (w;π0 0)] = 0, where m(·) and s(·) are known vector-valued functions of data (y T,ν , wT)T. The smallest asymptotic variance that √n-consistent regular estimators of 0 can have is calculated when fV|w(·) is only known to be a bounded, continuous, nonzero conditional density fu...

According to conventional wisdom, a positive relationship exists between governance and growth. This paper reexamines this empirical relationship using nonparametric methods. We use different governance measures, as defined in World Governance Indicators provided by the World Bank. The findings show that only three of the six measures: voice and ac...

The substance and style of modern microeconometrics is shaped by its role in analyses of public policy issues. Computational considerations have proved to be an important influence on the method-ology and scope of empirical analyses that address these issues. To be convincing to a wide readership the empirical analyses need to be based on represent...

We prove the strong consistency, uniformly in the bandwidth, of the smooth varying coefficient conditional least squares estimator. Our results justify data-driven choices of bandwidths, such as Silverman's rule-of thumb, or standard cross-validation, that are usually implemented by most practitioners.

This letter considers the problem of estimating expected values of functions that are inversely weighted by an unknown density using the k-Nearest Neighbor method. LÂ²-consistency is established. The proposed estimator is also shown to be asymptotically semiparametric efficient. Some limited Monte Carlo experiments show that the proposed estimator...

Conventional wisdom dictates that there is a positive relationship between governance and growth. This chapter reexamines this empirical relationship using nonparametric quantile methods. We apply these methods on different levels of countries' growth and governance measures as defined in World Governance Indicators provided by the World Bank. We c...

This note applies conditional density estimation as a visual method to present results. The proposed method is illustrated by application to a firm-level manufacturing data set from Ecuador in 2002.

We propose a new kernel estimator of conditional density and derive its asymptotic bias and variance. This new, non-negative estimator, is obtained by 'internalizing' the random denominator of the well known local constant smoother of Rosenblatt (1969). A limited Monte Carlo experiment demonstrates that the new estimator performs well in finite sam...

Let r (x;z) be a function that, along with its derivatives, can be consistently estimated nonpara- metrically. This paper discusses identication and consistent estimation of the unknown functions H, M, G and F , where r (x;z) = H (M (x;z)), M (x;z) = G (x) +F (z), and H is strictly mono- tonic. An estimation algorithm is proposed for each of the mo...

The focal point of this thesis is on identification and estimation of nonparametric models, as well as the efficiency and higher order properties of a class of semiparametric estimators in Microeconometrics. We present a new identification result for a particular nonparametric model that nests many popular parametric/nonparametric Econometric model...

Ecuador's large economic crisis in the late 1990s serves as an important case study of re-source reallocation. We conduct an empirical analysis using firm level data for 1998-2007 to investigate resource reallocation, firm turnover, and productivity patterns. We use the model by Restuccia and Rogerson (2008) to examine firm-level distortions and pr...