Latest in econ.em

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Nonparametric Identification and Estimation with Independent, Discrete InstrumentsJun 12 2019In a nonparametric instrumental regression model, we strengthen the conventional moment independence assumption towards full statistical independence between instrument and error term. This allows us to prove identification results and develop estimators ... More
Generalized Beta Prime Distribution: Stochastic Model of Economic Exchange and Properties of Inequality IndicesJun 11 2019We argue that a stochastic model of economic exchange, whose steady-state distribution is a Generalized Beta Prime (also known as GB2), and some unique properties of the latter, are the reason for GB2's success in describing wealth/income distributions. ... More
Bias-Aware Inference in Fuzzy Regression Discontinuity DesignsJun 11 2019Fuzzy regression discontinuity (FRD) designs occur frequently in many areas of applied economics. We argue that the confidence intervals based on nonparametric local linear regression that are commonly reported in empirical FRD studies can have poor finite ... More
Regional economic convergence and spatial quantile regressionJun 11 2019The presence of \b{eta}-convergence in European regions is an important issue to be analyzed. In this paper, we adopt a quantile regression approach in analyzing economic convergence. While previous work has performed quantile regression at the national ... More
The Regression Discontinuity DesignJun 10 2019This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods.
Efficient Bayesian estimation for GARCH-type models via Sequential Monte CarloJun 10 2019This paper exploits the advantages of sequential Monte Carlo (SMC) to develop parameter estimation and model selection methods for GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) style models. This approach provides an alternative method ... More
A long short-term memory stochastic volatility modelJun 07 2019Stochastic Volatility (SV) models are widely used in the financial sector while Long Short-Term Memory (LSTM) models have been successfully used in many large-scale industrial applications of Deep Learning. Our article combines these two methods non trivially ... More
Counterfactual Inference for Consumer Choice Across Many Product CategoriesJun 06 2019This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. The consumer's utility is additive in the different ... More
Indirect Inference for Locally Stationary ModelsJun 05 2019We propose the use of indirect inference estimation for inference in locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of the model ... More
Assessing Disparate Impacts of Personalized Interventions: Identifiability and BoundsJun 04 2019Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely ... More
The Laws of Motion of the Broker Call Rate in the United StatesJun 03 2019In this paper, which is the third installment of the author's trilogy on margin loan pricing, we analyze $1,367$ monthly observations of the U.S. broker call money rate, which is the interest rate at which stock brokers can borrow to fund their margin ... More
Stress Testing Network Reconstruction via Graphical Causal ModeJun 03 2019An optimal evaluation of the resilience in financial portfolios implies having initial hypotheses about the causal influence between the macroeconomic variables and the risk parameters. In this paper, we propose a graphical model for to infer the causal ... More
Stress Testing Network Reconstruction via Graphical Causal ModelJun 03 2019Jun 05 2019An optimal evaluation of the resilience in financial portfolios implies having initial hypotheses about the causal influence between the macroeconomic variables and the risk parameters. In this paper, we propose a graphical model for to infer the causal ... More
Bayesian nonparametric graphical models for time-varying parameters VARJun 03 2019Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common approach for addressing dimensionality issues relies on the use of static ... More
The Theory of Weak Revealed PreferenceJun 01 2019We offer a rationalization of the weak generalized axiom of revealed preference (WGARP) for both finite and infinite data sets of consumer choice. We call it maximin rationalization, in which each pairwise choice is associated with a "local" utility function. ... More
At What Level Should One Cluster Standard Errors in Paired Experiments?Jun 01 2019In paired experiments, the units included in the randomization, e.g. villages, are matched into pairs, and then one unit of each pair is randomly assigned to treatment. We conducted a survey of papers that used paired randomized experiments in economics. ... More
Kernel Instrumental Variable RegressionJun 01 2019Instrumental variable regression is a strategy for learning causal relationships in observational data. If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available ... More
lspartition: Partitioning-Based Least Squares RegressionJun 01 2019Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise polynomials. This article discusses the main methodological and numerical features ... More
nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected InferenceJun 01 2019Nonparametric kernel density and local polynomial regression estimators are very popular in Statistics, Economics, and many other disciplines. They are routinely employed in applied work, either as part of the main empirical analysis or as a preliminary ... More
Counterfactual Analysis under Partial Identification Using Locally Robust RefinementMay 31 2019Structural models that admit multiple reduced forms, such as game-theoretic models with multiple equilibria, pose challenges in practice, especially when parameters are set-identified and the identified set is large. In such cases, researchers often choose ... More
On Policy Evaluation with Aggregate Time-Series ShocksMay 31 2019In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level ... More
Learned Sectors: A fundamentals-driven sector reclassification projectMay 31 2019Market sectors play a key role in the efficient flow of capital through the modern Global economy. We analyze existing sectorization heuristics, and observe that the most popular - the GICS (which informs the S&P 500), and the NAICS (published by the ... More
Nonparametric Sample SplittingMay 30 2019This paper develops a threshold regression model, where the threshold is determined by an unknown relation between two variables. The threshold function is estimated fully nonparametrically. The observations are allowed to be cross-sectionally dependent ... More
Heterogeneity in demand and optimal price conditioning for local rail transportMay 30 2019This paper describes the results of research project on optimal pricing for LLC "Perm Local Rail Company". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand ... More
Deep Generalized Method of Moments for Instrumental Variable AnalysisMay 29 2019Instrumental variable analysis is a powerful tool for estimating causal effects when randomization or full control of confounders is not possible. The application of standard methods such as 2SLS, GMM, and more recent variants are significantly impeded ... More
Centered and non-centered variance inflation factorMay 29 2019This paper analyzes the diagnostic of near multicollinearity in a multiple linear regression from auxiliary centered regressions (with intercept) and non-centered (without intercept). From these auxiliary regression, the centered and non-centered Variance ... More
The Income Fluctuation Problem and the Evolution of WealthMay 29 2019We analyze the household savings problem in a general setting where returns on assets, non-financial income and impatience are all state dependent and fluctuate over time. All three processes can be serially correlated and mutually dependent. Rewards ... More
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High DimensionsMay 28 2019When pre-processing observational data via matching, we seek to approximate each unit with maximally similar peers that had an alternative treatment status--essentially replicating a randomized block design. However, as one considers a growing number ... More
Graph-based era segmentation of international financial integrationMay 28 2019Assessing world-wide financial integration constitutes a recurrent challenge in macroeconometrics, often addressed by visual inspections searching for data patterns. Econophysics literature enables us to build complementary, data-driven measures of financial ... More
Local Asymptotic Equivalence of the Bai and Ng (2004) and Moon and Perron (2004) Frameworks for Panel Unit Root TestingMay 27 2019This paper considers unit-root tests in large n and large T heterogeneous panels with cross-sectional dependence generated by unobserved factors. We reconsider the two prevalent approaches in the literature, that of Moon and Perron (2004) and the PANIC ... More
Inducing Sparsity and Shrinkage in Time-Varying Parameter ModelsMay 26 2019Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced ... More
Machine Learning Estimation of Heterogeneous Treatment Effects with InstrumentsMay 24 2019We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where ... More
Machine Learning Estimation of Heterogeneous Treatment Effects with InstrumentsMay 24 2019Jun 06 2019We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where ... More
Machine Learning Estimation of Heterogeneous Treatment Effects with InstrumentsMay 24 2019Jun 03 2019We consider the estimation of heterogeneous treatment effects with arbitrary machine learning methods in the presence of unobserved confounders with the aid of a valid instrument. Such settings arise in A/B tests with an intent-to-treat structure, where ... More
Semi-Parametric Efficient Policy Learning with Continuous ActionsMay 24 2019We consider off-policy evaluation and optimization with continuous action spaces. We focus on observational data where the data collection policy is unknown and needs to be estimated. We take a semi-parametric approach where the value function takes a ... More
Smoothing quantile regressionsMay 21 2019May 29 2019We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer ... More
Smoothing quantile regressionsMay 21 2019We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer ... More
Demand forecasting techniques for build-to-order lean manufacturing supply chainsMay 20 2019Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due ... More
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power MarketsMay 20 2019We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate ... More
Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning ModelMay 20 2019In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ... More
Nonparametric Instrumental Regressions with (Potentially Discrete) Instruments Independent of the Error TermMay 19 2019We consider a nonparametric instrumental regression model with continuous endogenous regressor where instruments are fully independent of the error term. This assumption allows us to extend the reach of this model to cases where the instrumental variable ... More
Cointegration in high frequency dataMay 17 2019In this paper, we consider a framework adapting the notion of cointegration when two asset prices are generated by a driftless It\^{o}-semimartingale featuring jumps with infinite activity, observed synchronously and regularly at high frequency. We develop ... More
A Comment on "Estimating Dynamic Discrete Choice Models with Hyperbolic Discounting" by Hanming Fang and Yang WangMay 16 2019The recent literature often cites Fang and Wang (2015) for analyzing the identification of time preferences in dynamic discrete choice under exclusion restrictions (e.g. Yao et al., 2012; Lee, 2013; Ching et al., 2013; Norets and Tang, 2014; Dub\'e et ... More
The Empirical Saddlepoint EstimatorMay 16 2019We define a moment-based estimator that maximizes the empirical saddlepoint (ESP) approximation of the distribution of solutions to empirical moment conditions. We call it the ESP estimator. We prove its existence, consistency and asymptotic normality, ... More
Non-Asymptotic Inference in a Class of Optimization ProblemsMay 16 2019This paper describes a method for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. The optimization problems arise in applications in which grouped data are used for estimation ... More
mRSC: Multi-dimensional Robust Synthetic ControlMay 15 2019When evaluating the impact of a policy on a metric of interest, it may not be possible to conduct a randomized control trial. In settings where only observational data is available, Synthetic Control (SC) methods provide a popular data-driven approach ... More
Analyzing Subjective Well-Being Data with MisclassificationMay 15 2019We use novel nonparametric techniques to test for the presence of non-classical measurement error in reported life satisfaction (LS) and study the potential effects from ignoring it. Our dataset comes from Wave 3 of the UK Understanding Society that is ... More
Sustainable Investing and the Cross-Section of Maximum DrawdownMay 13 2019We use supervised learning to identify factors that predict the cross-section of maximum drawdown for stocks in the US equity market. Our data run from January 1980 to June 2018 and our analysis includes ordinary least squares, penalized linear regressions, ... More
Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect EstimationMay 11 2019We propose a new estimation method for heterogeneous causal effects which utilizes a regression discontinuity (RD) design for multiple datasets with different thresholds. The standard RD design is frequently used in applied researches, but the result ... More
Demand and Welfare Analysis in Discrete Choice Models with Social InteractionsMay 10 2019Many real-life settings of consumer-choice involve social interactions, causing targeted policies to have spillover-effects. This paper develops novel empirical tools for analyzing demand and welfare-effects of policy-interventions in binary choice settings ... More
Identifying Present-Bias from the Timing of ChoicesMay 10 2019Timing decisions are common: when to file your taxes, finish a referee report, or complete a task at work. We ask whether time preferences can be inferred when \textsl{only} task completion is observed. To answer this question, we analyze the following ... More
The Likelihood of Mixed Hitting TimesMay 09 2019We present a method for computing the likelihood of a mixed hitting-time model that specifies durations as the first time a latent L\'evy process crosses a heterogeneous threshold. This likelihood is not generally known in closed form, but its Laplace ... More
Lasso under Multi-way Clustering: Estimation and Post-selection InferenceMay 06 2019This paper studies regression models with lasso when data is sampled under multi-way clustering. First, we establish the convergence rates for the lasso and post-lasso estimators. Second, we propose a novel inference method based on a post-double-selection ... More
Estimation of high-dimensional factor models and its application in power data analysisMay 06 2019In dealing with high-dimensional data, factor models are often used for reducing dimensions and extracting relevant information. The spectrum of covariance matrices from power data exhibits two aspects: 1) bulk, which arises from random noise or fluctuations ... More
Non-standard inference for augmented double autoregressive models with null volatility coefficientsMay 06 2019This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical ... More
A Uniform Bound of the Operator Norm of Random Element Matrices and Operator Norm Minimizing EstimationMay 03 2019In this paper, we derive a uniform stochastic bound of the operator norm (or equivalently, the largest singular value) of random matrices whose elements are indexed by parameters. As an application, we propose a new estimator that minimizes the operator ... More
Sparsity Double Robust Inference of Average Treatment EffectsMay 02 2019Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method ... More
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual HeterogeneityMay 01 2019Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for Bayesian estimation of mixed logit models. In this paper, we derive a VB method for posterior inference in mixed ... More
Boosting the Hodrick-Prescott FilterMay 01 2019The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. The technique is nonparametric and seeks to decompose a time series into a trend and a cyclical component unaided by economic theory ... More
A Factor-Augmented Markov Switching (FAMS) ModelApr 30 2019This paper investigates the role of high-dimensional information sets in the context of Markov switching models with time varying transition probabilities. Markov switching models are commonly employed in empirical macroeconomic research and policy work. ... More
A Factor-Augmented Markov Switching (FAMS) ModelApr 30 2019May 03 2019This paper investigates the role of high-dimensional information sets in the context of Markov switching models with time varying transition probabilities. Markov switching models are commonly employed in empirical macroeconomic research and policy work. ... More
Fast Mesh Refinement in Pseudospectral Optimal ControlApr 29 2019Mesh refinement in pseudospectral (PS) optimal control is embarrassingly easy --- simply increase the order $N$ of the Lagrange interpolating polynomial and the mathematics of convergence automates the distribution of the grid points. Unfortunately, as ... More
Exact Testing of Many Moment Inequalities Against Multiple ViolationsApr 29 2019This paper considers the problem of testing many moment inequalities, where the number of moment inequalities ($p$) is possibly larger than the sample size ($n$). Chernozhukov et al. (2018) proposed asymptotic tests for this problem using the maximum ... More
Working women and caste in India: A study of social disadvantage using feature attributionApr 27 2019Women belonging to the socially disadvantaged caste-groups in India have historically been engaged in labour-intensive, blue-collar work. We study whether there has been any change in the ability to predict a woman's work-status and work-type based on ... More
Nonparametric Estimation and Inference in Psychological and Economic ExperimentsApr 25 2019The goal of this paper is to provide some statistical tools for nonparametric estimation and inference in psychological and economic experiments. We consider a framework in which a quantity of interest depends on some primitives through an unknown function ... More
Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)Apr 25 2019This paper considers improved forecasting in possibly nonlinear dynamic settings, with high-dimension predictors ("big data" environments). To overcome the curse of dimensionality and manage data and model complexity, we examine shrinkage estimation of ... More
Identification of Regression Models with a Misclassified and Endogenous Binary RegressorApr 25 2019We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one ... More
Normal Approximation in Large Network ModelsApr 24 2019We prove central limit theorems for models of network formation and network processes with homophilous agents. The results hold under large-network asymptotics, enabling inference in the typical setting where the sample consists of a small set of large ... More
Average Density Estimators: Efficiency and Bootstrap ConsistencyApr 19 2019This paper highlights a tension between semiparametric efficiency and bootstrap consistency in the context of a canonical semiparametric estimation problem. It is shown that although simple plug-in estimators suffer from bias problems preventing them ... More
Location-Sector Analysis of International Profit Shifting on a Multilayer Ownership-Tax NetworkApr 19 2019Currently all countries including developing countries are expected to utilize their own tax revenues and carry out their own development for solving poverty in their countries. However, developing countries cannot earn tax revenues like developed countries ... More
Ridge regularization for Mean Squared Error Reduction in Regression with Weak InstrumentsApr 18 2019In this paper, I show that classic two-stage least squares (2SLS) estimates are highly unstable with weak instruments. I propose a ridge estimator (ridge IV) and show that it is asymptotically normal even with weak instruments, whereas 2SLS is severely ... More
Sharp Bounds for the Marginal Treatment Effect with Sample SelectionApr 17 2019I analyze treatment effects in situations when agents endogenously select into the treatment group and into the observed sample. As a theoretical contribution, I propose pointwise sharp bounds for the marginal treatment effect (MTE) of interest within ... More
A Generalized Continuous-Multinomial Response Model with a t-distributed Error KernelApr 17 2019Apr 23 2019In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to substitute these thin-tailed distributions with a t-distribution. First, we ... More
A Generalized Continuous-Multinomial Response Model with a t-distributed Error KernelApr 17 2019In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to substitute these thin-tailed distributions with a t-distribution. First, we ... More
Subgeometric ergodicity and $β$-mixingApr 15 2019It is well known that stationary geometrically ergodic Markov chains are $\beta$-mixing (absolutely regular) with geometrically decaying mixing coefficients. Furthermore, for initial distributions other than the stationary one, geometric ergodicity implies ... More
Subgeometric ergodicity and $β$-mixingApr 15 2019Apr 16 2019It is well known that stationary geometrically ergodic Markov chains are $\beta$-mixing (absolutely regular) with geometrically decaying mixing coefficients. Furthermore, for initial distributions other than the stationary one, geometric ergodicity implies ... More
Subgeometrically ergodic autoregressionsApr 15 2019In this paper we discuss how the notion of subgeometric ergodicity in Markov chain theory can be exploited to study the stability of nonlinear time series models. Subgeometric ergodicity means that the transition probability measures converge to the stationary ... More
Subgeometrically ergodic autoregressionsApr 15 2019Apr 16 2019In this paper we discuss how the notion of subgeometric ergodicity in Markov chain theory can be exploited to study the stability of nonlinear time series models. Subgeometric ergodicity means that the transition probability measures converge to the stationary ... More
Estimation of Cross-Sectional Dependence in Large PanelsApr 15 2019Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross{sectional dependence) together often results ... More
Peer Effects in Random Consideration SetsApr 14 2019This paper develops a dynamic model of discrete choice that incorporates peer effects into consideration sets. We characterize equilibrium behavior and study the empirical content of the dynamic model we offer. In our set-up, the choices of friends act ... More
Complex Network Construction of Internet Financial riskApr 14 2019Internet finance is a new financial model that applies Internet technology to payment, capital borrowing and lending and transaction processing. In order to study the internal risks, this paper uses the Internet financial risk elements as the network ... More
Complex Network Construction of Internet Financial riskApr 14 2019Apr 19 2019Internet finance is a new financial model that applies Internet technology to payment, capital borrowing and lending and transaction processing. In order to study the internal risks, this paper uses the Internet financial risk elements as the network ... More
Pólygamma Data Augmentation to address Non-conjugacy in the Bayesian Estimation of Mixed Multinomial Logit ModelsApr 13 2019The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this ... More
Distribution Regression in Duration Analysis: an Application to Unemployment SpellsApr 12 2019This article proposes estimation and inference procedures for distribution regression models with randomly right-censored data. The proposal generalizes classical duration models to a situation where slope coefficients can vary with the elapsed duration, ... More
Identification of Noncausal Models by Quantile AutoregressionsApr 11 2019We propose a model selection criterion to detect purely causal from purely noncausal models in the framework of quantile autoregressions (QAR). We also present asymptotics for the i.i.d. case with regularly varying distributed innovations in QAR. This ... More
On the construction of confidence intervals for ratios of expectationsApr 10 2019In econometrics, many parameters of interest can be written as ratios of expectations. The main approach to construct confidence intervals for such parameters is the delta method. However, this asymptotic procedure yields intervals that may not be relevant ... More
Solving Dynamic Discrete Choice Models Using Smoothing and Sieve MethodsApr 10 2019We propose to combine smoothing, simulations and sieve approximations to solve for either the integrated or expected value function in a general class of dynamic discrete choice (DDC) models. We use importance sampling to approximate the Bellman operators ... More
Local Polynomial Estimation of Time-Varying Parameters in Nonlinear ModelsApr 10 2019We develop a novel asymptotic theory for local polynomial (quasi-) maximum-likelihood estimators of time-varying parameters in a broad class of nonlinear time series models. Under weak regularity conditions, we show the proposed estimators are consistent ... More
Binary Choice Models with High-Dimensional Individual and Time Fixed EffectsApr 08 2019Empirical economists are often deterred from the application of binary choice models with fixed effects mainly for two reasons: the incidental parameter bias and the computational challenge in (moderately) large data sets. We show how both issues can ... More
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based EvaluationsApr 07 2019Apr 12 2019Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially ... More
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based EvaluationsApr 07 2019Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially ... More
Prudent case-based prediction when experience is lackingApr 05 2019An inexperienced predictor is asked to qualitatively rank eventualities according to their plausibility, given past cases. Inexperience means that, resampling past cases (with replacement) fails to generate a suitably diverse set of rankings. (4-diversity ... More
Synthetic learner: model-free inference on treatments over timeApr 02 2019Understanding of the effect of a particular treatment or a policy pertains to many areas of interest -- ranging from political economics, marketing to health-care and personalized treatment studies. In this paper, we develop a non-parametric, model-free ... More
Identification and Estimation of Nonseparable Models with Multivalued Endogeneity and a Binary InstrumentApr 02 2019In this paper, I show that a nonseparable model where the endogenous variable is multivalued can be point-identified even when the instrument (IV) is only binary. Though the order condition generally fails in this case, I show that exogenous covariates ... More
Counterfactual Sensitivity and RobustnessApr 01 2019Apr 15 2019Researchers frequently make parametric assumptions about the distribution of unobservables when formulating structural models. Such assumptions are typically motived by computational convenience rather than economic theory and are often untestable. Counterfactuals ... More
Post-Selection Inference in Three-Dimensional Panel DataMar 30 2019Apr 30 2019Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification ... More
Post-Selection Inference in Three-Dimensional Panel DataMar 30 2019Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification ... More
Inference in high-dimensional set-identified affine modelsMar 29 2019This paper proposes both point-wise and uniform confidence sets (CS) for an element $\theta_{1}$ of a parameter vector $\theta\in\mathbb{R}^{d}$ that is partially identified by affine moment equality and inequality conditions. The method is based on an ... More
Measuring Differences in Stochastic Network StructureMar 26 2019Mar 31 2019How can one determine whether the realized differences between two stochastic networks are statistically significant? This paper considers a two-sample goodness-of-fit testing problem for network data in which the null hypothesis is that the networks ... More
Measuring Differences in Stochastic Network StructureMar 26 2019How can one determine whether the realized differences between two stochastic networks are statistically significant? This paper considers a two-sample goodness-of-fit testing problem for network data in which the null hypothesis is that the corresponding ... More