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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 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

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

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

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

On the Effect of Imputation on the 2SLS VarianceMar 26 2019Endogeneity and missing data are common issues in empirical research. We investigate how both jointly affect inference on causal parameters. Conventional methods to estimate the variance, which treat the imputed data as if it was observed in the first ... More

Time series models for realized covariance matrices based on the matrix-F distributionMar 26 2019We propose a new Conditional BEKK matrix-F (CBF) model for the time-varying realized covariance (RCOV) matrices. This CBF model is capable of capturing heavy-tailed RCOV, which is an important stylized fact but could not be handled adequately by the Wishart-based ... More

Ensemble Methods for Causal Effects in Panel Data SettingsMar 24 2019This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for ... More

Machine Learning Methods Economists Should Know AboutMar 24 2019We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods and settings between the ML literature and the traditional econometrics and statistics literatures. ... More

Identification and Estimation of a Partially Linear Regression Model using Network DataMar 22 2019I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify or fit a parametric network formation model, I introduce a new method based on matching pairs of agents with ... More

Feature quantization for parsimonious and interpretable predictive modelsMar 21 2019For regulatory and interpretability reasons, logistic regression is still widely used. To improve prediction accuracy and interpretability, a preprocessing step quantizing both continuous and categorical data is usually performed: continuous features ... More

State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionMar 19 2019How would the frontier have evolved in the absence of homestead policies? I apply a matrix completion method to predict the counterfactual time-series of frontier state capacity had there been no homesteading. In placebo tests, the matrix completion method ... More

Bayesian MIDAS Penalized Regressions: Estimation, Selection, and PredictionMar 19 2019We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. To improve the sparse recovery ability of the model, we also consider ... More

An Integrated Panel Data Approach to Modelling Economic GrowthMar 19 2019Empirical growth analysis has three major problems --- variable selection, parameter heterogeneity and cross-sectional dependence --- which are addressed independently from each other in most studies. The purpose of this study is to propose an integrated ... More

Deciding with JudgmentMar 16 2019A decision maker starts from a judgmental decision and moves to the closest boundary of the confidence interval. This statistical decision rule is admissible and does not perform worse than the judgmental decision with a probability equal to the confidence ... More

Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's EstimatorMar 15 2019We consider inference on the probability density of valuations in the first-price sealed-bid auctions model within the independent private value paradigm. We show the asymptotic normality of the two-step nonparametric estimator of Guerre, Perrigne, and ... More

Nonparametric estimation and bootstrap inference on trends in atmospheric time series: an application to ethaneMar 13 2019Understanding the development of trends and identifying trend reversals in decadal time series is becoming more and more important. Many climatological and atmospheric time series are characterized by autocorrelation, heteroskedasticity and seasonal effects. ... More

Shapley regressions: A framework for statistical inference on machine learning modelsMar 11 2019Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inference challenging and disqualifies them from many applications where model interpretability ... More

A Varying Coefficient Model for Assessing the Returns to Growth to Account for Poverty and InequalityMar 06 2019Various papers demonstrate the importance of inequality, poverty and the size of the middle class for economic growth. When explaining why these measures of the income distribution are added to the growth regression, it is often mentioned that poor people ... More

The Africa-Dummy: Gone with the Millennium?Mar 06 2019A fixed effects regression estimator is introduced that can directly identify and estimate the Africa-Dummy in one regression step so that its correct standard errors as well as correlations to other coefficients can easily be estimated. We can estimate ... More

ppmlhdfe: Fast Poisson Estimation with High-Dimensional Fixed EffectsMar 05 2019In this paper we present ppmlhdfe, a new Stata command for estimation of (pseudo) Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the Iteratively Reweighted Least Squares ... More

ppmlhdfe: Fast Poisson Estimation with High-Dimensional Fixed EffectsMar 05 2019Mar 26 2019In this paper we present ppmlhdfe, a new Stata command for estimation of (pseudo) Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares ... More

A Nonparametric Dynamic Causal Model for MacroeconometricsMar 05 2019This paper uses potential outcome time series to provide a nonparametric framework for quantifying dynamic causal effects in macroeconometrics. This provides sufficient conditions for the nonparametric identification of dynamic causal effects as well ... More

Verifying the existence of maximum likelihood estimates for generalized linear modelsMar 05 2019We expand on Santos Silva and Tenreyro (2010)'s observation that estimates from Poisson models are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models ... More

Verifying the existence of maximum likelihood estimates for generalized linear modelsMar 05 2019Apr 10 2019A fundamental problem with nonlinear estimation models is that estimates are not guaranteed to exist. However, while non-existence is a well-studied issue for binary choice models, it presents significant challenges for other models as well and is not ... More

Verifying the existence of maximum likelihood estimates for generalized linear modelsMar 05 2019Mar 21 2019We expand on Santos Silva and Tenreyro (2010)'s observation that estimates from Poisson models are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models ... More

Finite Sample Inference for the Maximum Score EstimandMar 04 2019We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Manski (1975, 1985). Our inference method is valid for any sample size and ... More

Limit Theorems for Network Dependent Random VariablesMar 04 2019This paper considers a general form of network dependence where dependence between two sets of random variables becomes weaker as their distance in a network gets longer. We show that such network dependence cannot be embedded as a random field on a lattice ... More

Model Selection in Utility-Maximizing Binary PredictionMar 02 2019The semiparametric maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning in the machine learning literature. ... More

Approximation Properties of Variational Bayes for Vector AutoregressionsMar 02 2019Variational Bayes (VB) is a recent approximate method for Bayesian inference. It has the merit of being a fast and scalable alternative to Markov Chain Monte Carlo (MCMC) but its approximation error is often unknown. In this paper, we derive the approximation ... More

Robust Nearly-Efficient Estimation of Large Panels with Factor StructuresFeb 28 2019This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS ... More

Income Effects and Rationalizability in Multinomial Choice ModelsFeb 28 2019In multinomial choice settings, Daly-Zachary (1978) and Armstrong-Vickers (2015) provided closed-form conditions, under which choice probability functions can be rationalized via random utility models. A key condition is Slutsky symmetry. We first show ... More

The Empirical Content of Binary Choice ModelsFeb 28 2019Empirical demand models used for counterfactual predictions and welfare analysis must be rationalizable, i.e. theoretically consistent with utility maximization by heterogeneous consumers. We show that for binary choice under general unobserved heterogeneity, ... More

Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection ProcedureFeb 28 2019In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection ... More

Estimation of Dynamic Panel Threshold Model using StataFeb 27 2019We develop a Stata command xthenreg to implement the first-differenced GMM estimation of the dynamic panel threshold model, which Seo and Shin (2016, Journal of Econometrics 195: 169-186) have proposed. Furthermore, We derive the asymptotic variance formula ... More

Penalized Sieve GEL for Weighted Average Derivatives of Nonparametric Quantile IV RegressionsFeb 26 2019This paper considers estimation and inference for a weighted average derivative (WAD) of a nonparametric quantile instrumental variables regression (NPQIV). NPQIV is a non-separable and nonlinear ill-posed inverse problem, which might be why there is ... More

Semiparametric estimation of heterogeneous treatment effects under the nonignorable assignment conditionFeb 26 2019We propose a semiparametric two-stage least square estimator for the heterogeneous treatment effects (HTE). HTE is the solution to certain integral equation which belongs to the class of Fredholm integral equations of the first kind, which is known to ... More

Binscatter RegressionsFeb 25 2019We introduce the \texttt{Stata} (and \texttt{R}) package \textsf{Binsreg}, which implements the binscatter methods developed in \citet*{Cattaneo-Crump-Farrell-Feng_2019_Binscatter}. The package includes the commands \texttt{binsreg}, \texttt{binsregtest}, ... More

On BinscatterFeb 25 2019Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity ... More

Robust Principal Components Analysis with Non-Sparse ErrorsFeb 23 2019We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical ... More

Counterfactual Inference in Duration Models with Random CensoringFeb 22 2019We propose a counterfactual Kaplan-Meier estimator that incorporates exogenous covariates and unobserved heterogeneity of unrestricted dimensionality in duration models with random censoring. Under some regularity conditions, we establish the joint weak ... More

Nonparametric Counterfactuals in Random Utility ModelsFeb 22 2019We bound features of counterfactual choices in the nonparametric random utility model of demand, i.e. if observable choices are repeated cross-sections and one allows for unrestricted, unobserved heterogeneity. In this setting, tight bounds are developed ... More

Have Econometric Analyses of Happiness Data Been Futile? A Simple Truth About Happiness ScalesFeb 20 2019Econometric analyses in the happiness literature typically use subjective well-being (SWB) data to compare the mean of observed or latent happiness across samples. Recent critiques show that comparing the mean of ordinal data is only valid under strong ... More

Elicitation of ambiguous beliefs with mixing betsFeb 20 2019I consider the elicitation of ambiguous beliefs about an event. I introduce a mechanism that allows to identify an interval of probabilities (representing ambiguity perception) for several classes of ambiguity averse preferences. The agent reveals her ... More

Estimation and Inference for Synthetic Control Methods with Spillover EffectsFeb 19 2019The synthetic control method is often used in treatment effect estimation with panel data where only a few units are treated and a small number of post-treatment periods are available. Current estimation and inference procedures for synthetic control ... More

Estimating Network Effects Using Naturally Occurring Peer Notification Queue CounterfactualsFeb 19 2019Randomized experiments, or A/B tests are used to estimate the causal impact of a feature on the behavior of users by creating two parallel universes in which members are simultaneously assigned to treatment and control. However, in social network settings, ... More

Discrete Choice under Risk with Limited ConsiderationFeb 18 2019This paper is concerned with learning decision makers' (DMs) preferences using data on observed choices from a finite set of risky alternatives with monetary outcomes. We propose a discrete choice model with unobserved heterogeneity in consideration sets ... More

Semiparametric correction for endogenous truncation bias with Vox Populi based participation decisionFeb 17 2019We synthesize the knowledge present in various scientific disciplines for the development of semiparametric endogenous truncation-proof algorithm, correcting for truncation bias due to endogenous self-selection. This synthesis enriches the algorithm's ... More

Weak Identification and Estimation of Social Interaction ModelsFeb 16 2019The identification of the network effect is based on either group size variation, the structure of the network or the relative position in the network. I provide easy-to-verify necessary conditions for identification of undirected network models based ... More

Partial Identification in nonparametric one-to-one matching modelsFeb 14 2019We consider the one-to-one matching models with transfers of Choo and Siow (2006) and Galichon and Salani\'e (2015). When the analyst has data on one large market only, we study identification of the systematic components of the agents' preferences without ... More

Censored Quantile Regression ForestsFeb 08 2019Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a ... More

Testing the Order of Multivariate Normal Mixture ModelsFeb 08 2019Finite mixtures of multivariate normal distributions have been widely used in empirical applications in diverse fields such as statistical genetics and statistical finance. Testing the number of components in multivariate normal mixture models is a long-standing ... More

A Bootstrap Test for the Existence of Moments for GARCH ProcessesFeb 05 2019This paper studies the joint inference on conditional volatility parameters and the innovation moments by means of bootstrap to test for the existence of moments for GARCH(p,q) processes. We propose a residual bootstrap to mimic the joint distribution ... More

A General Framework for Prediction in Time Series ModelsFeb 05 2019In this paper we propose a general framework to analyze prediction in time series models and show how a wide class of popular time series models satisfies this framework. We postulate a set of high-level assumptions, and formally verify these assumptions ... More

Asymptotic Theory for Clustered SamplesFeb 04 2019We provide a complete asymptotic distribution theory for clustered data with a large number of independent groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory ... More

A Sieve-SMM Estimator for Dynamic ModelsFeb 04 2019This paper proposes a Sieve Simulated Method of Moments (Sieve-SMM) estimator for the parameters and the distribution of the shocks in nonlinear dynamic models where the likelihood and the moments are not tractable. An important concern with SMM, which ... More

Factor Investing: Hierarchical Ensemble LearningFeb 04 2019We present a Bayesian hierarchical framework for both cross-sectional and time-series return prediction. Our approach builds on a market-timing predictive system that jointly allows for time-varying coefficients driven by fundamental characteristics. ... More

Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with LeverageJan 31 2019The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. ... More

A dynamic factor model approach to incorporate Big Data in state space models for official statisticsJan 31 2019In this paper we consider estimation of unobserved components in state space models using a dynamic factor approach to incorporate auxiliary information from high-dimensional data sources. We apply the methodology to unemployment estimation as done by ... More

Volatility Models Applied to Geophysics and High Frequency Financial Market DataJan 26 2019This work is devoted to the study of modeling geophysical and financial time series. A class of volatility models with time-varying parameters is presented to forecast the volatility of time series in a stationary environment. The modeling of stationary ... More

Orthogonal Statistical LearningJan 25 2019We provide excess risk guarantees for statistical learning in the presence of an unknown nuisance component. We analyze a two-stage sample splitting meta-algorithm that takes as input two arbitrary estimation algorithms: one for the target model and one ... More

Orthogonal Statistical LearningJan 25 2019Mar 11 2019We provide excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target model depends on an unknown model that must be to be estimated from data (a "nuisance model"). We analyze a ... More

The Wisdom of a Kalman CrowdJan 23 2019The Kalman Filter has been called one of the greatest inventions in statistics during the 20th century. Its purpose is to measure the state of a system by processing the noisy data received from different electronic sensors. In comparison, a useful resource ... More

lassopack: Model selection and prediction with regularized regression in StataJan 16 2019This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional ... More

Inference on Functionals under First Order DegeneracyJan 15 2019This paper presents a unified second order asymptotic framework for conducting inference on parameters of the form $\phi(\theta_0)$, where $\theta_0$ is unknown but can be estimated by $\hat\theta_n$, and $\phi$ is a known map that admits null first order ... More

Mastering Panel 'Metrics: Causal Impact of Democracy on GrowthJan 12 2019The relationship between democracy and economic growth is of long-standing interest. We revisit the panel data analysis of this relationship by Acemoglu, Naidu, Restrepo and Robinson (forthcoming) using state of the art econometric methods. We argue that ... More

Non-Parametric Inference Adaptive to Intrinsic DimensionJan 11 2019We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation and inference is feasible as long ... More

Non-Parametric Inference Adaptive to Intrinsic DimensionJan 11 2019Feb 26 2019We consider non-parametric estimation and inference of conditional moment models in high dimensions. We show that even when the dimension $D$ of the conditioning variable is larger than the sample size $n$, estimation and inference is feasible as long ... More

Community Matters: Heterogeneous Impacts of a Sanitation InterventionJan 11 2019We study the effectiveness of a community-level information intervention aimed at reducing open defecation (OD) and increasing sanitation investments in Nigeria. The results of a cluster-randomized control trial conducted in 246 communities between 2014 ... More

Dynamic Tail Inference with Log-Laplace VolatilityJan 08 2019Feb 26 2019We propose a family of stochastic volatility models that enable predictive estimation of time-varying extreme event probabilities in time series with nonlinear dependence and power law tails. The models are a white noise process with conditionally log-Laplace ... More

Dynamic Tail Inference with Log-Laplace VolatilityJan 08 2019Feb 05 2019We propose a family of stochastic volatility models that enable direct estimation of time-varying extreme event probabilities in time series with nonlinear dependence and power law tails. The models are a white noise process with conditionally log-Laplace ... More

Dynamic Tail Inference with Log-Laplace VolatilityJan 08 2019Mar 28 2019We propose a family of stochastic volatility models that enable predictive estimation of time-varying extreme event probabilities in time series with nonlinear dependence and power law tails. The models are a white noise process with conditionally log-Laplace ... More

Semi-parametric dynamic contextual pricingJan 07 2019We consider a canonical revenue maximization problem where customers arrive sequentially; each customer is interested in buying one product, and the customer purchases the product if her valuation for it exceeds the price set by the seller. The valuations ... More

Shrinkage for Categorical RegressorsJan 07 2019This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared ... More

Modeling Dynamic Transport Network with Matrix Factor Models: with an Application to International Trade FlowJan 02 2019International trade research plays an important role to inform trade policy and shed light on wider issues relating to poverty, development, migration, productivity, and economy. With recent advances in information technology, global and regional agencies ... More

Salvaging Falsified Instrumental Variable ModelsDec 30 2018What should researchers do when their baseline model is refuted? We provide four constructive answers. First, researchers can measure the extent of falsification. To do this, we consider continuous relaxations of the baseline assumptions of concern. We ... More

Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design ConstraintsDec 28 2018Predicting future successful designs and corresponding market opportunity is a fundamental goal of product design firms. There is accordingly a long history of quantitative approaches that aim to capture diverse consumer preferences, and then translate ... More

Decentralization Estimators for Instrumental Variable Quantile Regression ModelsDec 28 2018The instrumental variable quantile regression (IVQR) model of Chernozhukov and Hansen (2005,2006) is a flexible and powerful tool for evaluating the impact of endogenous covariates on the whole distribution of the outcome of interest. Estimation, however, ... More

Semiparametric Difference-in-Differences with Potentially Many Control VariablesDec 27 2018Jan 08 2019This paper discusses difference-in-differences (DID) estimation when there exist many control variables, potentially more than the sample size. In this case, traditional estimation methods, which require a limited number of variables, do not work. One ... More

Inference on average treatment effects in aggregate panel data settingsDec 27 2018This paper studies inference on treatment effects in aggregate panel data settings with a single treated unit and many control units. We propose new methods for making inference on average treatment effects in settings where both the number of pre-treatment ... More