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  • Higher-order income dynamics with linked regression trees
    Econom. J. (IF 2.139) Pub Date : 2020-08-29
    Jeppe Druedahl; Anders Munk-Nielsen

    We propose a novel method for modelling income processes using machine learning. Our method links age-specific regression trees, and returns a discrete state process, which can easily be included in consumption-saving models without further discretizations. A central advantage of our approach is that it does not rely on any parametric assumptions, and because we build on existing machine learning tools

  • Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden
    Econom. J. (IF 2.139) Pub Date : 2020-08-29
    Sang-Wook (Stanley) Cho

    This paper estimates the effect of nonpharmaceutical intervention policies on public health during the COVID-19 outbreak by considering a counterfactual case for Sweden. Using a synthetic control approach, I find that strict initial lockdown measures play an important role in limiting the spread of the COVID-19 infection, as the infection cases in Sweden would have been reduced by almost 75 percent

  • Machine learning and structural econometrics: contrasts and synergies
    Econom. J. (IF 2.139) Pub Date : 2020-08-29
    Fedor Iskhakov; John Rust; Bertel Schjerning

    We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018, ‘Methodology and Applications of Structural Dynamic Models and Machine

  • Two-way exclusion restrictions in models with heterogeneous treatment effects
    Econom. J. (IF 2.139) Pub Date : 2020-06-01
    Liu S, Mourifié I, Wan Y.

    SummaryIn this paper, we propose a novel method to identify the conditional average treatment effect partial derivative (CATE-PD) in an environment in which the treatment is endogenous, the treatment effect is heterogeneous, the candidate 'instrumental variables' can be correlated with latent errors, and the treatment selection does not need to be (weakly) monotone. We show that CATE-PD is point-identified

  • Model Averaging Estimation for High-dimensional Covariance Matrix with a Network Structure
    Econom. J. (IF 2.139) Pub Date : 2020-09-29
    Rong Zhu; Xinyu Zhang; Yanyuan Ma; Guohua Zou

    In this paper, we develop a model averaging method to estimate the high-dimensional covariance matrix, where the candidate models are constructed by different orders of the polynomial functions. We propose a Mallows-type model averaging criterion and select the weights by minimizing this criterion, which is an unbiased estimator of the expected in-sample squared error plus a constant. Then, we prove

  • Online Estimation of DSGE Models*
    Econom. J. (IF 2.139) Pub Date : 2020-09-21
    Michael Cai; Marco Del Negro; Edward Herbst; Ethan Matlin; Reca Sarfati; Frank Schorfheide

    This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating DSGE model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for “online” estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that

  • Estimation of dynamic models of recurrent events with censored data
    Econom. J. (IF 2.139) Pub Date : 2020-09-09
    Sanghyeok Lee; Tue Gørgens

    In this paper we consider estimation of dynamic models of recurrent events (event histories) in continuous time using censored data. We develop maximum simulated likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out this unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments

  • Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions
    Econom. J. (IF 2.139) Pub Date : 2020-08-29
    Vira Semenova; Victor Chernozhukov

    This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools. We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate

  • A Simple Estimator for Quantile Panel Data Models Using Smoothed Quantile Regressions
    Econom. J. (IF 2.139) Pub Date : 2020-08-05
    Liang Chen; Yulong Huo

    Canay (2011)’s two-step estimator of quantile panel data models, due to its simple intuition and low computational cost, has been widely used in empirical studies in recent years. However, in the asymptotic analysis of Canay (2011), the bias of his estimator due to the estimation of the fixed effects is mistakenly omitted, and such omission will lead to invalid inference on the coefficients. To solve

  • Potential Outcomes and Finite Population Inference for M-estimators
    Econom. J. (IF 2.139) Pub Date : 2020-07-18
    Ruonan Xu

    When a sample is drawn from or coincides with a finite population, the uncertainty of the coefficient estimators is often reported assuming the population is effectively infinite. Abadie, Athey, Imbens, and Wooldridge (2020), who study finite population inference for linear regression, derive an alternative asymptotic variance of the least squares estimator. I extend their results to the more general

  • Panel VAR Models with Interactive Fixed Effects
    Econom. J. (IF 2.139) Pub Date : 2020-07-17
    Mustafa Tuğan

    The existing literature on panel models with interactive fixed effects have the common feature of modeling a univariate variable. In this regard, it is incapable of addressing dynamic and simultaneous interactions among a set of macroeconomic variables, which falls in the realm of structural analysis. This paper aims to contribute to the existing literature by studying a homogeneous panel vector autoregression

  • Identifying and Estimating the Effects of Unconventional Monetary Policy: How to Do It And What Have We Learned?
    Econom. J. (IF 2.139) Pub Date : 2020-07-16
    Barbara Rossi

    The recent financial crisis led central banks to lower their interest rates in order to stimulate the economy until they hit the zero lower bound. How should one identify monetary policy shocks in unconventional times? Are unconventional monetary policies as effective as conventional ones? And has the monetary policy transmission mechanism changed in the zero lower bound era? This article aims at providing

  • Erratum to: Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models
    Econom. J. (IF 2.139) Pub Date : 2020-07-01
    Banerjee A, Chevillon G, Kratz M.

    SummaryWe propose a near-explosive random coefficient autoregressive model (NERC) to obtain predictive probabilities of the apparition and devolution of bubbles. The distribution of the autoregressive coefficient of this model is allowed to be centred at an O(T−α) distance of unity, with α ∈ (0, 1). When the expectation of the autoregressive coefficient lies on the explosive side of unity, the NERC

  • Binary Classification with Covariate Selection through L0-Penalized Empirical Risk Minimization
    Econom. J. (IF 2.139) Pub Date : 2020-06-20
    Le-Yu Chen; Sokbae Lee

    We consider the problem of binary classification with covariate selection. We construct a classification procedure by minimizing the empirical misclassification risk with a penalty on the number of selected covariates. This optimization problem is equivalent to obtaining an ℓ0-penalized maximum score estimator. We derive probability bounds on the estimated sparsity as well as on the excess misclassification

  • Identifying present bias and time preferences with an application to land-lease-contract data
    Econom. J. (IF 2.139) Pub Date : 2020-06-23
    Pieter A Gautier; Aico van Vuuren

    What can contracts—traded and priced in a competitive market and featuring a pre-specified system of future payments—teach us about time preferences and present bias? We first show that identification of present bias requires assumptions on the felicity function and that agents must have credit constraints on consumption expenditure. Moreover, when there is heterogeneity in present bias, identification

  • Identification of a class of index models: A topological approach*
    Econom. J. (IF 2.139) Pub Date : 2020-06-17
    Mogens Fosgerau; Dennis Kristensen

    We establish nonparametric identification in a class of so-called index models using a novel approach that relies on general topological results. Our proof strategy requires substantially weaker conditions on the functions and distributions characterizing the model compared to existing strategies; in particular, it does not require any large support conditions on the regressors of our model. We apply

  • Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence
    Econom. J. (IF 2.139) Pub Date : 2020-06-06
    Michael C Knaus; Michael Lechner; Anthony Strittmatter

    We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data in an observational setting. We consider 24 different DGPs, eleven different causal machine learning estimators, and three

  • Comparing deep neural network and econometric approaches to predicting the impact of climate change on agricultural yield
    Econom. J. (IF 2.139) Pub Date : 2020-05-30
    Michael Keane; Timothy Neal

    Predicting the impact of climate change on crop yield is difficult, in part because the production function mapping weather to yield is high dimensional and nonlinear. We compare three approaches to predicting yields: (a) deep neural networks (DNNs), (b) traditional panel-data models, and (c) a new panel-data model that allows for unit and time fixed effects in both intercepts and slopes in the agricultural

  • Testing Identification via Heteroskedasticity in Structural Vector Autoregressive Models
    Econom. J. (IF 2.139) Pub Date : 2020-04-15
    Helmut Lütkepohl; Mika Meitz; Aleksei Netšunajev; Pentti Saikkonen

    Tests for identification through heteroskedasticity in structural vector autoregressive analysis are developed for models with two volatility states where the time point of volatility change is known. The tests are Wald type tests for which only the unrestricted model including the covariance matrices of the two volatility states have to be estimated. The residuals of the model are assumed to be from

  • Generalised Forecast Averaging in Autoregressions with a Near Unit Root
    Econom. J. (IF 2.139) Pub Date : 2020-04-01
    Mohitosh Kejriwal; Xuewen Yu

    This paper develops a new approach to forecasting a highly persistent time series that employs feasible generalized least squares (FGLS) estimation of the deterministic components in conjunction with Mallows model averaging.

  • Probabilistic forecasting of bubbles and flash crashes
    Econom. J. (IF 2.139) Pub Date : 2020-02-14
    Anurag Banerjee; Guillaume Chevillon; Marie Kratz

    We propose a near-explosive random coefficient autoregressive model (NERC) to obtain predictive probabilities of the apparition and devolution of bubbles. The distribution of the autoregressive coefficient of this model is allowed to be centred at an O(T−α) distance of unity, with α ∈ (0, 1). When the expectation of the autoregressive coefficient lies on the explosive side of unity, the NERC helps

  • Double/debiased machine learning for difference-in-differences models
    Econom. J. (IF 2.139) Pub Date : 2020-02-04
    Neng-Chieh Chang

    This paper provides an orthogonal extension of the semiparametric difference-in-differences estimator proposed in earlier literature. The proposed estimator enjoys the so-called Neyman orthogonality (Chernozhukov et al., 2018), and thus it allows researchers to flexibly use a rich set of machine learning methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional

  • Multilayer network analysis of oil linkages
    Econom. J. (IF 2.139) Pub Date : 2020-01-29
    Roberto Casarin; Matteo Iacopini; German Molina; Enrique ter Horst; Ramon Espinasa; Carlos Sucre; Roberto Rigobon

    This manuscript proposes a new approach for unveiling existing linkages within the international oil market across multiple driving factors beyond production. A multilayer, multicountry network is extracted through a novel Bayesian graphical vector autoregressive model, which allows for a more comprehensive, dynamic representation of the network linkages than do traditional or static pairwise Granger-causal

  • Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals
    Econom. J. (IF 2.139) Pub Date : 2020-01-25
    Yang He; Otávio Bartalotti

    This paper develops a novel wild bootstrap procedure to construct robust bias-corrected valid confidence intervals for fuzzy regression discontinuity designs, providing an intuitive complement to existing robust bias-corrected methods. The confidence intervals generated by this procedure are valid under conditions similar to the procedures proposed by Calonico et al. (2014) and related literature.

  • Partial identification in nonseparable count data instrumental variable models
    Econom. J. (IF 2.139) Pub Date : 2019-12-20
    Dongwoo Kim

    This paper investigates undesirable limitations of widely used count data instrumental variable models. To overcome the limitations, I propose a partially identifying single-equation model that requires neither strong separability of unobserved heterogeneity nor a triangular system. Sharp bounds (identified sets) of structural features are characterised by conditional moment inequalities. Numerical

  • The ignorant monopolist redux
    Econom. J. (IF 2.139) Pub Date : 2019-11-13
    Roger Koenker

    The classical problem of the monopolist faced with an unknown demand curve is considered in a simple stochastic setting. Sequential pricing strategies designed to maximize discounted profits are shown to converge sufficiently rapidly that they leave the monopolist ignorant about all but the most local features of demand. The failure of the monopolist to 'learn' his demand curve would seem to call into

  • Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs
    Econom. J. (IF 2.139) Pub Date : 2019-11-12
    Sebastian Calonico; Matias D Cattaneo; Max H Farrell

    Modern empirical work in regression discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias-corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show that

  • Kernel estimation for panel data with heterogeneous dynamics
    Econom. J. (IF 2.139) Pub Date : 2019-10-26
    Ryo Okui; Takahide Yanagi

    This paper proposes nonparametric kernel-smoothing estimation for panel data to examine the degree of heterogeneity across cross-sectional units. We first estimate the sample mean, autocovariances, and autocorrelations for each unit and then apply kernel smoothing to compute their density functions. The dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect

  • A New Structural Break Test for Panels with Common Factors
    Econom. J. (IF 2.139) Pub Date : 2019-10-18
    Huanjun Zhu; Vasilis Sarafidis; Mervyn J Silvapulle

    This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement; more commonly so in datasets with annual time frequency, in

  • IT Outsourcing and Firm Productivity: Eliminating Bias from Selective Missingness in the Dependent Variable1
    Econom. J. (IF 2.139) Pub Date : 2019-09-20
    Christoph Breunig; Michael Kummer; Joerg Ohnemus; Steffen Viete

    Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective

  • Fragility of Identification in Panel Binary Response Models
    Econom. J. (IF 2.139) Pub Date : 2019-09-01
    Giovanni Forchini; Bin Jiang

    The present paper considers a linear binary response model for panel data with random effects that differ across individuals but are constant over time, and it investigates the roles of the various ...

  • Reconsideration of a simple approach to quantile regression for panel data
    Econom. J. (IF 2.139) Pub Date : 2019-09-01
    Galina Besstremyannaya; Sergei Golovan

    The note discusses a fallacy in the approach proposed by Ivan Canay (2011, The Econometrics Journal) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. We formally prove that the estimator gives an incorrect inference for the constant term due to violation of the assumption about additive expansion of the first-step estimator

  • Estimating Latent Group Structure in Time-Varying Coefficient Panel Data Models
    Econom. J. (IF 2.139) Pub Date : 2019-08-01
    Jia Chen

    This paper studies the estimation of latent group structures in heterogeneous time-varying coefficient panel data models. While allowing the coefficient functions to vary over cross sections provides a good way to model cross-sectional heterogeneity, it reduces the degree of freedom and leads to poor estimation accuracy when the time-series length is short. On the other hand, in a lot of empirical

  • BLP-2LASSO for Aggregate Discrete Choice Models with Rich Covariates
    Econom. J. (IF 2.139) Pub Date : 2019-07-11
    Benjamin J Gillen; Sergio Montero; Hyungsik Roger Moon; Matthew Shum

    SummaryWe introduce the BLP-2LASSO model, which augments the classic BLP (Berry, Levinsohn, and Pakes, 1995) random-coefficients logit model to allow for data-driven selection among a high-dimensional set of control variables using the 'double-LASSO' procedure proposed by Belloni, Chernozhukov, and Hansen (2013). Economists often study consumers’ aggregate behaviour across markets choosing from a menu

  • Quantile-based Smooth Transition Value at Risk Estimation
    Econom. J. (IF 2.139) Pub Date : 2019-06-06
    Stefan Hubner; Pavel Čížek

    Value at risk models are concerned with the estimation of conditional quantiles of a time series. Formally, these quantities are a function of conditional volatility and the respective quantile of the innovation distribution. The former is often subject to asymmetric dynamic behaviour, e.g., with respect to past shocks. In this paper, we propose a model in which conditional quantiles follow a generalised

  • A Simple, Graphical Approach to Comparing Multiple Treatments
    Econom. J. (IF 2.139) Pub Date : 2019-05-01
    Brennan S Thompson; Matthew D Webb

    We propose a graphical approach to comparing multiple treatments that allows users to easily infer differences between any treatment effect and zero, and between any pair of treatment effects. Our approach makes use of a flexible, resampling-based procedure that asymptotically controls the familywise error rate (the probability of making one or more spurious inferences). We demonstrate the usefulness

  • Inferential results for a new measure of inequality
    Econom. J. (IF 2.139) Pub Date : 2019-03-19
    Youri Davydov; Francesca Greselin

    In this paper we derive inferential results for a new index of inequality, specifically defined for capturing significant changes observed both in the left and in the right tail of the income distributions. The latter shifts are an apparent fact for many countries like US, Germany, UK, and France in the last decades, and are a concern for many policy makers. We propose two empirical estimators for

  • On the Role of Covariates in the Synthetic Control Method
    Econom. J. (IF 2.139) Pub Date : 2019-01-29
    Irene Botosaru; Bruno Ferman

    This note revisits the role of time-invariant observed covariates in the Synthetic Control (SC) method. We first derive conditions under which the original result of Abadie et al (2010) regarding the bias of the SC estimator remains valid when we relax the assumption of a perfect match on observed covariates and assume only a perfect match on pre-treatment outcomes. We then show that, even when the

  • Testing Collinearity of Vector Time Series
    Econom. J. (IF 2.139) Pub Date : 2019-01-29
    Tucker S McElroy; Agnieszka Jach

    SummaryWe investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine

  • Quantile Coherency: A General Measure for Dependence between Cyclical Economic Variables
    Econom. J. (IF 2.139) Pub Date : 2019-01-29
    Jozef Baruník; Tobias Kley

    In this paper, we introduce quantile coherency to measure general dependence structures emerging in the joint distribution in the frequency domain and argue that this type of dependence is natural for economic time series but remains invisible when only the traditional analysis is employed. We define estimators which capture the general dependence structure, provide a detailed analysis of their asymptotic

  • High-dimensional Macroeconomic Forecasting and Variable Selection via Penalized Regression
    Econom. J. (IF 2.139) Pub Date : 2019-01-01
    Yoshimasa Uematsu; Shinya Tanaka

    SummaryThis study examines high-dimensional forecasting and variable selection via folded-concave penalized regressions. The penalized regression approach leads to sparse estimates of the regression coefficients and allows the dimensionality of the model to be much larger than the sample size. First, we discuss the theoretical aspects of a penalized regression in a time series setting. Specifically

  • Testing for constant correlation of filtered series under structural change
    Econom. J. (IF 2.139) Pub Date : 2019-01-01
    Matei Demetrescu; Dominik Wied

    SummaryThe paper proposes a test for constant correlations that allow for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is because incorrect standardization of the series transfers to the sample correlations onto which the tests

  • Two-Stage Least Squares as Minimum Distance
    Econom. J. (IF 2.139) Pub Date : 2019-01-01
    Frank Windmeijer

    SummaryThe two-stage least-squares (2SLS) instrumental-variables (IV) estimator for the parameters in linear models with a single endogenous variable is shown to be identical to an optimal minimum-distance (MD) estimator based on the individual instrument-specific IV estimators. The 2SLS estimator is a linear combination of the individual estimators, with the weights determined by their variances and

  • Unobserved Heterogeneity in Auctions
    Econom. J. (IF 2.139) Pub Date : 2018-12-22
    Philip A Haile; Yuichi Kitamura

    SummaryA common concern in the empirical study of auctions is the likely presence of auction-specific factors that are common knowledge among bidders but unobserved to the econometrician. Such unobserved heterogeneity confounds attempts to uncover the underlying structure of demand and information, typically a primary feature of interest in an auction market. Unobserved heterogeneity presents a particular

  • Optimal Panel Unit Root Testing with Covariates
    Econom. J. (IF 2.139) Pub Date : 2018-12-22
    Artūras Juodis; Joakim Westerlund

    SummaryThis paper provides asymptotic optimality results for panel unit root tests with covariates by deriving the Gaussian power envelope. The main conclusion is that the use of covariates holds considerable promise in the panel data context, much more so than in the time series context. In fact, the use of the covariates not only leads to increased power, but can actually have an order effect on

  • Testing for Moderate Explosiveness
    Econom. J. (IF 2.139) Pub Date : 2018-12-22
    Gangzheng Guo; Yixiao Sun; Shaoping Wang

    SummaryThis paper considers a moderately explosive AR(1) process where the autoregressive root approaches unity from the right at a certain rate. We first develop a test for the null of moderate explosiveness under independent and identically distributed errors. We show that the t statistic is asymptotically standard normal regardless of whether the true process is dominated by the stochastic moderately

  • Estimation of Graphical Lasso using the L 1,2 Norm
    Econom. J. (IF 2.139) Pub Date : 2018-09-14
    Khai Xiang Chiong; Hyungsik Roger Moon

    Gaussian graphical models are recently used in economics to obtain networks of dependence among agents. A widely-used estimator is the Graphical Lasso (GLASSO), which amounts to a maximum likelihood estimation regularized using the $L_{1,1}$ matrix norm on the precision matrix $\Omega$. The $L_{1,1}$ norm is a lasso penalty that controls for sparsity, or the number of zeros in $\Omega$. We propose

  • Identification of treatment effects with selective participation in a randomized trial
    Econom. J. (IF 2.139) Pub Date : 2018-09-14
    Brendan Kline; Elie Tamer

    Randomized trials (RTs) are used to learn about treatment effects. This paper studies identification of average treatment response (ATR) and average treatment effect (ATE) from RT data under various assumptions. The focus is the problem of external validity of the RT. RT data need not point identify the ATR or ATE because of selective participation in the RT. The paper reports partial‐identification

  • Robust Tests for Deterministic Seasonality and Seasonal Mean Shifts1
    Econom. J. (IF 2.139) Pub Date : 2018-09-13
    S. Astill; A. M. R. Taylor

    We develop tests for the presence of deterministic seasonal behaviour and seasonal mean shifts in a seasonally observed univariate time series. These tests are designed to be asymptotically robust to the order of integration of the series at both the zero and seasonal frequencies. Motivated by the approach of Hylleberg, Engle, Granger and Yoo [1990, Journal of Econometrics vol. 44, pp. 215-238], we

  • Non-parametric Bayesian Inference of Strategies in Repeated Games
    Econom. J. (IF 2.139) Pub Date : 2018-09-13
    Max Kleiman-Weiner; Joshua B. Tenenbaum; Penghui Zhou

    Inferring underlying cooperative and competitive strategies from human behaviour in repeated games is important for accurately characterizing human behaviour and understanding how people reason strategically. Finite automata, a bounded model of computation, have been extensively used to compactly represent strategies for these games and are a standard tool in game theoretic analyses. However, inference

  • CCE in panels with general unknown factors
    Econom. J. (IF 2.139) Pub Date : 2018-09-13
    Joakim Westerlund

    A popular approach to factor‐augmented panel regressions is the common correlated effects (CCE) estimator of Pesaran (2006). In fact, the approach is so popular that it has given rise to a separate CCE literature. A common assumption in this literature is that the common factors are stationary, which would seem to rule out many empirically relevant cases. Moreover, deterministic factors are typically

  • Beyond Plausibly Exogenous
    Econom. J. (IF 2.139) Pub Date : 2018-07-18
    Hans van Kippersluis; Cornelius A. Rietveld

    We synthesize two recent advances in the literature on instrumental variables (IVs) estimation that test and relax the exclusion restriction. Our approach first estimates the direct effect of the IV on the outcome in a subsample for which the IV does not affect the treatment variable. Subsequently, this estimate for the direct effect is used as input for the plausibly exogenous method developed by

  • Testing for changing volatility
    Econom. J. (IF 2.139) Pub Date : 2018-06-01
    Jilin Wu; Zhijie Xiao

    In this paper, we propose a consistent U‐statistic test with good sampling properties to detect changes in volatility. We show that the test has a limiting standard normal distribution under the null hypothesis, and that it is powerful compared with various alternatives. A Monte Carlo experiment is conducted to highlight the merits of the proposed test relative to other popular tests for structural

  • Identification and Estimation of Heteroskedastic Binary Choice Models with Endogenous Dummy Regressors
    Econom. J. (IF 2.139) Pub Date : 2018-06-01
    Beili Mu; Zhengyu Zhang

    In this paper, we consider the semiparametric identification and estimation of a heteroscedastic binary choice model with endogenous dummy regressors and no parametric restriction on the distribution of the error term. Our approach addresses various drawbacks associated with previous estimators proposed for this model. It allows for: general multiplicative heteroscedasticity in both selection and outcome

  • The wild bootstrap for few (treated) clusters
    Econom. J. (IF 2.139) Pub Date : 2018-05-06
    James G. MacKinnon; Matthew D. Webb

    Inference based on cluster-robust standard errors in linear regression models, using either the Student's t distribution or the wild cluster bootstrap, is known to fail when the number of treated clusters is very small. We propose a family of new procedures calledthe subcluster wild bootstrap, which includes the ordinary wild bootstrap as a limiting case. In the case of pure treatment models, where

  • Nonparametric inference on (conditional) quantile differences and interquantile ranges, using L -statistics
    Econom. J. (IF 2.139) Pub Date : 2018-02-07
    Matt Goldman; David M. Kaplan

    We provide novel, high‐order accurate methods for non‐parametric inference on quantile differences between two populations in both unconditional and conditional settings. These quantile differences correspond to (conditional) quantile treatment effects under (conditional) independence of a binary treatment and potential outcomes. Our methods use the probability integral transform and a Dirichlet (rather

  • Central limit theorems for conditional efficiency measures and tests of the “separability” condition in nonparametric, two-stage models of production
    Econom. J. (IF 2.139) Pub Date : 2018-01-18
    Cinzia Daraio; Léopold Simar; Paul W. Wilson

    In this paper, we demonstrate that standard central limit theorem (CLT) results do not hold for means of non‐parametric, conditional efficiency estimators, and we provide new CLTs that permit applied researchers to make valid inference about mean conditional efficiency or to compare mean efficiency across groups of producers. The new CLTs are used to develop a test of the restrictive ‘separability’

  • Double/debiased machine learning for treatment and structural parameters
    Econom. J. (IF 2.139) Pub Date : 2018-01-16
    Victor Chernozhukov; Denis Chetverikov; Mert Demirer; Esther Duflo; Christian Hansen; Whitney Newey; James Robins

    We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ in the presence of high‐dimensional nuisance parameters η. We depart from the classical setting by allowing for η to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η, we consider the use of

  • Adaptive wild bootstrap tests for a unit root with nonstationary volatility
    Econom. J. (IF 2.139) Pub Date : 2018-01-16
    H. Peter Boswijk; Yang Zu

    Recent research has emphasised that permanent changes in the innovation variance (caused by structural shifts or an integrated volatility process) lead to size distortions in conventional unit root tests. Cavaliere and Taylor (2008) show how these size distortions may be resolved using the wild bootstrap. In this paper, we first derive the asymptotic power envelope for the unit root testing problem

  • Simpler bootstrap estimation of the asymptotic variance of U -statistic-based estimators
    Econom. J. (IF 2.139) Pub Date : 2017-12-23
    Bo E. Honoré; Luojia Hu

    The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honore and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation

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