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  • 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

    更新日期:2020-07-01
  • 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

    更新日期:2020-06-23
  • 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

    更新日期:2020-06-23
  • 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

    更新日期:2020-06-17
  • Accelerated failure time models with log-concave errors
    Econom. J. (IF 2.139) Pub Date : 
    Liu R, Yu Z.

    SummaryWe study accelerated failure time models in which the survivor function of the additive error term is log-concave. The log-concavity assumption covers large families of commonly used distributions and also represents the aging or wear-out phenomenon of the baseline duration. For right-censored failure time data, we construct semiparametric maximum likelihood estimates of the finite-dimensional

    更新日期:2020-06-09
  • 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

    更新日期:2020-06-06
  • Two-way Exclusion Restrictions in Models with Heterogeneous Treatment Effects
    Econom. J. (IF 2.139) Pub Date : 2020-06-01
    Shenglong Liu; Ismael Mourifié; Yuanyuan Wan

    In 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

    更新日期:2020-06-01
  • 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

    更新日期:2020-02-14
  • 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

    更新日期:2020-02-04
  • 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

    更新日期:2020-01-29
  • 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.

    更新日期:2020-01-25
  • 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

    更新日期:2019-12-20
  • 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

    更新日期:2019-11-13
  • 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

    更新日期:2019-11-12
  • Peer effects in bedtime decisions among adolescents: a social network model with sampled data.
    Econom. J. (IF 2.139) Pub Date : 2017-10-01
    Xiaodong Liu,Eleonora Patacchini,Edoardo Rainone

    Using unique information on a representative sample of US teenagers, we investigate peer effects in adolescent bedtime decisions. We extend the nonlinear least-squares estimator for spatial autoregressive models to estimate network models with network fixed effects and sampled observations on the dependent variable. We show the extent to which neglecting the sampling issue yields misleading inferential

    更新日期:2019-11-01
  • My friend far, far away: a random field approach to exponential random graph models.
    Econom. J. (IF 2.139) Pub Date : 2017-10-01
    Vincent Boucher,Ismael Mourifié

    We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a set of parameters from the individuals' utility functions using the observation of a single, but large, social network. We show that, under some conditions, a simple logit-based estimator is coherent, consistent

    更新日期:2019-11-01
  • An instrumental variable random-coefficients model for binary outcomes.
    Econom. J. (IF 2.139) Pub Date : 2015-03-24
    Andrew Chesher,Adam M Rosen

    In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint

    更新日期:2019-11-01
  • More reliable inference for the dissimilarity index of segregation.
    Econom. J. (IF 2.139) Pub Date : 2015-02-01
    Rebecca Allen,Simon Burgess,Russell Davidson,Frank Windmeijer

    The most widely used measure of segregation is the so-called dissimilarity index. It is now well understood that this measure also reflects randomness in the allocation of individuals to units (i.e. it measures deviations from evenness, not deviations from randomness). This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there

    更新日期:2019-11-01
  • 更新日期:2019-11-01
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