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Weak identification with many instruments Econom. J. (IF 1.9) Pub Date : 2024-02-24 Anna Mikusheva, Liyang Sun
Linear instrumental variable regressions are widely used to estimate causal effects. Many instruments arise from the use of “technical” instruments and more recently from the empirical strategy of “judge design”. This paper surveys and summarizes ideas from recent literature on estimation and statistical inferences with many instruments for a single endogenous regressor. We discuss how to assess the
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Threshold nonlinearities and the democracy-growth nexus Econom. J. (IF 1.9) Pub Date : 2024-02-21 Chaoyi Chen, Thanasis Stengos
This paper investigates the relationship between democracy and economic growth in the context of a linear index threshold regression model. We first introduce the baseline model with endogeneity and propose a two-step smoothed GMM estimation method. We establish the consistency and derive the asymptotic distributions of the proposed estimators. We then extend the approach to a dynamic panel context
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The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies Econom. J. (IF 1.9) Pub Date : 2024-02-07 Anna Baiardi, Andrea A Naghi
A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with
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Vaccination policy and mortality from COVID-19 in the European Union Econom. J. (IF 1.9) Pub Date : 2024-02-01 Eleonora Agostini, Francesco Bloise, Massimiliano Tancioni
This paper estimates the dynamic effect of vaccination on mortality from COVID-19 using weekly data from 26 European Union countries during 2021. Our analysis relies on the double machine learning method to control for multiple confounders, including nonpharmaceutical interventions, climate variables, mobility factors, variants of concern, country- and week-specific shocks. In our baseline specification
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Design-based identification with formula instruments: A review Econom. J. (IF 1.9) Pub Date : 2024-01-28 Kirill Borusyak, Peter Hull, Xavier Jaravel
Many studies in economics use instruments or treatments which combine a set of exogenous shocks with other predetermined variables by a known formula. Examples include shift-share instruments and measures of social or spatial spillovers. We review recent econometric tools for this setting, which leverage the assignment process of the exogenous shocks and the structure of the formula for identification
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Asymptotic properties of endogeneity corrections using nonlinear transformations Econom. J. (IF 1.9) Pub Date : 2024-01-17 Jörg Breitung, Alexander Mayer, Dominik Wied
This paper studies the asymptotic properties of endogeneity corrections based on nonlinear transformations without external instruments, which were originally proposed by Park and Gupta (2012) and have become popular in applied research. In contrast to the original copula-based estimator, our approach is based on a nonparametric control function and does not require a conformably specified copula.
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Estimating spot volatility under infinite variation jumps with dependent market microstructure noise Econom. J. (IF 1.9) Pub Date : 2024-01-11 Qiang Liu, Zhi Liu
Jumps and market microstructure noise are stylized features of high-frequency financial data. It is well known that they introduce bias in the estimation of volatility (including integrated and spot volatilities) of assets, and many methods have been proposed to deal with this problem. When the jumps are intensive with infinite variation, the efficient estimation of spot volatility under serially dependent
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Ignoring Measurement Errors in Social Networks Econom. J. (IF 1.9) Pub Date : 2023-12-28 Arthur Lewbel, Xi Qu, Xun Tang
We consider peer effect estimation in social network models where some network links are incorrectly measured. We show that if the number or magnitude of mismeasured links does not grow too quickly with the sample size, then standard instrumental variables estimators that ignore these measurement errors remain consistent, and standard asymptotic inference methods remain valid. These results hold even
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A New Method for Generating Random Correlation Matrices Econom. J. (IF 1.9) Pub Date : 2023-12-22 Ilya Archakov, Peter Reinhard Hansen, Yiyao Luo
We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, γ = g(C), which maps any distribution on $\mathbb {R}^{n(n-1)/2}$ to a distribution on the space of non-singular n × n correlation matrices. Correlation matrices with certain properties, such as being well-conditioned, having
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Spatial differencing for sample selection models with ‘site-specific’ unobserved local effects Econom. J. (IF 1.9) Pub Date : 2023-11-29 Alexander Klein, Guy Tchuente
This paper proposes an estimator which combines spatial differencing with a two-step sample selection estimator. We derive identification, estimation, and inference results from ’site-specific’ unobserved effects. These effects operate at a spatial scale that cannot be captured by administrative borders. Therefore, we use spatial differencing. We show that under justifiable assumptions, the estimator
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A new test for unit roots with a partial quadratic trend Econom. J. (IF 1.9) Pub Date : 2023-11-23 Yanglin Li, Shaoping Wang, Sainan Jin, Zhijie Xiao
This paper proposes a new test for unit root processes with a partial quadratic trend on an unknown break date, denoted as the URQ process herein. Such a process is extremely similar to the explosive bubble process and both can capture the sharp rise in prices. We develop the asymptotic distributions under the local-to-unity hypothesis which covers the URQ null and explosive root alternatives. Simulations
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Constructing High Frequency Economic Indicators by Imputation Econom. J. (IF 1.9) Pub Date : 2023-11-15 Serena Ng, Susannah Scanlan
Monthly and weekly economic indicators are often taken to be the largest common factor estimated from high and low frequency data, either separately or jointly. To incorporate mixed frequency information without directly modeling them, we target a low frequency diffusion index that is already available, and treat high frequency values as missing. We impute these values using multiple factors estimated
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The Vector Error Correction Index Model: Representation, Estimation and Identification Econom. J. (IF 1.9) Pub Date : 2023-10-24 Gianluca Cubadda, Marco Mazzali
This paper extends the multivariate index autoregressive model by Reinsel (1983) to the case of cointegrated time series of order (1,1). In this new modelling, namely the Vector Error-Correction Index Model (VECIM), the first differences of series are driven by some linear combinations of the variables, namely the indexes. When the indexes are significantly fewer than the variables, the VECIM achieves
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Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics Econom. J. (IF 1.9) Pub Date : 2023-10-10 Rahul Singh, Liyang Sun
We propose a semiparametric test to evaluate (a) whether different instruments induce subpopulations of compliers with the same observable characteristics, on average; and (b) whether compliers have observable characteristics that are the same as the full population, treated subpopulation, or untreated subpopulation, on average. The test is a flexible robustness check for the external validity of instruments
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Penalized quasi-likelihood estimation and model selection with parameters on the boundary of the parameter space Econom. J. (IF 1.9) Pub Date : 2023-10-02 Heino Bohn Nielsen, Anders Rahbek
We consider here penalized likelihood-based estimation and model selection applied to econometric time series models, which allow for non-negativity (boundary) constraints on some or all of the parameters. We establish that joint model selection and estimation result in standard asymptotic Gaussian distributed estimators. The results contrasts with non-penalized estimation, which as well-known leads
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Revealing priors from posteriors with an application to inflation forecasting in the UK Econom. J. (IF 1.9) Pub Date : 2023-10-02 Masako Ikefuji, Jan R Magnus, Takashi Yamagata
A Bayesian typically uses data and a prior to produce a posterior. We shall follow the opposite route, using data and the posterior information to reveal the prior. We then apply this theory to inflation forecasts by the Bank of England and the National Institute of Economic and Social Research in an attempt to get some insight into the prior beliefs of the policy makers in these two institutions,
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Identifying the elasticity of substitution with biased technical change - a structural panel GMM estimator Econom. J. (IF 1.9) Pub Date : 2023-09-29 Thomas von Brasch, Arvid Raknerud, Trond C Vigtel
This paper provides a structural panel GMM (P-GMM) estimator of the elasticity of substitution between capital and labour that does not depend on external instruments, and which can be applied in the presence of biased technical change. We identify the conditions under which P-GMM is a consistent estimator and compare it to a fixed effects estimator. Using a Monte Carlo study, we find that the P-GMM
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Estimation of Large Covariance Matrices with Mixed Factor Structures Econom. J. (IF 1.9) Pub Date : 2023-09-28 Runyu Dai, Yoshimasa Uematsu, Yasumasa Matsuda
We extend the Principal Orthogonal complEment Thresholding (POET) framework by Fan, J., Y. Liao, M. Mincheva (2013) to estimate large covariance matrices with a “mixed” structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of much slowly divergent eigenvalues of the covariance matrix
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Simple approaches to nonlinear difference-in-differences with panel data Econom. J. (IF 1.9) Pub Date : 2023-08-25 Jeffrey M Wooldridge
I derive simple, flexible strategies for difference-in-differences settings where the nature of the response variable may warrant a nonlinear model. I allow for general staggered interventions, with and without covariates. Under an index version of parallel trends, I show that average treatment effects on the treated (ATTs) are identified for each cohort and calendar time period in which a cohort was
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Instrumental variable quantile regression under random right censoring Econom. J. (IF 1.9) Pub Date : 2023-07-27 Jad Beyhum, Lorenzo Tedesco, Ingrid Van Keilegom
This paper studies a semiparametric quantile regression model with endogenous variables and random right censoring. The endogeneity issue is solved using instrumental variables. It is assumed that the structural quantile of the logarithm of the outcome variable is linear in the covariates and censoring is independent. The regressors and instruments can be either continuous or discrete. The specification
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Augmented two-step estimating equations with nuisance functionals and complex survey data Econom. J. (IF 1.9) Pub Date : 2023-07-21 Puying Zhao, Changbao Wu
Statistical inference in the presence of nuisance functionals with complex survey data is an important topic in social and economic studies. The Gini index, Lorenz curves and quantile shares are among the commonly encountered examples. The nuisance functionals are usually handled by a plug-in nonparametric estimator and the main inferential procedure can be carried out through a two-step generalized
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It is never too LATE: A new look at local average treatment effects with or without defiers Econom. J. (IF 1.9) Pub Date : 2023-07-13 Christian M Dahl, Martin Huber, Giovanni Mellace
Summary: In heterogeneous treatment effect models with endogeneity, identification of the local average treatment effect (LATE) typically relies on the availability of an exogenous instrument monotonically related to treatment participation. First, we demonstrate that a strictly weaker local monotonicity condition—invoked for specific potential outcome values rather than globally—identifies the LATEs
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Three-way gravity models with multiplicative unobserved effects Econom. J. (IF 1.9) Pub Date : 2023-05-06 Yimin Yang, Huili Zhang
This paper investigates three-way gravity models with multiplicative unobserved effects, which are popular in bilateral trade analysis and many other contexts. Such models are usually estimated by the fixed effects Poisson pseudo maximum likelihood method. As an alternative, we extend the estimation strategy proposed by Jochmans (2017 to our new settings by constructing moment conditions that are independent
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A first-stage representation for instrumental variables quantile regression Econom. J. (IF 1.9) Pub Date : 2023-04-04 Javier Alejo, Antonio F Galvao, Gabriel Montes-Rojas
This paper develops a first-stage linear regression representation for an instrumental variables (IV) quantile regression (QR) model. The quantile first-stage is analogous to the least squares case, i.e., a linear projection of the endogenous variables on the instruments and other exogenous covariates, with the difference that the QR case is a weighted projection. The weights are given by the conditional
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Using Information Criteria to Select Averages in CCE Econom. J. (IF 1.9) Pub Date : 2023-04-04 Luca Margaritella, Joakim Westerlund
In the interactive effects panel data literature information criteria are commonly used to consistently determine which of the estimated principal components factors to include. The present paper shows that the same approach can be applied to factors estimated by taking the cross-sectional averages of the observables, as prescribed by the popular common correlated effects (CCE) approach. This should
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Model selection for varying coefficient nonparametric transformation model Econom. J. (IF 1.9) Pub Date : 2023-03-11 Xiao Zhang, Xu Liu, Xingjie Shi
Based on the smoothed partial rank (SPR) loss function, we propose a group LASSO penalized SPR estimator for the varying coefficient nonparametric transformation models, and derive its estimation and model selection consistencies. It not only selects important variables but is also able to select between varying and constant coefficients. To deal with the computational challenges in the rank loss function
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Causal Inference and Data Fusion in Econometrics Econom. J. (IF 1.9) Pub Date : 2023-03-11 Paul Hünermund, Elias Bareinboim
Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available about the phenomenon under investigation. For instance, unobserved confounding factors threaten the internal validity
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Testing for parameter change epochs in GARCH time series Econom. J. (IF 1.9) Pub Date : 2023-02-02 Stefan Richter, Weining Wang, Wei Biao Wu
We develop a uniform test for detecting and dating the integrated or mildly explosive behaviour of a strictly stationary generalized autoregressive conditional heteroskedasticity (GARCH) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters against the alternative that there is an “abnormal” period with changed parameter values. During this period
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Choosing exogeneity assumptions in potential outcome models Econom. J. (IF 1.9) Pub Date : 2023-01-27 Matthew A Masten, Alexandre Poirier
There are many kinds of exogeneity assumptions. How should researchers choose among them? When exogeneity is imposed on an unobservable like a potential outcome, we argue that the form of exogeneity should be chosen based on the kind of selection on unobservables it allows. Consequently, researchers can assess the plausibility of any exogeneity assumption by studying the distributions of treatment
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Disentangling the effect of measures, variants and vaccines on SARS-CoV-2 Infections in England: A dynamic intensity model Econom. J. (IF 1.9) Pub Date : 2023-01-24 Otilia Boldea, Adriana Cornea-Madeira, João Madeira
In this paper, we estimate the path of daily SARS-CoV-2 infections in England from the beginning of the pandemic until the end of 2021. We employ a dynamic intensity model, where the mean intensity conditional on the past depends both on past intensity of infections and past realised infections. The model parameters are time-varying and we employ a multiplicative specification along with logistic transition
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Estimation of high-dimensional vector autoregression via sparse precision matrix Econom. J. (IF 1.9) Pub Date : 2023-01-12 Benjamin Poignard, Manabu Asai
We consider the problem of estimating sparse Vector Autoregression (VAR) via penalized precision matrix. This matrix is the output of the underlying directed acyclic graph of the VAR process, whose zero components correspond to the zero coefficients of the graphical representation of the VAR. The sparsity-based precision matrix estimator is deduced from the D-trace loss with convex and non-convex penalty
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A nonparametric test for cooperation in discrete games Econom. J. (IF 1.9) Pub Date : 2023-01-08 Andrés Aradillas-López, Lidia Kosenkova
We propose a nonparametric test for cooperative behavior among players in discrete, static games. Assuming that certain exchangeability conditions hold if we match observable characteristics across all players, we obtain testable implications for cooperative (coalitional) behavior, which we define as occurring when players maximize an unknown joint objective function that is symmetric conditional on
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Nonparametric Identification of Random Coefficients in Aggregate Demand Models for Differentiated Products Econom. J. (IF 1.9) Pub Date : 2023-01-04 Fabian Dunker, Stefan Hoderlein, Hiroaki Kaido
This paper studies nonparametric identification in market level demand models for differentiated products with heterogeneous consumers. We consider a general class of models that allows for the individual-specific coefficients to vary continuously across the population and give conditions under which the density of these coefficients, and hence also functionals such as the fractions of individuals
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Inference in regression discontinuity designs with high-dimensional covariates Econom. J. (IF 1.9) Pub Date : 2022-12-22 Alexander Kreiss, C Rothe
Summary We study regression discontinuity designs in which many predetermined covariates, possibly much more than the number of observations, can be used to increase the precision of treatment effect estimates. We consider a two-step estimator which first selects a small number of ‘important’ covariates through a localised lasso-type procedure, and then, in a second step, estimates the treatment effect
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Feasible IV regression without excluded instruments Econom. J. (IF 1.9) Pub Date : 2022-12-12 Emmanuel Selorm Tsyawo
The relevance condition of Integrated Conditional Moment (ICM) estimators is significantly weaker than the conventional IV’s in at least two respects: (1) consistent estimation without excluded instruments is possible, provided endogenous covariates are non-linearly mean-dependent on exogenous covariates, and (2) endogenous covariates may be uncorrelated with but mean-dependent on instruments. These
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IV estimation of spatial dynamic panels with interactive effects: Large sample theory and an application on bank attitude toward risk Econom. J. (IF 1.9) Pub Date : 2022-11-22 Guowei Cui, Vasilis Sarafidis, Takashi Yamagata
This paper develops a new Instrumental Variables estimator for spatial, dynamic panels with interactive effects under large N and T asymptotics. For this class of models, most approaches available in the literature are based on quasi-maximum likelihood estimation. The approach put forward here is appealing from both a theoretical and a practical point of view for a number of reasons. Firstly, it is
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Semiparametric inference on Gini indices of two semicontinuous populations under density ratio models Econom. J. (IF 1.9) Pub Date : 2022-11-22 Meng Yuan, Pengfei Li, Changbao Wu
The Gini index is a popular inequality measure with many applications in social and economic studies. This paper studies semiparametric inference on the Gini indices of two semicontinuous populations. We characterize the distribution of each semicontinuous population by a mixture of a discrete point mass at zero and a continuous skewed positive component. A semiparametric density ratio model is then
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Comparing latent inequality with ordinal data Econom. J. (IF 1.9) Pub Date : 2022-11-22 David M Kaplan, Wei Zhao
We propose new ways to compare two latent distributions when only ordinal data are available and without imposing parametric assumptions on the underlying continuous distributions. First, we contribute identification results. We show how certain ordinal conditions provide evidence of between-group inequality, quantified by particular quantiles being higher in one latent distribution than in the other
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Feasible weighted projected principal component analysis for semiparametric factor models Econom. J. (IF 1.9) Pub Date : 2022-11-22 Sung Hoon Choi
Various factor estimation procedures have been developed, based on the latent factor model. They often consider general conditions that allow for correlations and heteroskedasticity. However, the conventional principal components (PC) method does not efficiently estimate the parameters. It also does not accommodate additional covariates, which explain the unknown factors, even if they are available
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Testing for quantile sample selection Econom. J. (IF 1.9) Pub Date : 2022-11-22 Valentina Corradi, Daniel Gutknecht
This paper provides distribution free tests for detecting sample selection in conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. In the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test using only individuals with propensity score close
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Dynamic demand for differentiated products with fixed-effects unobserved heterogeneity Econom. J. (IF 1.9) Pub Date : 2022-10-16 Victor Aguirregabiria
This paper studies identification and estimation of a dynamic discrete choice model of demand for differentiated product using consumer-level panel data with few purchase events per consumer (i.e., short panel). Consumers are forward-looking and their preferences incorporate two sources of dynamics: last choice dependence due to habits and switching costs, and duration dependence due to inventory,
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Matching with semi-bandits Econom. J. (IF 1.9) Pub Date : 2022-09-26 Maximilian Kasy, Alexander Teytelboym
Summary We consider an experimental setting in which a matching of resources to participants has to be chosen repeatedly and returns from the individual chosen matches are unknown, but can be learned. Our setting covers two-sided and one-sided matching with (potentially complex) capacity constraints, such as refugee resettlement, social housing allocation, and foster care. We propose a variant of the
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Combining counterfactual outcomes and ARIMA models for policy evaluation Econom. J. (IF 1.9) Pub Date : 2022-09-24 Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series settings. None of these makes use of ARIMA models, which are instead very common in the econometrics literature. In this paper, we propose a novel approach
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Explicit minimal representation of variance matrices, and its implication for dynamic volatility models Econom. J. (IF 1.9) Pub Date : 2022-08-26 Karim M Abadir
We propose a minimal representation of variance matrices of dimension k, where parameterization and positive-definiteness conditions are both explicit. Then, we apply it to the specification of dynamic multivariate volatility processes. Compared to the most parsimonious unrestricted formulation currently available, the required number of covariance parameters (hence processes) is reduced by about a
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Asymptotic Properties of the Maximum Likelihood Estimator in Regime-Switching Models with Time-Varying Transition Probabilities Econom. J. (IF 1.9) Pub Date : 2022-08-19 Chaojun Li, Yan Liu
Time-varying transition probability (TVTP) regime-switching models extend the constant regime transition probability in Markov-switching models to include information from observations. We show consistency and asymptotic normality of the maximum likelihood estimator (MLE) in general TVTP regime-switching models where the conditional distribution of Yt depends on lagged regimes. Consistency of the MLE
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Bubble testing under polynomial trends Econom. J. (IF 1.9) Pub Date : 2022-07-22 Xiaohu Wang, Jun Yu
This paper develops the asymptotic theory of the least squares estimator of the autoregressive (AR) coefficient in an AR(1) regression with intercept when data is generated from a polynomial trend model in different forms. It is shown that the commonly used right-tailed unit root tests tend to favor the explosive alternative. A new procedure, which implements the right-tailed unit root tests in an
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Doubly robust identification for causal panel data models Econom. J. (IF 1.9) Pub Date : 2022-06-24 Dmitry Arkhangelsky, Guido W Imbens
We study identification and estimation of causal effects in settings with panel data. Traditionally researchers follow model-based identification strategies relying on assumptions governing the relation between the potential outcomes and the observed and unobserved confounders. We focus on a different, complementary approach to identification where assumptions are made about the connection between
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Double machine learning based program evaluation under unconfoundedness Econom. J. (IF 1.9) Pub Date : 2022-06-01 Michael C Knaus
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. An
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Effects of Covid-19 Lockdowns on Social Distancing in Turkey Econom. J. (IF 1.9) Pub Date : 2022-05-23 Fırat Bilgel
This paper elucidates the causal effect of lockdowns on social distancing behaviour in Turkey by adopting an augmented synthetic control and a factor-augmented model approach for imputing counterfactuals. By constructing a synthetic control group that reproduces pre-lockdown trajectory of mobility of the treated provinces and that accommodates staggered adoption, the difference between the counterfactual
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Equilibrium Multiplicity in Dynamic Games: Testing and Estimation Econom. J. (IF 1.9) Pub Date : 2022-04-30 Taisuke Otsu, Martin Pesendorfer
This paper surveys the recent literature on dynamic games estimation when there is a concern of equilibrium multiplicity. We focus on the questions of testing for equilibrium multiplicity and estimation in the presence of multiplicity.
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Debiased machine learning of global and local parameters using regularized Riesz representers Econom. J. (IF 1.9) Pub Date : 2022-04-23 Victor Chernozhukov,Whitney K Newey,Rahul Singh
Summary We provide adaptive inference methods, based on $\ell _1$ regularization, for regular (semiparametric) and nonregular (nonparametric) linear functionals of the conditional expectation function. Examples of regular functionals include average treatment effects, policy effects, and derivatives. Examples of nonregular functionals include average treatment effects, policy effects, and derivatives
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CCE in heterogenous fixed-T panels Econom. J. (IF 1.9) Pub Date : 2022-03-30 Joakim Westerlund, Yousef Kaddoura
Summary The present paper shows that the CCE approach of Pesaran (2006) is more useful than commonly appreciated, in that it enables consistent and asymptotically normal estimation of interactive effects models with heterogeneous slope coefficients when the number of time periods, T, is fixed and only the number of cross-sectional units, N, is large.
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On reduced form estimation of the effect of policy interventions on the COVID-19 pandemic Econom. J. (IF 1.9) Pub Date : 2022-03-29 Ivan Korolev
Several studies have estimated the effects of various non-pharmaceutical interventions on the COVID-19 pandemic using a “reduced form” approach. In this paper, I show that many different SIR (Susceptible, Infectious, Recovered) models can generate virtually identical dynamics of the number of reported cases during the early stages of the epidemic and lead to the same reduced form estimates. In some
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Relative Contagiousness of Emerging Virus Variants: An Analysis of the Alpha, Delta, and Omicron SARS-CoV-2 Variants Econom. J. (IF 1.9) Pub Date : 2022-03-25 Peter Reinhard Hansen
We propose a simple dynamic model for estimating the relative contagiousness of two virus variants. Maximum likelihood estimation and inference is conveniently invariant to variation in the total number of cases over the sample period and can be expressed as a logistic regression. We apply the model to Danish SARS-CoV-2 variant data. We estimate the reproduction numbers of Alpha and Delta to be larger
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Estimation and inference on treatment effects under treatment-based sampling designs Econom. J. (IF 1.9) Pub Date : 2022-03-25 Kyungchul Song, Zhengfei Yu
Summary Causal inference in a programme evaluation setting faces the problem of external validity when the treatment effect in the target population is different from the treatment effect identified from the population of which the sample is representative. This paper focuses on a situation where such discrepancy arises by a stratified sampling design based on the individual treatment status and other
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The triple difference estimator Econom. J. (IF 1.9) Pub Date : 2022-03-09 Andreas Olden, Jarle Men
Triple difference has become a widely used estimator in empirical work. A close reading of articles in top economics journals reveals that the use of the estimator to a large extent rests on intuition. The identifying assumptions are neither formally derived nor generally agreed on. We give a complete presentation of the triple difference estimator, and show that even though the estimator can be computed
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Algorithms for inference in SVARs identified with sign and zero restrictions Econom. J. (IF 1.9) Pub Date : 2022-02-16 Matthew Read
I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms
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Distribution regression in duration analysis: an application to unemployment spells Econom. J. (IF 1.9) Pub Date : 2022-02-12 Miguel A Delgado, Andrés García-Suaza, Pedro H C Sant’Anna
This article proposes inference procedures for distribution regression models in duration analysis using randomly right-censored data. This generalizes classical duration models by allowing situations where explanatory variables’ marginal effects freely vary with duration time. The article discusses applications to testing uniform restrictions on the varying coefficients, inferences on average marginal
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Causal mediation analysis with double machine learning Econom. J. (IF 1.9) Pub Date : 2022-01-31 Helmut Farbmacher, Martin Huber, Lukáš Lafférs, Henrika Langen, Martin Spindler
Summary This paper combines causal mediation analysis with double machine learning for a data-driven control of observed confounders in a high-dimensional setting. The average indirect effect of a binary treatment and the unmediated direct effect are estimated based on efficient score functions, which are robust with respect to misspecifications of the outcome, mediator, and treatment models. This
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Estimating the SARS-CoV-2 infection fatality rate by data combination: The case of Germany’s first wave Econom. J. (IF 1.9) Pub Date : 2022-01-29 Thomas Dimpfl, Jantje Sönksen, Ingo Bechmann, Joachim Grammig
Summary Assessing the infection fatality rate (IFR) of SARS-CoV-2 in a population is a controversial issue. Due to asymptomatic courses of COVID-19, many infections remain undetected. Reported case fatality rates are therefore poor estimates of the IFR. We propose a strategy to estimate the IFR that combines official data on cases and fatalities with data from seroepidemiological studies in infection