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Policy evaluation with multiple instrumental variables Journal of Econometrics (IF 6.3) Pub Date : 2024-03-14 Magne Mogstad, Alexander Torgovitsky, Christopher R. Walters
Marginal treatment effect methods are widely used for causal inference and policy evaluation with instrumental variables. However, they fundamentally rely on the well-known monotonicity (threshold-crossing) condition on treatment choice behavior. This condition cannot hold with multiple instruments unless treatment choice is effectively homogeneous. We develop a new marginal treatment effect framework
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Efficiency bounds for moment condition models with mixed identification strength Journal of Econometrics (IF 6.3) Pub Date : 2024-03-11 Prosper Dovonon, Yves F. Atchadé, Firmin Doko Tchatoka
Moment condition models with mixed identification strength are models that are point identified but with estimating moment functions that are allowed to drift to 0 uniformly over the parameter space. Even though identification fails in the limit, depending on how slow the moment functions vanish, consistent estimation is possible. Existing estimators such as the generalized method of moment (GMM) estimator
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Score-type tests for normal mixtures Journal of Econometrics (IF 6.3) Pub Date : 2024-03-11 Dante Amengual, Xinyue Bei, Marine Carrasco, Enrique Sentana
Testing normality against discrete normal mixtures is complex because some parameters turn increasingly underidentified along alternative ways of approaching the null, others are inequality constrained, and several higher-order derivatives become identically 0. These problems make the maximum of the alternative model log-likelihood function numerically unreliable. We propose score-type tests asymptotically
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No star is good news: A unified look at rerandomization based on [formula omitted]-values from covariate balance tests Journal of Econometrics (IF 6.3) Pub Date : 2024-03-11 Anqi Zhao, Peng Ding
Randomized experiments balance all covariates on average and are considered the gold standard for estimating treatment effects. Chance imbalances are nonetheless common in realized treatment allocations. To inform readers of the comparability of treatment groups at baseline, contemporary scientific publications often report covariate balance tables with not only covariate means by treatment group but
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Predictive ability tests with possibly overlapping models Journal of Econometrics (IF 6.3) Pub Date : 2024-03-11 Valentina Corradi, Jack Fosten, Daniel Gutknecht
This paper provides novel tests for comparing out-of-sample predictive ability of two or more competing models that are possibly overlapping. The tests do not require pre-testing, they allow for dynamic misspecification and are valid under different estimation schemes and loss functions. In pairwise model comparisons, the test is constructed by adding a random perturbation to both the numerator and
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Bayesian estimation of cluster covariance matrices of unknown form Journal of Econometrics (IF 6.3) Pub Date : 2024-03-07 Drew Creal, Jaeho Kim
We develop a flexible Bayesian model for cluster covariance matrices in large dimensions where the number of clusters and the assignment of cross-sectional units to a cluster are a-priori unknown and estimated from the data. In a cluster covariance matrix, the variances and covariances are equal within each diagonal block, while the covariances are equal in each off-diagonal block. This reduces the
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State-dependent local projections Journal of Econometrics (IF 6.3) Pub Date : 2024-02-27 Sílvia Gonçalves, Ana María Herrera, Lutz Kilian, Elena Pesavento
Do state-dependent local projections asymptotically recover the population responses of macroeconomic aggregates to structural shocks? The answer to this question depends on how the state of the economy is determined and on the magnitude of the shocks. When the state is exogenous, the local projection estimator recovers the population response regardless of the shock size. When the state depends on
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Dealing with imperfect randomization: Inference for the highscope perry preschool program Journal of Econometrics (IF 6.3) Pub Date : 2024-02-23 James Heckman, Rodrigo Pinto, Azeem M. Shaikh
This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach
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Robust inference on correlation under general heterogeneity Journal of Econometrics (IF 6.3) Pub Date : 2024-02-22 Liudas Giraitis, Yufei Li, Peter C.B. Phillips
Considerable evidence in past research shows size distortion in standard tests for zero autocorrelation or zero cross-correlation when time series are not independent identically distributed random variables, pointing to the need for more robust procedures. Recent tests for serial correlation and cross-correlation in Dalla, Giraitis, and Phillips (2022) provide a more robust approach, allowing for
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Finite underidentification Journal of Econometrics (IF 6.3) Pub Date : 2024-02-20 E, n, r, i, q, u, e, , S, e, n, t, a, n, a
I adapt the Generalised Method of Moments to deal with nonlinear models in which a finite number of isolated parameter values satisfy the moment conditions. I also study the closely related class of first-order underidentified models, whose expected Jacobian is rank deficient but not necessarily zero. In both cases, my proposed procedures exploit the underidentification structure to yield parameter
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Bias in local projections Journal of Econometrics (IF 6.3) Pub Date : 2024-02-19 Edward P. Herbst, Benjamin K. Johannsen
Local projections (LPs) are a popular tool in macroeconomic research. We show that LPs are often used with very small samples in the time dimension and, consequently, that LP point estimates can be severely biased. Under regularity conditions, we derive simple expressions to approximate this bias and propose a way to correct for bias in LPs. Using a medium-scale macroeconomic time-series model, we
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Nonparametric estimation for high-frequency data incorporating trading information Journal of Econometrics (IF 6.3) Pub Date : 2024-02-17 Wenhao Cui, Jie Hu, Jiandong Wang
We propose nonparametric estimators for the explicative part of the noise in a model where the market microstructure noise is an unknown function of the trading information while allowing for the presence of an additional residual noise component. Our method allows for dependence in the observable trading information and accommodates the presence of infinite variation jumps in the efficient price process
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Classical [formula omitted]-values and the Bayesian posterior probability that the hypothesis is approximately true Journal of Econometrics (IF 6.3) Pub Date : 2024-02-15 B, r, e, n, d, a, n, , K, l, i, n, e
This paper relates -values for the hypothesis that to the Bayesian posterior probability that the hypothesis is approximately true, in the sense that for a selected . In a setup with a continuous prior for , the results show that a larger (respectively, smaller) -value does not necessarily correspond to a higher (respectively, lower) probability that is close to . Therefore, the results suggest caution
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Time-varying forecast combination for factor-augmented regressions with smooth structural changes Journal of Econometrics (IF 6.3) Pub Date : 2024-02-14 Qitong Chen, Yongmiao Hong, Haiqi Li
This study proposes a time-varying forecast combination for factor-augmented (TVFCFA) regressions with smooth structural changes. First, we establish the limiting distribution of the estimators of the time-varying factor-augmented regressions. To estimate the optimal time-varying combination weights, we propose a local leave--out cross-validation (LLOCV) criterion that is asymptotically unbiased for
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Identifying the effects of a program offer with an application to Head Start Journal of Econometrics (IF 6.3) Pub Date : 2024-02-12 V, i, s, h, a, l, , K, a, m, a, t
I propose a treatment selection model that introduces unobserved heterogeneity in both choice sets and preferences to evaluate the average effects of a program offer. I show how to exploit the model structure to define parameters capturing these effects and then computationally characterize their identified sets under instrumental variable variation in choice sets. I illustrate these tools by analyzing
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Non-representative sampled networks: Estimation of network structural properties by weighting Journal of Econometrics (IF 6.3) Pub Date : 2024-02-10 Chih-Sheng Hsieh, Yu-Chin Hsu, Stanley I.M. Ko, Jaromír Kovářík, Trevon D. Logan
This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability
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The variance of regression coefficients when the population is finite Journal of Econometrics (IF 6.3) Pub Date : 2024-02-10 Richard Startz, Douglas G. Steigerwald
Recent work has returned attention to the role of finite-population corrections in empirical settings. It is well established that if the only source of variation arises from the sampling design, then the asymptotic variance of regression estimators must include the proportion of the finite population that is sampled. If there is, in addition, a random shock to each element of the finite population
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When uncertainty and volatility are disconnected: Implications for asset pricing and portfolio performance Journal of Econometrics (IF 6.3) Pub Date : 2024-02-09 Yacine Aït-Sahalia, Felix Matthys, Emilio Osambela, Ronnie Sircar
We analyze an environment where the uncertainty in the equity market return and its volatility are both stochastic and may be potentially disconnected. We solve a representative investor’s optimal asset allocation and derive the resulting conditional equity premium and risk-free rate in equilibrium. Our empirical analysis shows that the equity premium appears to be earned for facing uncertainty, especially
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High frequency principal component analysis based on correlation matrix that is robust to jumps, microstructure noise and asynchronous observation times Journal of Econometrics (IF 6.3) Pub Date : 2024-02-09 D, a, c, h, u, a, n, , C, h, e, n
This paper developed the high frequency estimation for the principal component analysis (PCA) based on correlation matrix. This estimation methodology is robust to jumps, microstructure noise and asynchronous observation times simultaneously, which is enabled by the newly proposed Truncated and Smoothed Two-Scales Realized Volatility (Truncated S-TSRV) estimator. The general framework of our methodology
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Confidence intervals of treatment effects in panel data models with interactive fixed effects Journal of Econometrics (IF 6.3) Pub Date : 2024-02-07 Xingyu Li, Yan Shen, Qiankun Zhou
We augment the factor-based estimation of treatment effects proposed by Bai and Ng (2021) with easy-to-implement and nonparametric confidence intervals of treatment effects on every treated unit at every post-treatment time. The construction of confidence intervals entails a residual-based bootstrap resampling procedure. This method does not rely on any parametric assumption on the distribution of
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Whitney Newey’s contributions to econometrics Journal of Econometrics (IF 6.3) Pub Date : 2024-02-06 Alberto Abadie, Joshua Angrist, Guido Imbens
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Fast inference for quantile regression with tens of millions of observations Journal of Econometrics (IF 6.3) Pub Date : 2024-02-05 Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin
Big data analytics has opened new avenues in economic research, but the challenge of analyzing datasets with tens of millions of observations is substantial. Conventional econometric methods based on extreme estimators require large amounts of computing resources and memory, which are often not readily available. In this paper, we focus on linear quantile regression applied to “ultra-large” datasets
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Eliciting willingness-to-pay to decompose beliefs and preferences that determine selection into competition in lab experiments Journal of Econometrics (IF 6.3) Pub Date : 2024-01-30 Yvonne Jie Chen, Deniz Dutz, Li Li, Sarah Moon, Edward Vytlacil, Songfa Zhong
This paper develops a partial-identification methodology for analyzing self-selection into alternative compensation schemes in a laboratory environment. We formulate a model of self-selection in which individuals select the compensation scheme with the largest expected valuation, which depends on individual- and scheme-specific beliefs and non-monetary preferences. We characterize the resulting sharp
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Inference on quantile processes with a finite number of clusters Journal of Econometrics (IF 6.3) Pub Date : 2024-02-02 Andreas Hagemann
I introduce a generic method for inference on entire quantile and regression quantile processes in the presence of a finite number of large and arbitrarily heterogeneous clusters. The method asymptotically controls size by generating statistics that exhibit enough distributional symmetry such that randomization tests can be applied. The randomization test does not require ex-ante matching of clusters
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Panel quantile regression for extreme risk Journal of Econometrics (IF 6.3) Pub Date : 2024-02-01 Yanxi Hou, Xuan Leng, Liang Peng, Yinggang Zhou
Panel quantile regression models play an essential role in finance, insurance, and risk management applications. However, a direct application of panel regression for extreme conditional quantiles may suffer from a significant estimation uncertainty due to data sparsity on the far tail. We introduce a two-stage method to predict extreme conditional quantiles over cross-sections, which uses panel quantile
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Distributional counterfactual analysis in high-dimensional setup Journal of Econometrics (IF 6.3) Pub Date : 2024-02-01 Ricardo Masini
In the context of treatment effect estimation, this paper proposes a new methodology to recover the counterfactual distribution when there is a single (or a few) treated unit and possibly a high-dimensional number of potential controls observed in a panel structure. The methodology accommodates, albeit does not require, the number of units to be larger than the number of time periods (high-dimensional
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Unconditional quantile partial effects via conditional quantile regression Journal of Econometrics (IF 6.3) Pub Date : 2024-02-01 Javier Alejo, Antonio F. Galvao, Julian Martinez-Iriarte, Gabriel Montes-Rojas
This paper develops a semi-parametric procedure for estimation of unconditional quantile partial effects using quantile regression coefficients. The estimator is based on an identification result showing that, for continuous covariates, unconditional quantile effects are a weighted average of conditional ones at particular quantile levels that depend on the covariates. We propose a two-step estimator
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Enhanced pricing and management of bundled insurance risks with dependence-aware prediction using pair copula construction Journal of Econometrics (IF 6.3) Pub Date : 2024-02-01 Peng Shi, Zifeng Zhao
We propose a dependence-aware predictive modeling framework for multivariate risks stemmed from an insurance contract with bundling features — an important type of policy increasingly offered by major insurance companies. The bundling feature naturally leads to longitudinal measurements of multiple insurance risks, and correct pricing and management of such risks is of fundamental interest to financial
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Identification of a rational inattention discrete choice model Journal of Econometrics (IF 6.3) Pub Date : 2024-01-29 M, o, y, u, , L, i, a, o
This paper studies the non-parametric identification and estimation of an empirical rational inattention discrete choice model. Decision-makers do not observe their realized utility perfectly but can obtain a costly signal to inform them about the utility. This model nests the standard discrete choice model as a special case. We characterize the identified set of decision-makers’ prior beliefs based
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Panel data models with time-varying latent group structures Journal of Econometrics (IF 6.3) Pub Date : 2024-01-28 Yiren Wang, Peter C.B. Phillips, Liangjun Su
This paper considers a linear panel model with interactive fixed effects and unobserved individual and time heterogeneities that are captured by some latent group structures and an unknown structural break, respectively. To enhance realism, the model may have different numbers of groups and/or different group memberships before and after the break. With preliminary nuclear norm regularized estimation
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Inference for low-rank completion without sample splitting with application to treatment effect estimation Journal of Econometrics (IF 6.3) Pub Date : 2024-01-26 Jungjun Choi, Hyukjun Kwon, Yuan Liao
This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper
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Robust inference for moment condition models without rational expectations Journal of Econometrics (IF 6.3) Pub Date : 2024-01-25 Xiaohong Chen, Lars Peter Hansen, Peter G. Hansen
Applied researchers using structural models under rational expectations (RE) often confront empirical evidence of misspecification. In this paper we consider a generic dynamic model that is posed as a vector of unconditional moment restrictions. We suppose that the model is globally misspecified under RE, and thus empirically flawed in a way that is not econometrically subtle. We relax the RE restriction
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A computational approach to identification of treatment effects for policy evaluation Journal of Econometrics (IF 6.3) Pub Date : 2024-01-24 Sukjin Han, Shenshen Yang
For counterfactual policy evaluation, it is important to ensure that treatment parameters are relevant to policies in question. This is especially challenging under unobserved heterogeneity, as is well featured in the definition of the local average treatment effect (LATE). Being intrinsically local, the LATE is known to lack external validity in counterfactual environments. This paper investigates
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Volatility of volatility and leverage effect from options Journal of Econometrics (IF 6.3) Pub Date : 2024-01-22 Carsten H. Chong, Viktor Todorov
We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options’ expiration and we use these estimates to recover spot volatility. Our volatility of volatility
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Cross-section bootstrap for CCE regressions Journal of Econometrics (IF 6.3) Pub Date : 2024-01-16 Ignace De Vos, Ovidijus Stauskas
The Common Correlated Effects (CCE) methodology is now well established for the analysis of factor-augmented panel data models. Yet, it is often neglected that the pooled variant is biased unless the cross-section dimension () of the dataset dominates the time series length (). This is problematic for inference with typical macroeconomic datasets, where is often equal or larger than . In response,
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Phase transitions in nonparametric regressions Journal of Econometrics (IF 6.3) Pub Date : 2024-01-09 Ying Zhu
When the unknown regression function of a single variable is known to have derivatives up to the (γ+1)th order bounded in absolute values by a common constant everywhere or a.e. (i.e., (γ+1)th degree of smoothness), the minimax optimal rate of the mean integrated squared error (MISE) is stated as 1n2γ+22γ+3 in the literature. This paper shows that: (i) if n≤γ+12γ+3, the minimax optimal MISE rate is
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Reprint of: When will Arctic sea ice disappear? Projections of area, extent, thickness, and volume Journal of Econometrics (IF 6.3) Pub Date : 2024-01-09 Francis X. Diebold, Glenn D. Rudebusch, Maximilian Göbel, Philippe Goulet Coulombe, Boyuan Zhang
Rapidly diminishing Arctic summer sea ice is a strong signal of the pace of global climate change. We provide point, interval, and density forecasts for four measures of Arctic sea ice: area, extent, thickness, and volume. Importantly, we enforce the joint constraint that these measures must simultaneously arrive at an ice-free Arctic. We apply this constrained joint forecast procedure to models relating
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Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic Journal of Econometrics (IF 6.3) Pub Date : 2024-01-09 Xu Guo, Runze Li, Jingyuan Liu, Mudong Zeng
Mediation analysis draws increasing attention in many research areas such as economics, finance and social sciences. In this paper, we propose new statistical inference procedures for high dimensional mediation models, in which both the outcome model and the mediator model are linear with high dimensional mediators. Traditional procedures for mediation analysis cannot be used to make statistical inference
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Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective Journal of Econometrics (IF 6.3) Pub Date : 2024-01-08 Weichen Wang, Ran An, Ziwei Zhu
Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic
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Locally robust inference for non-Gaussian linear simultaneous equations models Journal of Econometrics (IF 6.3) Pub Date : 2024-01-09 Adam Lee, Geert Mesters
All parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distortions when the true distributions of the shocks are close to Gaussian. To address this problem we develop
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Introduction to the Themed Issue on Climate Econometrics Journal of Econometrics (IF 6.3) Pub Date : 2024-01-07 J. Isaac Miller, Felix Pretis
Abstract not available
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Observation-driven filtering of time-varying parameters using moment conditions Journal of Econometrics (IF 6.3) Pub Date : 2024-01-08 Drew Creal, Siem Jan Koopman, André Lucas, Marcin Zamojski
We develop a new and flexible semi-parametric approach for time-varying parameter models when the true dynamics are unknown. The time-varying parameters are estimated using a recursive updating scheme that is driven by the influence function of a conditional moments-based criterion. We show that the updates ensure local improvements of the conditional criterion function in expectation. The dynamics
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Quantile analysis of “hazard-rate” game models Journal of Econometrics (IF 6.3) Pub Date : 2024-01-06 Andreea Enache, Jean-Pierre Florens
This paper consists of an econometric analysis of a broad class of games of incomplete information. In these games, a player’s action depends both on her unobservable characteristic (the private information), as well as on the ratio of the distribution of the unobservable characteristic and its density function (which we call the ”hazard-rate”). The goal is to use data on players’ actions to recover
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Likelihood approach to dynamic panel models with interactive effects Journal of Econometrics (IF 6.3) Pub Date : 2024-01-06 J, u, s, h, a, n, , B, a, i
This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small ) and long panels (large ) are considered. A dynamic panel forms a simultaneous-equation system, and under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information
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Identification of time-varying counterfactual parameters in nonlinear panel models Journal of Econometrics (IF 6.3) Pub Date : 2024-01-05 Irene Botosaru, Chris Muris
We develop a general framework for the identification of counterfactual parameters in a class of nonlinear semiparametric panel models with fixed effects and time effects. Our method applies to models for discrete outcomes (e.g., two-way fixed effects binary choice) or continuous outcomes (e.g., censored regression), with discrete or continuous regressors. Our results do not require parametric assumptions
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The likelihood ratio test for structural changes in factor models Journal of Econometrics (IF 6.3) Pub Date : 2024-01-02 Jushan Bai, Jiangtao Duan, Xu Han
A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but with a change in the variance of its factors. This approach effectively transforms a high-dimensional structural change problem into a low-dimensional problem. This paper considers the likelihood ratio (LR) test for a variance change in the estimated factors. The LR test implicitly
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Data science in economics and finance: Introduction Journal of Econometrics (IF 6.3) Pub Date : 2023-12-27 Matias D. Cattaneo, Yingying Fan, Runze Li, Rui Song
Abstract not available
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Nonparametric estimation of stochastic frontier models with weak separability Journal of Econometrics (IF 6.3) Pub Date : 2023-12-26 Samuele Centorrino, Christopher F. Parmeter
We propose a robust and versatile approach to estimate the stochastic frontier model which avoids parametric assumptions. Our approach requires a single continuous covariate which monotonically influences the conditional mean of inefficiency. Subject to these conditions, the frontier and the conditional mean of inefficiency can be estimated nonparametrically. The estimator we propose uses local least
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Semiparametric Bayesian estimation of dynamic discrete choice models Journal of Econometrics (IF 6.3) Pub Date : 2023-12-28 Andriy Norets, Kenichi Shimizu
We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extreme value distributions with varying numbers of mixture components. Our approach exploits the analytical tractability of extreme value distributions in the multinomial choice settings
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Bipartite network influence analysis of a two-mode network Journal of Econometrics (IF 6.3) Pub Date : 2023-12-27 Yujia Wu, Wei Lan, Xinyan Fan, Kuangnan Fang
A two-mode network contains two types of nodes, and edges exist only between any two nodes that are associated with different entities. Owing to the network connections (i.e., edges) between the two types of network nodes, nodal responses are unlikely to be independently and identically distributed, resulting in possible nodal heterogeneity across the two types of nodes. This study proposes a novel
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Estimation and inference by stochastic optimization Journal of Econometrics (IF 6.3) Pub Date : 2023-12-26 Jean-Jacques Forneron
In non-linear estimations, it is common to assess sampling uncertainty by bootstrap inference. For complex models, this can be computationally intensive. This paper combines optimization with resampling: turning stochastic optimization into a fast resampling device. Two methods are introduced: a resampled Newton–Raphson (rnr) and a resampled quasi-Newton (rqn) algorithm. Both produce draws that can
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Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails Journal of Econometrics (IF 6.3) Pub Date : 2023-12-23 Juan Antolín-Díaz, Thomas Drechsel, Ivan Petrella
A key question for households, firms, and policy makers is: how is the economy doing now? This paper develops a Bayesian dynamic factor model that allows for nonlinearities, heterogeneous lead–lag patterns and fat tails in macroeconomic data. Explicitly modeling these features changes the way that different indicators contribute to the real-time assessment of the state of the economy, and substantially
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Identification of heterogeneous elasticities in gross-output production functions Journal of Econometrics (IF 6.3) Pub Date : 2023-12-23 Tong Li, Yuya Sasaki
This paper presents the identification of heterogeneous elasticities in gross-output production functions with non-separable unobserved productivity. We propose that the ex-ante flexible input cost shares identify the heterogeneous output elasticities with respect to flexible inputs for each firm. Applying the proposed method to a panel of firms in the food production industry in Chile, we find that
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Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change Journal of Econometrics (IF 6.3) Pub Date : 2023-12-22 Xiyu Jiao, Felix Pretis, Moritz Schwarz
Outlying observations can bias regression estimates, requiring the use of outlier-robust estimators. Comparing robust estimates to those obtained using ordinary least squares (OLS) is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal test for coefficient distortion due to outliers in regression models. Our proposed
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Loss aversion and the welfare ranking of policy interventions Journal of Econometrics (IF 6.3) Pub Date : 2023-12-21 Sergio Firpo, Antonio F. Galvao, Martyna Kobus, Thomas Parker, Pedro Rosa-Dias
This paper develops theoretical criteria and econometric methods to rank policy interventions in terms of welfare when individuals are loss-averse. Our new criterion for “loss aversion-sensitive dominance” defines a weak partial ordering of the distributions of policy-induced gains and losses. It applies to the class of welfare functions which model individual preferences with non-decreasing and loss-averse
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Identification and estimation of sequential games of incomplete information with multiple equilibria Journal of Econometrics (IF 6.3) Pub Date : 2023-12-19 Jangsu Yoon
This paper discusses the identification and estimation of game-theoretic models, mainly focusing on sequential games of incomplete information. In most empirical games, researchers cannot observe the exact order of actions played in the game and rely on the assumption of simultaneous actions. My structural modeling generalizes an empirical game to encompass simultaneous and sequential actions as special
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Bellman filtering and smoothing for state–space models Journal of Econometrics (IF 6.3) Pub Date : 2023-12-19 Rutger-Jan Lange
This paper presents a new filter for state–space models based on Bellman’s dynamic-programming principle, allowing for nonlinearity, non-Gaussianity and degeneracy in the observation and/or state-transition equations. The resulting Bellman filter is a direct generalisation of the (iterated and extended) Kalman filter, enabling scalability to higher dimensions while remaining computationally inexpensive
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Distributed estimation and inference for spatial autoregression model with large scale networks Journal of Econometrics (IF 6.3) Pub Date : 2023-12-14 Yimeng Ren, Zhe Li, Xuening Zhu, Yuan Gao, Hansheng Wang
The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This