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SEMIPARAMETRIC ESTIMATION OF DYNAMIC BINARY CHOICE PANEL DATA MODELS Econom. Theory (IF 0.8) Pub Date : 2024-03-11 Fu Ouyang, Thomas Tao Yang
We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model under consideration has the same random utility framework as in Honoré and Kyriazidou (2000, Econometrica 68, 839–874). We demonstrate that, with additional serial dependence conditions on the process of deterministic utility and tail restrictions
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A NONPARAMETRIC TEST OF HETEROGENEITY IN CONDITIONAL QUANTILE TREATMENT EFFECTS Econom. Theory (IF 0.8) Pub Date : 2024-03-07 Zongwu Cai, Ying Fang, Ming Lin, Shengfang Tang
This paper proposes a nonparametric test to assess whether there exist heterogeneous quantile treatment effects (QTEs) of an intervention on the outcome of interest across different sub-populations defined by covariates of interest. Specifically, a consistent test statistic based on the Cramér–von Mises type criterion is developed to test if the treatment has a constant quantile effect for all sub-populations
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COINTEGRATING POLYNOMIAL REGRESSIONS: ROBUSTNESS OF FULLY MODIFIED OLS Econom. Theory (IF 0.8) Pub Date : 2024-02-15 Oliver Stypka, Martin Wagner, Peter Grabarczyk, Rafael Kawka
Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being
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SEMIPARAMETRIC ESTIMATION AND VARIABLE SELECTION FOR SPARSE SINGLE INDEX MODELS IN INCREASING DIMENSION Econom. Theory (IF 0.8) Pub Date : 2024-02-08 Chaohua Dong, Yundong Tu
This paper considers semiparametric sieve estimation in high-dimensional single index models. The use of Hermite polynomials in approximating the unknown link function provides a convenient framework to conduct both estimation and variable selection. The estimation of the index parameter is formulated from solutions obtained by the routine penalized weighted linear regression procedure, where the weights
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ARE UNOBSERVABLES SEPARABLE? Econom. Theory (IF 0.8) Pub Date : 2024-01-26 Andrii Babii, Jean-Pierre Florens
It is common to assume in empirical research that observables and unobservables are additively separable, especially when the former are endogenous. This is because it is widely recognized that identification and estimation challenges arise when interactions between the two are allowed for. Starting from a nonseparable IV model, where the instrumental variable is independent of unobservables, we develop
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RATE-ADAPTIVE BOOTSTRAP FOR POSSIBLY MISSPECIFIED GMM Econom. Theory (IF 0.8) Pub Date : 2024-01-25 Han Hong, Jessie Li
We consider inference for possibly misspecified GMM models based on possibly nonsmooth moment conditions. While it is well known that misspecified GMM estimators with smooth moments remain $\sqrt {n}$ consistent and asymptotically normal, globally misspecified nonsmooth GMM estimators are $n^{1/3}$ consistent when either the weighting matrix is fixed or when the weighting matrix is estimated at the
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INFERENCE IN PARTIALLY IDENTIFIED PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS Econom. Theory (IF 0.8) Pub Date : 2024-01-19 Shengjie Hong, Liangjun Su, Yaqi Wang
In this paper, we develop methods for statistical inferences in a partially identified nonparametric panel data model with endogeneity and interactive fixed effects. Under some normalization rules, we can concentrate out the large-dimensional parameter vector of factor loadings and specify a set of conditional moment restrictions that are involved with only the finite-dimensional factor parameters
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ASYMPTOTICS FOR TIME-VARYING VECTOR MA() PROCESSES Econom. Theory (IF 0.8) Pub Date : 2024-01-09 Yayi Yan, Jiti Gao, Bin Peng
This paper introduces a new class of time-varying vector moving average processes of infinite order. These processes serve dual purposes: (1) they can be used to model time-varying dependence structures, and (2) they can be used to establish asymptotic theories for multivariate time series models. To illustrate these two points, we first establish some fundamental asymptotic properties and use them
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SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS WITH AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY Econom. Theory (IF 0.8) Pub Date : 2023-11-17 Mika Meitz, Pentti Saikkonen
In this paper, we consider subgeometric (specifically, polynomial) ergodicity of univariate nonlinear autoregressions with autoregressive conditional heteroskedasticity (ARCH). The notion of subgeometric ergodicity was introduced in the Markov chain literature in the 1980s, and it means that the transition probability measures converge to the stationary measure at a rate slower than geometric; this
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PERFORMANCE OF EMPIRICAL RISK MINIMIZATION FOR LINEAR REGRESSION WITH DEPENDENT DATA Econom. Theory (IF 0.8) Pub Date : 2023-11-10 Christian Brownlees, Guđmundur Stefán Guđmundsson
This paper establishes bounds on the performance of empirical risk minimization for large-dimensional linear regression. We generalize existing results by allowing the data to be dependent and heavy-tailed. The analysis covers both the cases of identically and heterogeneously distributed observations. Our analysis is nonparametric in the sense that the relationship between the regressand and the regressors
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SUBSAMPLING INFERENCE FOR NONPARAMETRIC EXTREMAL CONDITIONAL QUANTILES Econom. Theory (IF 0.8) Pub Date : 2023-11-06 Daisuke Kurisu, Taisuke Otsu
This paper proposes a subsampling inference method for extreme conditional quantiles based on a self-normalized version of a local estimator for conditional quantiles, such as the local linear quantile regression estimator. The proposed method circumvents difficulty of estimating nuisance parameters in the limiting distribution of the local estimator. A simulation study and empirical example illustrate
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NONPARAMETRIC TIME-VARYING PANEL DATA MODELS WITH HETEROGENEITY Econom. Theory (IF 0.8) Pub Date : 2023-10-23 Fei Liu
Since Bai (2009, Econometrica 77, 1229–1279), considerable extensions have been made to panel data models with interactive fixed effects (IFEs). However, little work has been conducted to understand the associated iterative algorithm, which, to the best of our knowledge, is the most commonly adopted approach in this line of research. In this paper, we refine the algorithm of panel data models with
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ON GMM INFERENCE: PARTIAL IDENTIFICATION, IDENTIFICATION STRENGTH, AND NONSTANDARD ASYMPTOTICS Econom. Theory (IF 0.8) Pub Date : 2023-09-18 Donald S. Poskitt
This paper analyses aspects of generalized method of moments (GMM) inference in moment equality models in settings where standard regularity conditions may break down. Explicit analytic formulations for the asymptotic distributions of estimable functions of the GMM estimator and statistics based on the GMM criterion function are derived under relatively mild assumptions. The moment Jacobian is allowed
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VALID HETEROSKEDASTICITY ROBUST TESTING Econom. Theory (IF 0.8) Pub Date : 2023-09-11 Benedikt M. Pötscher, David Preinerstorfer
Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conventional critical values based on asymptotics often lead to severe size distortions, and so do existing adjustments including the bootstrap. To avoid these issues, we suggest to use smallest size-controlling critical values
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INTERACTIVE EFFECTS PANEL DATA MODELS WITH GENERAL FACTORS AND REGRESSORS Econom. Theory (IF 0.8) Pub Date : 2023-09-05 Bin Peng, Liangjun Su, Joakim Westerlund, Yanrong Yang
This paper considers a model with general regressors and unobservable common factors. An estimator based on iterated principal component analysis is proposed, which is shown to be not only asymptotically normal, but under certain conditions also free of the otherwise so common asymptotic incidental parameters bias. Interestingly, the conditions required to achieve unbiasedness become weaker the stronger
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THE ESTIMATION RISK IN EXTREME SYSTEMIC RISK FORECASTS Econom. Theory (IF 0.8) Pub Date : 2023-08-22 Yannick Hoga
Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and, then, calculating systemic risk for the
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IDENTIFICATION AND INFERENCE IN A QUANTILE REGRESSION DISCONTINUITY DESIGN UNDER RANK SIMILARITY WITH COVARIATES Econom. Theory (IF 0.8) Pub Date : 2023-08-02 Zequn Jin, Yu Zhang, Zhengyu Zhang, Yahong Zhou
This study investigates the identification and inference of quantile treatment effects (QTEs) in a fuzzy regression discontinuity (RD) design under rank similarity. Unlike Frandsen et al. (2012, Journal of Econometrics 168, 382–395), who focus on QTEs only for the compliant subpopulation, our approach can identify QTEs and average treatment effect for the whole population at the threshold. We derived
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ALGORITHMIC SUBSAMPLING UNDER MULTIWAY CLUSTERING Econom. Theory (IF 0.8) Pub Date : 2023-07-11 Harold D. Chiang, Jiatong Li, Yuya Sasaki
This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster-dependent data. We establish a new uniform weak law of large numbers and a new central limit theorem for multiway algorithmic subsample means. We show that algorithmic subsampling allows for robustness against potential degeneracy, and even non-Gaussian degeneracy, of the asymptotic distribution under
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SPECIFICATION TESTS FOR TIME-VARYING COEFFICIENT PANEL DATA MODELS Econom. Theory (IF 0.8) Pub Date : 2023-06-01 Alev Atak, Thomas yang Tao, Yonghui Zhang, Qiankun Zhou
This paper provides nonparametric specification tests for the commonly used homogeneous and stable coefficients structures in panel data models. We first obtain the augmented residuals by estimating the model under the null hypothesis and then run auxiliary time series regressions of augmented residuals on covariates with time-varying coefficients (TVCs) via sieve methods. The test statistic is then
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TESTING FOR ANTICIPATED CHANGES IN SPOT VOLATILITY AT EVENT TIMES Econom. Theory (IF 0.8) Pub Date : 2023-05-19 Viktor Todorov, Yang Zhang
We propose a test for anticipated changes in spot volatility, either due to continuous or discontinuous price moves, at the times of realization of event risk in the form of pre-scheduled releases of economic information such as earnings announcements by firms and macroeconomic news announcements. These events can generate nontrivial volatility in asset returns, which does not scale even locally in
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A NOVEL APPROACH TO PREDICTIVE ACCURACY TESTING IN NESTED ENVIRONMENTS Econom. Theory (IF 0.8) Pub Date : 2023-05-17 Jean-Yves Pitarakis
We introduce a new approach for comparing the predictive accuracy of two nested models that bypasses the difficulties caused by the degeneracy of the asymptotic variance of forecast error loss differentials used in the construction of commonly used predictive comparison statistics. Our approach continues to rely on the out of sample mean squared error loss differentials between the two competing models
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INFERENCE ON GARCH-MIDAS MODELS WITHOUT ANY SMALL-ORDER MOMENT Econom. Theory (IF 0.8) Pub Date : 2023-05-12 Christian Francq, Baye Matar Kandji, Jean-Michel Zakoian
In GARCH-mixed-data sampling models, the volatility is decomposed into the product of two factors which are often interpreted as “short-run” (high-frequency) and “long-run” (low-frequency) components. While two-component volatility models are widely used in applied works, some of their theoretical properties remain unexplored. We show that the strictly stationary solutions of such models do not admit
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NEW ROBUST INFERENCE FOR PREDICTIVE REGRESSIONS Econom. Theory (IF 0.8) Pub Date : 2023-05-03 Rustam Ibragimov, Jihyun Kim, Anton Skrobotov
We propose a robust inference method for predictive regression models under heterogeneously persistent volatility as well as endogeneity, persistence, or heavy-tailedness of regressors. This approach relies on two methodologies, nonlinear instrumental variable estimation and volatility correction, which are used to deal with the aforementioned characteristics of regressors and volatility, respectively
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ON THE SIZE CONTROL OF THE HYBRID TEST FOR SUPERIOR PREDICTIVE ABILITY Econom. Theory (IF 0.8) Pub Date : 2023-05-02 Deborah Kim
This article analyzes the theoretical properties of the hybrid test for superior predictive ability. A simple example reveals that the test may not be size-controlled at common significance levels with rejection rates exceeding $11\%$ at a $5\%$ nominal level. Generalizing this observation, the main results show the pointwise asymptotic invalidity of the hybrid test under reasonable conditions. Monte
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INTERCEPT ESTIMATION IN NONLINEAR SELECTION MODELS Econom. Theory (IF 0.8) Pub Date : 2023-04-24 Wiji Arulampalam, Valentina Corradi, Daniel Gutknecht
We propose various semiparametric estimators for nonlinear selection models, where slope and intercept can be separately identified. When the selection equation satisfies a monotonic index restriction, we suggest a local polynomial estimator, using only observations for which the marginal cumulative distribution function of the instrument index is close to one. Data-driven procedures such as cross-validation
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NUCLEAR NORM REGULARIZED QUANTILE REGRESSION WITH INTERACTIVE FIXED EFFECTS Econom. Theory (IF 0.8) Pub Date : 2023-04-24 Junlong Feng
This paper studies large N and large T conditional quantile panel data models with interactive fixed effects. We propose a nuclear norm penalized estimator of the coefficients on the covariates and the low-rank matrix formed by the interactive fixed effects. The estimator solves a convex minimization problem, not requiring pre-estimation of the (number of) interactive fixed effects. It also allows
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A POWERFUL SUBVECTOR ANDERSON–RUBIN TEST IN LINEAR INSTRUMENTAL VARIABLES REGRESSION WITH CONDITIONAL HETEROSKEDASTICITY Econom. Theory (IF 0.8) Pub Date : 2023-04-14 Patrik Guggenberger, Frank Kleibergen, Sophocles Mavroeidis
We introduce a new test for a two-sided hypothesis involving a subset of the structural parameter vector in the linear instrumental variables (IVs) model. Guggenberger, Kleibergen, and Mavroeidis (2019, Quantitative Economics, 10, 487–526; hereafter GKM19) introduce a subvector Anderson–Rubin (AR) test with data-dependent critical values that has asymptotic size equal to nominal size for a parameter
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FUNCTIONAL SEQUENTIAL TREATMENT ALLOCATION WITH COVARIATES Econom. Theory (IF 0.8) Pub Date : 2023-03-16 Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev
We consider a sequential treatment problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision-maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socioeconomic functional such as an inequality
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NEW CONTROL FUNCTION APPROACHES IN THRESHOLD REGRESSION WITH ENDOGENEITY Econom. Theory (IF 0.8) Pub Date : 2023-03-16 Ping Yu, Qin Liao, Peter C. B. Phillips
This paper studies control function (CF) approaches in endogenous threshold regression where the threshold variable is allowed to be endogenous. We first use a simple example to show that the structural threshold regression (STR) estimator of the threshold point in Kourtellos, Stengos and Tan (2016, Econometric Theory 32, 827–860) is inconsistent unless the endogeneity level of the threshold variable
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SHARP TEST FOR EQUILIBRIUM UNIQUENESS IN DISCRETE GAMES WITH PRIVATE INFORMATION AND COMMON KNOWLEDGE UNOBSERVED HETEROGENEITY Econom. Theory (IF 0.8) Pub Date : 2023-03-16 Mathieu Marcoux
This paper proposes a test of the single equilibrium in the data assumption commonly maintained when estimating static discrete games of incomplete information. By allowing for discrete common knowledge payoff-relevant unobserved heterogeneity, the test generalizes existing methods attributing all correlation between players’ decisions to multiple equilibria. It does not require the estimation of payoffs
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CONSISTENT NON-GAUSSIAN PSEUDO MAXIMUM LIKELIHOOD ESTIMATORS OF SPATIAL AUTOREGRESSIVE MODELS Econom. Theory (IF 0.8) Pub Date : 2023-02-06 Fei Jin, Yuqin Wang
This paper studies the non-Gaussian pseudo maximum likelihood (PML) estimation of a spatial autoregressive (SAR) model with SAR disturbances. If the spatial weights matrix $M_{n}$ for the SAR disturbances is normalized to have row sums equal to 1 or the model reduces to a SAR model with no SAR process of disturbances, the non-Gaussian PML estimator (NGPMLE) for model parameters except the intercept
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RELEVANT MOMENT SELECTION UNDER MIXED IDENTIFICATION STRENGTH Econom. Theory (IF 0.8) Pub Date : 2023-01-16 Prosper Dovonon, Firmin Doko Tchatoka, Michael Aguessy
This paper proposes a robust moment selection method aiming to pick the best model even if this is a moment condition model with mixed identification strength, that is, moment conditions including moment functions that are local to zero uniformly over the parameter set. We show that the relevant moment selection procedure of Hall et al. (2007, Journal of Econometrics 138, 488–512) is inconsistent in
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NEARLY EFFICIENT LIKELIHOOD RATIO TESTS OF A UNIT ROOT IN AN AUTOREGRESSIVE MODEL OF ARBITRARY ORDER Econom. Theory (IF 0.8) Pub Date : 2022-12-20 Samuel Brien, Michael Jansson, Morten Ørregaard Nielsen
We study large sample properties of likelihood ratio tests of the unit-root hypothesis in an autoregressive model of arbitrary order. Earlier research on this testing problem has developed likelihood ratio tests in the autoregressive model of order 1, but resorted to a plug-in approach when dealing with higher-order models. In contrast, we consider the full model and derive the relevant large sample
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LARGE SAMPLE JUSTIFICATIONS FOR THE BAYESIAN EMPIRICAL LIKELIHOOD Econom. Theory (IF 0.8) Pub Date : 2022-12-05 Naoya Sueishi
This study investigates the asymptotic properties of the Bayesian empirical likelihood (BEL), which uses the empirical likelihood as an alternative to a parametric likelihood for Bayesian inference. We establish two asymptotic equivalence results based on the Bernstein–von Mises (BvM) theorem by introducing a new formulation of the moment restriction model. First, the limiting posterior distribution
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TESTING A CLASS OF SEMI- OR NONPARAMETRIC CONDITIONAL MOMENT RESTRICTION MODELS USING SERIES METHODS Econom. Theory (IF 0.8) Pub Date : 2022-12-05 Jesper Riis-Vestergaard Sørensen
This paper proposes a new test for a class of conditional moment restrictions (CMRs) whose parameterization involves unknown, unrestricted conditional expectation functions. Motivating examples of such CMRs arise from models of discrete choice under uncertainty including certain static games of incomplete information. The proposed test may be viewed as a semi-/nonparametric extension of the Bierens
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WEAK CONVERGENCE TO DERIVATIVES OF FRACTIONAL BROWNIAN MOTION Econom. Theory (IF 0.8) Pub Date : 2022-12-05 Søren Johansen, Morten Ørregaard Nielsen
It is well known that, under suitable regularity conditions, the normalized fractional process with fractional parameter d converges weakly to fractional Brownian motion (fBm) for $d>\frac {1}{2}$ . We show that, for any nonnegative integer M, derivatives of order $m=0,1,\dots ,M$ of the normalized fractional process with respect to the fractional parameter d jointly converge weakly to the corresponding
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A JACKKNIFE LAGRANGE MULTIPLIER TEST WITH MANY WEAK INSTRUMENTS Econom. Theory (IF 0.8) Pub Date : 2022-11-11 Yukitoshi Matsushita, Taisuke Otsu
This paper proposes a jackknife Lagrange multiplier (JLM) test for instrumental variable regression models, which is robust to (i) many instruments, where the number of instruments may increase proportionally with the sample size, (ii) arbitrarily weak instruments, and (iii) heteroskedastic errors. In contrast to Crudu, Mellace, and Sándor (2021, Econometric Theory 37, 281–310) and Mikusheva and Sun
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EXPONENTIAL REALIZED GARCH-ITÔ VOLATILITY MODELS Econom. Theory (IF 0.8) Pub Date : 2022-11-10 Donggyu Kim
This paper introduces a novel Itô diffusion process to model high-frequency financial data that can accommodate low-frequency volatility dynamics by embedding the discrete-time nonlinear exponential generalized autoregressive conditional heteroskedasticity (GARCH) structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that
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HIGHER-ORDER APPROXIMATION OF IV ESTIMATORS WITH INVALID INSTRUMENTS Econom. Theory (IF 0.8) Pub Date : 2022-11-10 Byunghoon Kang
This paper analyzes the higher-order approximation of instrumental variable (IV) estimators in a linear homoskedastic IV regression model when a large set of instruments with potential invalidity is present. We establish theoretical results on the higher-order mean-squared error (MSE) approximation of the two-stage least-squares (2SLS), the limited information maximum likelihood (LIML), the Fuller
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INSTRUMENTAL VARIABLES INFERENCE IN A SMALL-DIMENSIONAL VAR MODEL WITH DYNAMIC LATENT FACTORS Econom. Theory (IF 0.8) Pub Date : 2022-11-10 Federico Carlini, Patrick Gagliardini
We study semiparametric inference in a small-dimensional vector autoregressive (VAR) model of order p augmented by unobservable common factors with a dynamic described by a VAR process of order q. This state-space specification is useful to measure separately the direct causality effects and the responses to dynamic common factors. We show that the state-space parameters are identifiable from the autocovariance
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ADAPTATION FOR NONPARAMETRIC ESTIMATORS OF LOCALLY STATIONARY PROCESSES Econom. Theory (IF 0.8) Pub Date : 2022-11-07 Rainer Dahlhaus, Stefan Richter
Two adaptive bandwidth selection methods for minimizing the mean squared error of nonparametric estimators in locally stationary processes are proposed. We investigate a cross-validation approach and a method based on contrast minimization and derive asymptotic properties of both methods. The results are applicable for different statistics under a general setting of local stationarity including nonlinear
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ADVANCES IN USING VECTOR AUTOREGRESSIONS TO ESTIMATE STRUCTURAL MAGNITUDES Econom. Theory (IF 0.8) Pub Date : 2022-11-07 Christiane Baumeister, James D. Hamilton
This paper surveys recent advances in drawing structural conclusions from vector autoregressions (VARs), providing a unified perspective on the role of prior knowledge. We describe the traditional approach to identification as a claim to have exact prior information about the structural model and propose Bayesian inference as a way to acknowledge that prior information is imperfect or subject to error
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AN AVERAGING ESTIMATOR FOR TWO-STEP M-ESTIMATION IN SEMIPARAMETRIC MODELS Econom. Theory (IF 0.8) Pub Date : 2022-11-07 Ruoyao Shi
In a two-step extremum estimation (M-estimation) framework with a finite-dimensional parameter of interest and a potentially infinite-dimensional first-step nuisance parameter, this paper proposes an averaging estimator that combines a semiparametric estimator based on a nonparametric first step and a parametric estimator which imposes parametric restrictions on the first step. The averaging weight
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A MOLLIFIER APPROACH TO THE DECONVOLUTION OF PROBABILITY DENSITIES Econom. Theory (IF 0.8) Pub Date : 2022-10-28 Thorsten Hohage, Pierre Maréchal, Léopold Simar, Anne Vanhems
We use mollification to regularize the problem of deconvolution of random variables. This regularization method offers a unifying and generalizing framework in order to compare the benefits of various filter-type techniques like deconvolution kernels, Tikhonov, or spectral cutoff methods. In particular, the mollifier approach allows to relax some restrictive assumptions required for the deconvolution
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REGULARIZED ESTIMATION OF DYNAMIC PANEL MODELS Econom. Theory (IF 0.8) Pub Date : 2022-10-28 Marine Carrasco, Ada Nayihouba
In a dynamic panel data model, the number of moment conditions increases rapidly with the time dimension, resulting in a large dimensional covariance matrix of the instruments. As a consequence, the generalized method of moments (GMM) estimator exhibits a large bias in small samples, especially when the autoregressive parameter is close to unity. To address this issue, we propose a regularized version
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SUPERCONSISTENCY OF TESTS IN HIGH DIMENSIONS Econom. Theory (IF 0.8) Pub Date : 2022-10-28 Anders Bredahl Kock, David Preinerstorfer
To assess whether there is some signal in a big database, aggregate tests for the global null hypothesis of no effect are routinely applied in practice before more specialized analysis is carried out. Although a plethora of aggregate tests is available, each test has its strengths but also its blind spots. In a Gaussian sequence model, we study whether it is possible to obtain a test with substantially
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TESTING FOR HOMOGENEOUS THRESHOLDS IN THRESHOLD REGRESSION MODELS Econom. Theory (IF 0.8) Pub Date : 2022-10-28 Yoonseok Lee, Yulong Wang
This paper develops a test for homogeneity of the threshold parameter in threshold regression models. The test has a natural interpretation from time series perspectives and can also be applied to test for additional change points in the structural break models. The limiting distribution of the test statistic is derived, and the finite sample properties are studied in Monte Carlo simulations. We apply
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TESTING FOR STRICT STATIONARITY VIA THE DISCRETE FOURIER TRANSFORM Econom. Theory (IF 0.8) Pub Date : 2022-10-28 Zhonghao Fu, Shang Gao, Liangjun Su, Xia Wang
This paper proposes a model-free test for the strict stationarity of a potentially vector-valued time series using the discrete Fourier transform (DFT) approach. We show that the DFT of a residual process based on the empirical characteristic function weakly converges to a zero spectrum in the frequency domain for a strictly stationary time series and a nonzero spectrum otherwise. The proposed test
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ANALYSIS OF GLOBAL AND LOCAL OPTIMA OF REGULARIZED QUANTILE REGRESSION IN HIGH DIMENSIONS: A SUBGRADIENT APPROACH Econom. Theory (IF 0.8) Pub Date : 2022-10-18 Lan Wang, Xuming He
Regularized quantile regression (QR) is a useful technique for analyzing heterogeneous data under potentially heavy-tailed error contamination in high dimensions. This paper provides a new analysis of the estimation/prediction error bounds of the global solution of $L_1$-regularized QR (QR-LASSO) and the local solutions of nonconvex regularized QR (QR-NCP) when the number of covariates is greater than
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KERNEL ESTIMATION OF SPOT VOLATILITY WITH MICROSTRUCTURE NOISE USING PRE-AVERAGING Econom. Theory (IF 0.8) Pub Date : 2022-10-18 José E. Figueroa-López, Bei Wu
We revisit the problem of estimating the spot volatility of an Itô semimartingale using a kernel estimator. A central limit theorem (CLT) with an optimal convergence rate is established for a general two-sided kernel. A new pre-averaging/kernel estimator for spot volatility is also introduced to handle the microstructure noise of ultra high-frequency observations. A CLT for the estimation error of
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CONSISTENT SPECIFICATION TESTING UNDER SPATIAL DEPENDENCE Econom. Theory (IF 0.8) Pub Date : 2022-10-11 Abhimanyu Gupta, Xi Qu
We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the “space” being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity
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CENTRAL LIMIT THEORY FOR COMBINED CROSS SECTION AND TIME SERIES WITH AN APPLICATION TO AGGREGATE PRODUCTIVITY SHOCKS Econom. Theory (IF 0.8) Pub Date : 2022-09-19 Jinyong Hahn, Guido Kuersteiner, Maurizio Mazzocco
Combining cross-sectional and time-series data is a long and well-established practice in empirical economics. We develop a central limit theory that explicitly accounts for possible dependence between the two datasets. We focus on common factors as the mechanism behind this dependence. Using our central limit theorem (CLT), we establish the asymptotic properties of parameter estimates of a general
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SEQUENTIALLY ESTIMATING THE STRUCTURAL EQUATION BY POWER TRANSFORMATION Econom. Theory (IF 0.8) Pub Date : 2022-09-19 Jaedo Choi, Hyungsik Roger Moon, Jin Seo Cho
This study provides an econometric methodology to test a linear structural relationship among economic variables. We propose the so-called distance-difference (DD) test and show that it has omnibus power against arbitrary nonlinear structural relationships. If the DD-test rejects the linear model hypothesis, a sequential testing procedure assisted by the DD-test can consistently estimate the degree
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INFERENCE ON A DISTRIBUTION FROM NOISY DRAWS Econom. Theory (IF 0.8) Pub Date : 2022-08-18 Koen Jochmans, Martin Weidner
We consider a situation where the distribution of a random variable is being estimated by the empirical distribution of noisy measurements of that variable. This is common practice in, for example, teacher value-added models and other fixed-effect models for panel data. We use an asymptotic embedding where the noise shrinks with the sample size to calculate the leading bias in the empirical distribution
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RECURSIVE DIFFERENCING FOR ESTIMATING SEMIPARAMETRIC MODELS Econom. Theory (IF 0.8) Pub Date : 2022-08-18 Chan Shen, Roger Klein
Controlling the bias is central to estimating semiparametric models. Many methods have been developed to control bias in estimating conditional expectations while maintaining a desirable variance order. However, these methods typically do not perform well at moderate sample sizes. Moreover, and perhaps related to their performance, nonoptimal windows are selected with undersmoothing needed to ensure
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TWO-STEP ESTIMATION OF QUANTILE PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS Econom. Theory (IF 0.8) Pub Date : 2022-08-18 Liang Chen
This paper considers the estimation of panel data models with interactive fixed effects where the idiosyncratic errors are subject to conditional quantile restrictions. An easy-to-implement two-step estimator is proposed for the coefficients of the observed regressors. In the first step, the principal component analysis is applied to the cross-sectional averages of the regressors to estimate the latent
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CONSISTENT LOCAL SPECTRUM INFERENCE FOR PREDICTIVE RETURN REGRESSIONS Econom. Theory (IF 0.8) Pub Date : 2022-08-03 Torben G. Andersen, Rasmus T. Varneskov
This paper studies the properties of predictive regressions for asset returns in economic systems governed by persistent vector autoregressive dynamics. In particular, we allow for the state variables to be fractionally integrated, potentially of different orders, and for the returns to have a latent persistent conditional mean, whose memory is difficult to estimate consistently by standard techniques
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SIMULTANEOUS CONFIDENCE BANDS FOR CONDITIONAL VALUE-AT-RISK AND EXPECTED SHORTFALL Econom. Theory (IF 0.8) Pub Date : 2022-08-03 Shuo Li, Liuhua Peng, Xiaojun Song
Conditional value-at-risk (CVaR) and conditional expected shortfall (CES) are widely adopted risk measures which help monitor potential tail risk while adapting to evolving market information. In this paper, we propose an approach to constructing simultaneous confidence bands (SCBs) for tail risk as measured by CVaR and CES, with the confidence bands uniformly valid for a set of tail levels. We consider
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ESTIMATION AND INFERENCE WITH NEAR UNIT ROOTS Econom. Theory (IF 0.8) Pub Date : 2022-07-27 Peter C.B. Phillips
New methods are developed for identifying, estimating, and performing inference with nonstationary time series that have autoregressive roots near unity. The approach subsumes unit-root (UR), local unit-root (LUR), mildly integrated (MI), and mildly explosive (ME) specifications in the new model formulation. It is shown how a new parameterization involving a localizing rate sequence that characterizes
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IDENTIFICATION AND THE INFLUENCE FUNCTION OF OLLEY AND PAKES’ (1996) PRODUCTION FUNCTION ESTIMATOR Econom. Theory (IF 0.8) Pub Date : 2022-07-22 Jinyong Hahn, Zhipeng Liao, Geert Ridder
In this paper, we reconsider the assumptions that ensure the identification of the production function in Olley and Pakes (1996, Econometrica 64, 1263–1297). We show that an index restriction plays a crucial role in the identification, especially if the capital stock is measured by the perpetual inventory method. The index restriction is not sufficient for identification under sample selectivity. The