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  • AVERAGE DERIVATIVE ESTIMATION UNDER MEASUREMENT ERROR
    Econom. Theory (IF 1.17) Pub Date : 2020-11-13
    Hao Dong; Taisuke Otsu; Luke Taylor

    In this paper, we derive the asymptotic properties of the density-weighted average derivative estimator when a regressor is contaminated with classical measurement error and the density of this error must be estimated. Average derivatives of conditional mean functions are used extensively in economics and statistics, most notably in semiparametric index models. As well as ordinary smooth measurement

    更新日期:2020-11-13
  • LIMIT THEOREMS FOR FACTOR MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-11-09
    Stanislav Anatolyev; Anna Mikusheva

    This paper establishes central limit theorems (CLTs) and proposes how to perform valid inference in factor models. We consider a setting where many counties/regions/assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous microparameters estimated separately for each entity. The CLT applies for quantities involving

    更新日期:2020-11-09
  • A MULTIPLEX INTERDEPENDENT DURATIONS MODEL
    Econom. Theory (IF 1.17) Pub Date : 2020-11-09
    Zhongjian Lin; Ruixuan Liu

    We propose a multiplex interdependent durations model and study its empirical content. The model considers an empirical stopping game of multiple agents making optimal timing decisions with incomplete information. We characterize the unique Bayesian Nash equilibrium of the stopping game in a system of simultaneous equations involving the conditional distribution of each duration with a moderate strategic

    更新日期:2020-11-09
  • SUBGEOMETRICALLY ERGODIC AUTOREGRESSIONS
    Econom. Theory (IF 1.17) Pub Date : 2020-11-09
    Mika Meitz; Pentti Saikkonen

    In this paper, we discuss how the notion of subgeometric ergodicity in Markov chain theory can be exploited to study stationarity and ergodicity of nonlinear time series models. Subgeometric ergodicity means that the transition probability measures converge to the stationary measure at a rate slower than geometric. Specifically, we consider suitably defined higher-order nonlinear autoregressions that

    更新日期:2020-11-09
  • PARTIAL IDENTIFICATION OF NONSEPARABLE MODELS USING BINARY INSTRUMENTS
    Econom. Theory (IF 1.17) Pub Date : 2020-10-30
    Takuya Ishihara

    In this study, we explore the partial identification of nonseparable models with continuous endogenous and binary instrumental variables. We show that the structural function is partially identified when it is monotone or concave in the explanatory variable. D’Haultfœuille and Février (2015, Econometrica 83(3), 1199–1210) and Torgovitsky (2015, Econometrica 83(3), 1185–1197) prove the point identification

    更新日期:2020-10-30
  • NONSTATIONARY LINEAR PROCESSES WITH INFINITE VARIANCE GARCH ERRORS
    Econom. Theory (IF 1.17) Pub Date : 2020-10-26
    Rongmao Zhang; Ngai Hang Chan

    Recently, Cavaliere, Georgiev, and Taylor (2018, Econometric Theory 34, 302–348) (CGT) considered the augmented Dickey–Fuller (ADF) test for a unit-root model with linear noise driven by i.i.d. infinite variance innovations and showed that ordinary least squares (OLS)-based ADF statistics have the same distribution as in Chan and Tran (1989, Econometric Theory 5, 354–362) for i.i.d. infinite variance

    更新日期:2020-10-30
  • ESTIMATION OF VOLATILITY FUNCTIONS IN JUMP DIFFUSIONS USING TRUNCATED BIPOWER INCREMENTS
    Econom. Theory (IF 1.17) Pub Date : 2020-10-08
    Jihyun Kim; Joon Y. Park; Bin Wang

    In this article, we introduce and analyze a new methodology to estimate the volatility functions of jump diffusion models. Our methodology relies on the standard kernel estimation technique using truncated bipower increments. The relevant asymptotics are fully developed, allowing for the time span to increase as well as the sampling interval to decrease, and accommodate both stationary and nonstationary

    更新日期:2020-10-08
  • NONPARAMETRIC EULER EQUATION IDENTIFICATION AND ESTIMATION
    Econom. Theory (IF 1.17) Pub Date : 2020-09-28
    Juan Carlos Escanciano; Stefan Hoderlein; Arthur Lewbel; Oliver Linton; Sorawoot Srisuma

    We consider nonparametric identification and estimation of pricing kernels, or equivalently of marginal utility functions up to scale, in consumption-based asset pricing Euler equations. Ours is the first paper to prove nonparametric identification of Euler equations under low level conditions (without imposing functional restrictions or just assuming completeness). We also propose a novel nonparametric

    更新日期:2020-09-28
  • FACTORISABLE MULTITASK QUANTILE REGRESSION
    Econom. Theory (IF 1.17) Pub Date : 2020-09-22
    Shih-Kang Chao; Wolfgang K. Härdle; Ming Yuan

    A multivariate quantile regression model with a factor structure is proposed to study data with multivariate responses with covariates. The factor structure is allowed to vary with the quantile levels, which is more flexible than the classical factor models. Assuming the number of factors is small, and the number of responses and the input variables are growing with the sample size, the model is estimated

    更新日期:2020-09-22
  • ROBUST TESTS FOR WHITE NOISE AND CROSS-CORRELATION
    Econom. Theory (IF 1.17) Pub Date : 2020-09-21
    Violetta Dalla; Liudas Giraitis; Peter C. B. Phillips

    Commonly used tests to assess evidence for the absence of autocorrelation in a univariate time series or serial cross-correlation between time series rely on procedures whose validity holds for i.i.d. data. When the series are not i.i.d., the size of correlogram and cumulative Ljung–Box tests can be significantly distorted. This paper adapts standard correlogram and portmanteau tests to accommodate

    更新日期:2020-09-21
  • JOINT TIME-SERIES AND CROSS-SECTION LIMIT THEORY UNDER MIXINGALE ASSUMPTIONS
    Econom. Theory (IF 1.17) Pub Date : 2020-08-11
    Jinyong Hahn; Guido Kuersteiner; Maurizio Mazzocco

    In this paper, we complement joint time-series and cross-section convergence results derived in a companion paper Hahn, Kuersteiner, and Mazzocco (2016, Central Limit Theory for Combined Cross-Section and Time Series) by allowing for serial correlation in the time-series sample. The implications of our analysis are limiting distributions that have a well-known form of long-run variances for the time-series

    更新日期:2020-08-11
  • LARGE SAMPLE PROPERTIES OF BAYESIAN ESTIMATION OF SPATIAL ECONOMETRIC MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-08-11
    Xiaoyi Han; Lung-Fei Lee; Xingbai Xu

    This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial

    更新日期:2020-08-11
  • FINITE-SAMPLE SIZE CONTROL OF IVX-BASED TESTS IN PREDICTIVE REGRESSIONS
    Econom. Theory (IF 1.17) Pub Date : 2020-08-10
    Mehdi Hosseinkouchack; Matei Demetrescu

    In predictive regressions with variables of unknown persistence, the use of extended IV (IVX) instruments leads to asymptotically valid inference. Under highly persistent regressors, the standard normal or chi-squared limiting distributions for the usual t and Wald statistics may, however, differ markedly from the actual finite-sample distributions which exhibit in particular noncentrality. Convergence

    更新日期:2020-08-10
  • EFFICIENT ESTIMATION OF INTEGRATED VOLATILITY FUNCTIONALS UNDER GENERAL VOLATILITY DYNAMICS
    Econom. Theory (IF 1.17) Pub Date : 2020-07-17
    Jia Li; Yunxiao Liu

    We provide an asymptotic theory for the estimation of a general class of smooth nonlinear integrated volatility functionals. Such functionals are broadly useful for measuring financial risk and estimating economic models using high-frequency transaction data. The theory is valid under general volatility dynamics, which accommodates both Itô semimartingales (e.g., jump-diffusions) and long-memory processes

    更新日期:2020-07-17
  • EFFICIENT TWO-STEP GENERALIZED EMPIRICAL LIKELIHOOD ESTIMATION AND TESTS WITH MARTINGALE DIFFERENCES
    Econom. Theory (IF 1.17) Pub Date : 2020-06-29
    Fei Jin; Lung-fei Lee

    This paper considers two-step generalized empirical likelihood (GEL) estimation and tests with martingale differences when there is a computationally simple $\sqrt n-$ consistent estimator of nuisance parameters or the nuisance parameters can be eliminated with an estimating function of parameters of interest. As an initial estimate might have asymptotic impact on final estimates, we propose general

    更新日期:2020-06-29
  • IDENTIFICATION OF LINEAR REGRESSIONS WITH ERRORS IN ALL VARIABLES
    Econom. Theory (IF 1.17) Pub Date : 2020-06-29
    Dan Ben-Moshe

    This paper analyzes the classical linear regression model with measurement errors in all the variables. First, we provide necessary and sufficient conditions for identification of the coefficients. We show that the coefficients are not identified if and only if an independent normally distributed linear combination of regressors can be transferred from the regressors to the errors. Second, we introduce

    更新日期:2020-06-29
  • SPECIFICATION TESTING FOR ERRORS-IN-VARIABLES MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-06-19
    Taisuke Otsu; Luke Taylor

    This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniques. In contrast to the methods proposed by Hall and Ma (2007, Annals of Statistics, 35, 2620–2638) and Song (2008, Journal of Multivariate Analysis, 99, 2406–2443), our test allows

    更新日期:2020-06-19
  • AN ADAPTIVE TEST OF STOCHASTIC MONOTONICITY
    Econom. Theory (IF 1.17) Pub Date : 2020-06-16
    Denis Chetverikov; Daniel Wilhelm; Dongwoo Kim

    We propose a new nonparametric test of stochastic monotonicity which adapts to the unknown smoothness of the conditional distribution of interest, possesses desirable asymptotic properties, is conceptually easy to implement, and computationally attractive. In particular, we show that the test asymptotically controls size at a polynomial rate, is nonconservative, and detects certain smooth local alternatives

    更新日期:2020-06-16
  • INFERENCE IN DYNAMIC, NONPARAMETRIC MODELS OF PRODUCTION: CENTRAL LIMIT THEOREMS FOR MALMQUIST INDICES
    Econom. Theory (IF 1.17) Pub Date : 2020-06-15
    Alois Kneip; Léopold Simar; Paul W. Wilson

    The Malmquist index gives a measure of productivity in dynamic settings and has been widely applied in empirical work. The index is typically estimated using envelopment estimators, particularly data envelopment analysis (DEA) estimators. Until now, inference about productivity change measured by Malmquist indices has been problematic, including both inference regarding productivity change experienced

    更新日期:2020-06-15
  • WEAK-IDENTIFICATION ROBUST WILD BOOTSTRAP APPLIED TO A CONSISTENT MODEL SPECIFICATION TEST
    Econom. Theory (IF 1.17) Pub Date : 2020-06-02
    Jonathan B. Hill

    We present a new robust bootstrap method for a test when there is a nuisance parameter under the alternative, and some parameters are possibly weakly or nonidentified. We focus on a Bierens (1990, Econometrica 58, 1443–1458)-type conditional moment test of omitted nonlinearity for convenience. Existing methods include the supremum p-value which promotes a conservative test that is generally not consistent

    更新日期:2020-06-02
  • IDENTIFYING MULTIPLE MARGINAL EFFECTS WITH A SINGLE INSTRUMENT
    Econom. Theory (IF 1.17) Pub Date : 2020-05-19
    Carolina Caetano; Juan Carlos Escanciano

    This paper proposes a new strategy for the identification of the marginal effects of an endogenous multivalued variable (which can be continuous, or a vector) in a model with an Instrumental Variable (IV) of lower dimension, which may even be a single binary variable, and multiple controls. Despite the failure of the classical order condition, we show that identification may be achieved by exploiting

    更新日期:2020-05-19
  • A MAX-CORRELATION WHITE NOISE TEST FOR WEAKLY DEPENDENT TIME SERIES
    Econom. Theory (IF 1.17) Pub Date : 2020-05-12
    Jonathan B. Hill; Kaiji Motegi

    This article presents a bootstrapped p-value white noise test based on the maximum correlation, for a time series that may be weakly dependent under the null hypothesis. The time series may be prefiltered residuals. The test statistic is a normalized weighted maximum sample correlation coefficient $ \max _{1\leq h\leq \mathcal {L}_{n}}\sqrt {n}|\hat {\omega }_{n}(h)\hat {\rho }_{n}(h)|$ , where $\hat

    更新日期:2020-05-12
  • TESTING A PARAMETRIC TRANSFORMATION MODEL VERSUS A NONPARAMETRIC ALTERNATIVE
    Econom. Theory (IF 1.17) Pub Date : 2020-05-12
    Arkadiusz Szydłowski

    Despite an abundance of semiparametric estimators of the transformation model, no procedure has been proposed yet to test the hypothesis that the transformation function belongs to a finite-dimensional parametric family against a nonparametric alternative. In this article, we introduce a bootstrap test based on integrated squared distance between a nonparametric estimator and a parametric null. As

    更新日期:2020-05-12
  • TESTING FOR STRUCTURAL CHANGES IN FACTOR MODELS VIA A NONPARAMETRIC REGRESSION
    Econom. Theory (IF 1.17) Pub Date : 2020-05-12
    Liangjun Su; Xia Wang

    We propose a model-free test for structural changes in factor models. The basic idea is to regress the data on commonly estimated factors by local smoothing and compare the fitted values of time-varying factor loadings with those of time-invariant factor loadings estimated via principal component analysis. By construction, the test is designed to be powerful against both smooth structural changes and

    更新日期:2020-05-12
  • NEARLY OPTIMAL TEST FOR LONG-RUN PREDICTABILITY WITH NEARLY INTEGRATED REGRESSORS
    Econom. Theory (IF 1.17) Pub Date : 2020-04-27
    Natalia Sizova

    We develop a method for long-run predictability testing in series Y by a persistent series X. We consider a class of tests based on the long-run behavior of these series that are robust to short-run dynamics and attempt to attain the highest possible power. The test is based on the Whittle approximation to the likelihood ratio that is adjusted to remain accurate across a range of persistence in X.

    更新日期:2020-04-27
  • OPTIMAL AUXILIARY PRIORS AND REVERSIBLE JUMP PROPOSALS FOR A CLASS OF VARIABLE DIMENSION MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-04-27
    Andriy Norets

    This article develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions

    更新日期:2020-04-27
  • A NEW STUDY ON ASYMPTOTIC OPTIMALITY OF LEAST SQUARES MODEL AVERAGING
    Econom. Theory (IF 1.17) Pub Date : 2020-04-14
    Xinyu Zhang

    In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction

    更新日期:2020-04-14
  • SPATIAL DEPENDENCE IN OPTION OBSERVATION ERRORS
    Econom. Theory (IF 1.17) Pub Date : 2020-04-13
    Torben G. Andersen; Nicola Fusari; Viktor Todorov; Rasmus T. Varneskov

    In this paper, we develop the first formal nonparametric test for whether the observation errors in option panels display spatial dependence. The panel consists of options with different strikes and tenors written on a given underlying asset. The asymptotic design is of the infill type—the mesh of the strike grid for the observed options shrinks asymptotically to zero, while the set of observation

    更新日期:2020-04-13
  • RANDOMIZATION TESTS OF COPULA SYMMETRY
    Econom. Theory (IF 1.17) Pub Date : 2020-04-06
    Brendan K. Beare; Juwon Seo

    New nonparametric tests of copula exchangeability and radial symmetry are proposed. The novel aspect of the tests is a resampling procedure that exploits group invariance conditions associated with the relevant symmetry hypothesis. They may be viewed as feasible versions of randomization tests of symmetry, the latter being inapplicable due to the unobservability of marginals. Our tests are simple to

    更新日期:2020-04-06
  • ROBUSTIFIED EXPECTED MAXIMUM PRODUCTION FRONTIERS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-27
    Abdelaati Daouia; Jean-Pierre Florens; Léopold Simar

    The aim of this paper is to construct a robust nonparametric estimator for the production frontier. We study this problem under a regression model with one-sided errors, where the regression function defines the achievable maximum output, for a given level of inputs-usage, and the regression error defines the inefficiency term. The main tool is a concept of partial regression boundary defined as a

    更新日期:2020-03-27
  • INFERENCE IN INSTRUMENTAL VARIABLE MODELS WITH HETEROSKEDASTICITY AND MANY INSTRUMENTS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-26
    Federico Crudu; Giovanni Mellace; Zsolt Sándor

    This paper proposes novel inference procedures for instrumental variable models in the presence of many, potentially weak instruments that are robust to the presence of heteroskedasticity. First, we provide an Anderson–Rubin-type test for the entire parameter vector that is valid under assumptions weaker than previously proposed Anderson–Rubin-type tests. Second, we consider the case of testing a subset

    更新日期:2020-03-26
  • INFERENCE IN NONPARAMETRIC SERIES ESTIMATION WITH SPECIFICATION SEARCHES FOR THE NUMBER OF SERIES TERMS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-26
    Byunghoon Kang

    Nonparametric series regression often involves specification search over the tuning parameter, that is, evaluating estimates and confidence intervals with a different number of series terms. This paper develops pointwise and uniform inferences for conditional mean functions in nonparametric series estimations that are uniform in the number of series terms. As a result, this paper constructs confidence

    更新日期:2020-03-26
  • LOCAL COMPOSITE QUANTILE REGRESSION SMOOTHING: A FLEXIBLE DATA STRUCTURE AND CROSS-VALIDATION
    Econom. Theory (IF 1.17) Pub Date : 2020-03-26
    Xiao Huang; Zhongjian Lin

    In this paper, we study the local composite quantile regression estimator for mixed categorical and continuous data. The local composite quantile estimator is an efficient and safe alternative to the local polynomial method and has been well-studied for continuous covariates. Generalization of the local composite quantile regression estimator to a flexible data structure is appealing to practitioners

    更新日期:2020-03-26
  • LATENT VARIABLE NONPARAMETRIC COINTEGRATING REGRESSION
    Econom. Theory (IF 1.17) Pub Date : 2020-03-23
    Qiying Wang; Peter C.B. Phillips; Ioannis Kasparis

    This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true regressor. Such models occur in linear and nonlinear

    更新日期:2020-03-23
  • OPTIMAL MULTISTEP VAR FORECAST AVERAGING
    Econom. Theory (IF 1.17) Pub Date : 2020-03-23
    Jen-Che Liao; Wen-Jen Tsay

    This article proposes frequentist multiple-equation least-squares averaging approaches for multistep forecasting with vector autoregressive (VAR) models. The proposed VAR forecast averaging methods are based on the multivariate Mallows model averaging (MMMA) and multivariate leave-h-out cross-validation averaging (MCVAh) criteria (with h denoting the forecast horizon), which are valid for iterative

    更新日期:2020-03-23
  • A PROPERTY OF THE HODRICK–PRESCOTT FILTER AND ITS APPLICATION
    Econom. Theory (IF 1.17) Pub Date : 2020-03-23
    Neslihan Sakarya; Robert M. de Jong

    This article explores a simple property of the Hodrick–Prescott (HP) filter: when the HP filter is applied to a series, the cyclical component is equal to the HP-filtered trend of the fourth difference of the series, except for the first and last two observations, for which different formulas are needed. We use this result to derive small sample results and asymptotic results for a fixed smoothing

    更新日期:2020-03-23
  • A SMOOTHING METHOD THAT LOOKS LIKE THE HODRICK–PRESCOTT FILTER
    Econom. Theory (IF 1.17) Pub Date : 2020-03-23
    Hiroshi Yamada

    In recent decades, in the research community of macroeconometric time series analysis, we have observed growing interest in the smoothing method known as the Hodrick–Prescott (HP) filter. This article examines the properties of an alternative smoothing method that looks like the HP filter, but is much less well known. We show that this is actually more like the exponential smoothing filter than the

    更新日期:2020-03-23
  • A PRIMER ON BOOTSTRAP TESTING OF HYPOTHESES IN TIME SERIES MODELS: WITH AN APPLICATION TO DOUBLE AUTOREGRESSIVE MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-20
    Giuseppe Cavaliere; Anders Rahbek

    In this article, we discuss the bootstrap as a tool for statistical inference in econometric time series models. Importantly, in the context of testing, properties of the bootstrap under the null (size) as well as under the alternative (power) are discussed. Although properties under the alternative are crucial to ensure the consistency of bootstrap-based tests, it is often the case in the literature

    更新日期:2020-03-20
  • COUNT AND DURATION TIME SERIES WITH EQUAL CONDITIONAL STOCHASTIC AND MEAN ORDERS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-17
    Abdelhakim Aknouche; Christian Francq

    We consider a positive-valued time series whose conditional distribution has a time-varying mean, which may depend on exogenous variables. The main applications concern count or duration data. Under a contraction condition on the mean function, it is shown that stationarity and ergodicity hold when the mean and stochastic orders of the conditional distribution are the same. The latter condition holds

    更新日期:2020-03-17
  • QUANTILE TREATMENT EFFECTS IN REGRESSION KINK DESIGNS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-17
    Heng Chen; Harold D. Chiang; Yuya Sasaki

    The literature on regression kink designs develops identification results for average effects of continuous treatments (Nielsen et al., 2010, American Economic Journal: Economic Policy 2, 185–215; Card et al., 2015, Econometrica 83, 2453–2483), average effects of binary treatments (Dong, 2018, Jump or Kink? Identifying Education Effects by Regression Discontinuity Design without the Discontinuity)

    更新日期:2020-03-17
  • INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION
    Econom. Theory (IF 1.17) Pub Date : 2020-03-13
    Takuya Ura

    This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics, 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable

    更新日期:2020-03-13
  • EXACT LOCAL WHITTLE ESTIMATION IN LONG MEMORY TIME SERIES WITH MULTIPLE POLES
    Econom. Theory (IF 1.17) Pub Date : 2020-03-05
    Josu Arteche

    A generalization of the Exact Local Whittle estimator in Shimotsu and Phillips (2005, Annals of Statistics 33, 1890–1933) is proposed for jointly estimating all the memory parameters in general long memory time series that possibly display standard, seasonal, and/or other cyclical strong persistence. Consistency and asymptotic normality are proven for stationary, nonstationary, and noninvertible series

    更新日期:2020-03-05
  • HONEST CONFIDENCE SETS IN NONPARAMETRIC IV REGRESSION AND OTHER ILL-POSED MODELS
    Econom. Theory (IF 1.17) Pub Date : 2020-03-05
    Andrii Babii

    This article develops inferential methods for a very general class of ill-posed models in econometrics encompassing the nonparametric instrumental variable regression, various functional regressions, and the density deconvolution. We focus on uniform confidence sets for the parameter of interest estimated with Tikhonov regularization, as in Darolles et al. (2011, Econometrica 79, 1541–1565). Since

    更新日期:2020-03-05
  • A NEW MULTILEVEL MODELING APPROACH FOR CLUSTERED SURVIVAL DATA
    Econom. Theory (IF 1.17) Pub Date : 2020-03-03
    Jinfeng Xu; Mu Yue; Wenyang Zhang

    In multilevel modeling of clustered survival data, to account for the differences among different clusters, a commonly used approach is to introduce cluster effects, either random or fixed, into the model. Modeling with random effects may lead to difficulties in the implementation of the estimation procedure for the unknown parameters of interest because the numerical computation of multiple integrals

    更新日期:2020-03-03
  • SPECIFICATION TESTING IN NONPARAMETRIC INSTRUMENTAL QUANTILE REGRESSION
    Econom. Theory (IF 1.17) Pub Date : 2020-01-07
    Christoph Breunig

    There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the

    更新日期:2020-01-07
  • LIKELIHOOD INFERENCE ON SEMIPARAMETRIC MODELS WITH GENERATED REGRESSORS
    Econom. Theory (IF 1.17) Pub Date : 2019-11-25
    Yukitoshi Matsushita; Taisuke Otsu

    Hahn and Ridder (2013, Econometrica 81, 315–340) formulated influence functions of semiparametric three-step estimators where generated regressors are computed in the first step. This class of estimators covers several important examples for empirical analysis, such as production function estimators by Olley and Pakes (1996, Econometrica 64, 1263–1297) and propensity score matching estimators for treatment

    更新日期:2019-11-25
  • COINTEGRATION IN FUNCTIONAL AUTOREGRESSIVE PROCESSES
    Econom. Theory (IF 1.17) Pub Date : 2019-11-22
    Massimo Franchi; Paolo Paruolo

    This article defines the class of ${\cal H}$ -valued autoregressive (AR) processes with a unit root of finite type, where ${\cal H}$ is a possibly infinite-dimensional separable Hilbert space, and derives a generalization of the Granger–Johansen Representation Theorem valid for any integration order $d = 1,2, \ldots$ . An existence theorem shows that the solution of an AR process with a unit root of

    更新日期:2019-11-22
  • REPRESENTATION OF I(1) AND I(2) AUTOREGRESSIVE HILBERTIAN PROCESSES
    Econom. Theory (IF 1.17) Pub Date : 2019-11-22
    Brendan K. Beare; Won-Ki Seo

    We develop versions of the Granger–Johansen representation theorems for I(1) and I(2) vector autoregressive processes that apply to processes taking values in an arbitrary complex separable Hilbert space. This more general setting is of central relevance for statistical applications involving functional time series. An I(1) or I(2) solution to an autoregressive law of motion is obtained when the inverse

    更新日期:2019-11-22
  • ASYMPTOTIC THEORY FOR KERNEL ESTIMATORS UNDER MODERATE DEVIATIONS FROM A UNIT ROOT, WITH AN APPLICATION TO THE ASYMPTOTIC SIZE OF NONPARAMETRIC TESTS
    Econom. Theory (IF 1.17) Pub Date : 2019-10-21
    James A. Duffy

    We provide new asymptotic theory for kernel density estimators, when these are applied to autoregressive processes exhibiting moderate deviations from a unit root. This fills a gap in the existing literature, which has to date considered only nearly integrated and stationary autoregressive processes. These results have applications to nonparametric predictive regression models. In particular, we show

    更新日期:2019-10-21
  • ON EFFICIENCY GAINS FROM MULTIPLE INCOMPLETE SUBSAMPLES
    Econom. Theory (IF 1.17) Pub Date : 2019-09-04
    Saraswata Chaudhuri

    Cost-effective survey methods such as multi(R)-phase sampling typically generate samples that are collections of monotonic subsamples, i.e., the variables observed for the units in subsample r are also observed for the units in subsample r + 1 for r = 1,…,R – 1. These subsamples represent subpopulations that can be systematically different if the selection of a unit in each phase of sampling depends

    更新日期:2019-09-04
  • QUANTILOGRAMS UNDER STRONG DEPENDENCE
    Econom. Theory (IF 1.17) Pub Date : 2019-08-30
    Ji Hyung Lee; Oliver Linton; Yoon-Jae Whang

    We develop the limit theory of the quantilogram and cross-quantilogram under long memory. We establish the sub-root-n central limit theorems for quantilograms that depend on nuisance parameters. We propose a moving block bootstrap (MBB) procedure for inference and establish its consistency, thereby enabling a consistent confidence interval construction for the quantilograms. The newly developed reduction

    更新日期:2019-08-30
  • IDENTIFYING LATENT GROUPED PATTERNS IN COINTEGRATED PANELS
    Econom. Theory (IF 1.17) Pub Date : 2019-07-22
    Wenxin Huang; Sainan Jin; Liangjun Su

    We consider a panel cointegration model with latent group structures that allows for heterogeneous long-run relationships across groups. We extend Su, Shi, and Phillips (2016, Econometrica 84(6), 2215–2264) classifier-Lasso (C-Lasso) method to the nonstationary panels and allow for the presence of endogeneity in both the stationary and nonstationary regressors in the model. In addition, we allow the

    更新日期:2019-07-22
  • A PORTMANTEAU TEST FOR CORRELATION IN SHORT PANELS
    Econom. Theory (IF 1.17) Pub Date : 2019-07-22
    Koen Jochmans

    Inoue and Solon (2006, Econometric Theory 22, 835–851) presented a test against serial correlation of arbitrary form in fixed-effect models for short panel data. Implementing the test requires choosing a regularization parameter that may severely affect power and for which no optimal selection rule is available. We present a modified version of their test that does not require any regularization parameter

    更新日期:2019-07-22
  • TRUNCATED SUM OF SQUARES ESTIMATION OF FRACTIONAL TIME SERIES MODELS WITH DETERMINISTIC TRENDS
    Econom. Theory (IF 1.17) Pub Date : 2019-07-01
    Javier Hualde; Morten Ørregaard Nielsen

    We consider truncated (or conditional) sum of squares estimation of a parametric model composed of a fractional time series and an additive generalized polynomial trend. Both the memory parameter, which characterizes the behavior of the stochastic component of the model, and the exponent parameter, which drives the shape of the deterministic component, are considered not only unknown real numbers but

    更新日期:2019-07-01
  • LARGE SYSTEM OF SEEMINGLY UNRELATED REGRESSIONS: A PENALIZED QUASI-MAXIMUM LIKELIHOOD ESTIMATION PERSPECTIVE
    Econom. Theory (IF 1.17) Pub Date : 2019-05-27
    Qingliang Fan; Xiao Han; Guangming Pan; Bibo Jiang

    In this article, using a shrinkage estimator, we propose a penalized quasi-maximum likelihood estimator (PQMLE) to estimate a large system of equations in seemingly unrelated regression models, where the number of equations is large relative to the sample size. We develop the asymptotic properties of the PQMLE for both the error covariance matrix and model coefficients. In particular, we derive the

    更新日期:2019-05-27
  • IDENTIFICATION AND ESTIMATION IN A THIRD-PRICE AUCTION MODEL
    Econom. Theory (IF 1.17) Pub Date : 2019-03-08
    Andreea Enache; Jean-Pierre Florens

    The first novelty of this paper is that we show global identification of the private values distribution in a sealed-bid third-price auction model using a fully nonparametric methodology. The second novelty of the paper comes from the study of the identification and estimation of the model using a quantile approach. We consider an i.i.d. private values environment with risk-averse bidders. In the first

    更新日期:2019-03-08
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