Asymptotic analysis ≈ Asymptotic analysis
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DYNAMIC LINEAR PANEL REGRESSION MODELS WITH INTERACTIVE FIXED EFFECTS Open
We analyze linear panel regression models with interactive fixed effects and predetermined regressors, for example lagged-dependent variables. The first-order asymptotic theory of the least squares (LS) estimator of the regression coeffici…
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Linear Serial Rank Tests for Randomness Against Arma Alternatives Open
In this paper we introduce a class of linear serial rank statistics for the problem of testing white noise against alternatives of ARMA serial dependence. The asymptotic normality of the proposed statistics is established, both under the n…
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Inference for subvectors and other functions of partially identified parameters in moment inequality models Open
This paper introduces a bootstrap-based inference method for functions of the parameter vector in a moment (in)equality model. These functions are restricted to be linear for two-sided testing problems, but may be nonlinear for one-sided t…
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Bootstrap Methods in Econometrics Open
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstra…
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Asymptotic theory of rerandomization in treatment–control experiments Open
Significance Rerandomization refers to experimental designs that enforce covariate balance. This paper studies the asymptotic properties of the difference-in-means estimator under rerandomization, based on the randomness of the treatment a…
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Asymptotic Dimension Open
This chapter considers the notion of asymptotic dimension. It first provides an overview of asymptotic dimension before explaining the asymptotic dimensions of free abelian and nonabelian groups, free groups, groups, integers, metric space…
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Inference on higher-order spatial autoregressive models with increasingly many parameters Open
This paper develops consistency and asymptotic normality of parameter estimates for a higher-order spatial autoregressive model whose order, and number of regressors, are allowed to approach infinity slowly with sample size. Both least squ…
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Applications of distance correlation to time series Open
The use of empirical characteristic functions for inference problems, including estimation in some special parametric settings and testing for goodness of fit, has a long history dating back to the 70s. More recently, there has been renewe…
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Asymptotic solutions in asymptotic safety Open
We explain how to find the asymptotic form of fixed point solutions in\nfunctional truncations, in particular $f(R)$ approximations. We find that\nquantum fluctuations do not decouple at large $R$, typically leading to\nelaborate asymptoti…
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Series estimation under cross-sectional dependence Open
An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, i…
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Non-Parametric Estimation of Finite Mixtures from Repeated Measurements Open
Summary This paper provides methods to estimate finite mixtures from data with repeated measurements non-parametrically. We present a constructive identification argument and use it to develop simple two-step estimators of the component di…
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Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation Open
The exceedance probability of extreme daily precipitation is usually quantified assuming asymptotic behaviours. Non-asymptotic statistics, however, would allow us to describe extremes with reduced uncertainty and to establish relations bet…
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Semi-supervised inference: General theory and estimation of means Open
We propose a general semi-supervised inference framework focused on the estimation of the population mean. As usual in semi-supervised settings, there exists an unlabeled sample of covariate vectors and a labeled sample consisting of covar…
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Semiparametric GEE analysis in partially linear single-index models for longitudinal data Open
In this article, we study a partially linear single-index model for longitudinal data under a general framework which includes both the sparse and dense longitudinal data cases. A semiparametric estimation method based on a combination of …
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Nonparametric estimation and inference under shape restrictions Open
Economic theory often provides shape restrictions on functions of interest in applications, such as monotonicity, convexity, non-increasing (non-decreasing) returns to scale, or the Slutsky inequality of consumer theory; but economic theor…
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Oscillatory integrals with uniformity in parameters Open
We prove a sharp asymptotic formula for certain oscillatory integrals that may be approached using the stationary phase method. The estimates are uniform in terms of auxiliary parameters, which is crucial for application in analytic number…
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On uniform asymptotic risk of averaging GMM estimators Open
This paper studies the averaging GMM estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is…
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Diagnostic Checking in Multivariate ARMA Models With Dependent Errors Using Normalized Residual Autocorrelations Open
In this paper, we derive the asymptotic distribution of normalized residual empirical autocovariances and autocorrelations under weak assumptions on the noise. We propose new portmanteau statistics for vector autoregressive moving average …
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Asymptotic Theory of Eigenvectors for Random Matrices With Diverging Spikes Open
Characterizing the asymptotic distributions of eigenvectors for large random matrices poses important challenges yet can provide useful insights into a range of statistical applications. To this end, in this paper we introduce a general fr…
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Assessing Model Fit in Latent Class Analysis When Asymptotics Do Not Hold Open
The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asym…
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Two-step semiparametric empirical likelihood inference Open
In both parametric and certain nonparametric statistical models, the empirical likelihood ratio satisfies a nonparametric version of Wilks’ theorem. For many semiparametric models, however, the commonly used two-step (plug-in) empirical lik…
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Eigenvalue spectrum of the spheroidal harmonics: A uniform asymptotic analysis Open
The spheroidal harmonics $S_{lm}(\theta;c)$ have attracted the attention of both physicists and mathematicians over the years. These special functions play a central role in the mathematical description of diverse physical phenomena, inclu…
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Kernel estimates of nonparametric functional autoregression models and their bootstrap approximation Open
This paper considers a nonparametric functional autoregression model of order one. Existing contributions addressing the problem of functional time series prediction have focused on the linear model and literatures are rather lacking in th…
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On Asymptotic Theory of Beams, Plates and Shells Open
Bases of asymptotic theory of beams, plates and shells are stated. The comparison with classic theory is conducted. New classes of thin bodies problems, for which hypotheses of classic theory are not applicable, are considered. By the asym…
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Asymptotic distribution of ∆AUC, NRIs, and IDI based on theory of U‐statistics Open
The change in area under the curve (∆AUC), the integrated discrimination improvement (IDI), and net reclassification index (NRI) are commonly used measures of risk prediction model performance. Some authors have reported good validity of a…
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UNIT ROOT INFERENCE FOR NON-STATIONARY LINEAR PROCESSES DRIVEN BY INFINITE VARIANCE INNOVATIONS Open
The contribution of this paper is two-fold. First, we derive the asymptotic null distribution of the familiar augmented Dickey-Fuller [ADF] statistics in the case where the shocks follow a linear process driven by infinite variance innovat…
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An asymptotic analysis of distributed nonparametric methods Open
We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently. We do this in the context of a distributed version of the classical signal-in-Gaussian-white-noise model, whic…
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Estimation in nonlinear regression with Harris recurrent Markov chains Open
In this paper, we study parametric nonlinear regression under the Harris recurrent Markov chain framework. We first consider the nonlinear least squares estimators of the parameters in the homoskedastic case, and establish asymptotic theor…
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DETERMINING THE COINTEGRATION RANK IN HETEROSKEDASTIC VAR MODELS OF UNKNOWN ORDER Open
We investigate the asymptotic and finite sample properties of a number of methods for estimating the cointegration rank in integrated vector autoregressive systems of unknown autoregressive order driven by heteroskedastic shocks. We allow …
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Trends in Extreme Value Indices Open
We consider extreme value analysis for independent but nonidentically distributed observations. In particular, the observations do not share the same extreme value index. Assuming continuously changing extreme value indices, we provide a n…