Econometrics Journal最新文献

In this paper, I estimate the slope coefficient parameter β of the regression model , where the error term e satisfies almost surely and ϕ is an unknown function. It is possible to achieve -consistency for estimating β when ϕ is known up to a finite-dimensional parameter. I present a consistent and asymptotically normal estimator for β, which does not require prescribing a functional form for ϕ, let alone a parametrization. Furthermore, the rate of convergence in probability is equal to at least , and approaches if a certain density is sufficiently differentiable around the origin. This method allows both heteroscedasticity and skewness of the distribution of . Moreover, under suitable conditions, the proposed estimator exhibits an oracle property, namely the rate of convergence is identical to that when ϕ is known. A Monte Carlo study is conducted, and reveals the benefits of this estimator with fat-tailed and/or skewed data. Moreover, I apply the proposed estimator to measure the effect of primogeniture on economic achievement.

In this paper, I investigate the estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. I introduce a uniformly (in the data-generating process) consistent estimator under nearly minimal identifying assumptions. The proposed estimator, called the integrated instrumental variables (IIV) estimator, is a simple weighted least-squares estimator. It does not require the choice of a bandwidth or tuning parameter, or the selection of a finite set of instruments. Thus, the estimator is extremely simple to implement. Monte Carlo evidence supports the theoretical claims and suggests that the IIV estimator is a robust complement to optimal instrumental variables in finite samples. In an application with quarterly UK data, the IIV estimator estimates a positive and significant elasticity of intertemporal substitution and an equally sensible estimate for its reciprocal, in sharp contrast to instrumental variables methods that fail to identify these parameters.

In this paper, we present a semi-parametric identification and estimation method for censored dynamic panel data models of short time periods and their average partial effects with only two periods of data. The proposed method transforms the semi-parametric specification of censored dynamic panel data models into a parametric family of distribution functions of observables without specifying the distribution of the initial condition. Then the censored dynamic panel data models are globally identified under a standard maximum likelihood estimation framework. The identifying assumptions are related to the completeness of the families of known parametric distribution functions corresponding to censored dynamic panel data models. Dynamic tobit models and two-part dynamic regression models satisfy the key assumptions. We propose a sieve maximum likelihood estimator and we investigate the finite sample properties of these sieve-based estimators using Monte Carlo analysis. Our empirical application using the Medical Expenditure Panel Survey shows that individuals consume more health care when their incomes increase, after controlling for past health expenditures.

We develop a specification test of predictive densities, based on the fact that the generalized residuals of correctly specified predictive density models are independent and identically distributed uniform. The proposed sequential test examines the hypotheses of serial independence and uniformity in two stages, wherein the first-stage test of serial independence is robust to violation of uniformity. The approach of the data-driven smooth test is employed to construct the test statistics. The asymptotic independence between the two stages facilitates proper control of the overall type I error of the sequential test. We derive the asymptotic null distribution of the test, which is free of nuisance parameters, and we establish its consistency. Monte Carlo simulations demonstrate excellent finite sample performance of the test. We apply this test to evaluate some commonly used models of stock returns.

Ordinary least-squares (OLS) is well known to produce an inconsistent estimator of the spatial parameter in pure spatial autoregression (SAR). In this paper, we explore the potential of indirect inference to correct the inconsistency of OLS. Under broad conditions, it is shown that indirect inference (II) based on OLS produces consistent and asymptotically normal estimates in pure SAR regression. The II estimator used here is robust to departures from normal disturbances and is computationally straightforward compared with quasi-maximum likelihood (QML). Monte Carlo experiments based on various specifications of the weight matrix show that: (a) the II estimator displays little bias even in very small samples and gives overall performance that is comparable to the QML while raising variance in some cases; (b) II applied to QML also enjoys good finite sample properties; and (c) II shows robust performance in the presence of heavy-tailed error distributions.

This paper surveys recent applications of methods from the theory of optimal transport to econometric problems.

We study the nonparametric estimation of a regression function with nonstationary (integrated or nearly integrated) covariates and the error series of the regressor process following a fractional integrated autoregressive moving average model. A local linear estimation method is developed to estimate the unknown regression function. The asymptotic results of the resulting estimator at both interior points and boundaries are obtained. The asymptotic distribution is mixed normal, associated with the local time of an Ornstein–Uhlenbeck fractional Brownian motion. Furthermore, we study the Nadaraya–Watson estimator and we examine its asymptotic results. As a result, it shares exactly the same asymptotic results as those for the local linear estimator for the zero energy situation. However, for the non-zero energy case, the local linear estimator is superior to the Nadaraya–Watson estimator in terms of optimal convergence rate. We also present a comparison of our results with the conventional results for stationary covariates. Finally, we conduct a Monte Carlo simulation to illustrate the finite sample performance of the proposed estimator.

We propose a Vuong-type model-selection test for models defined by conditional moment restrictions. The moment restrictions that define the models can be standard equality restrictions that point-identify the model parameters, or moment equality or inequality restrictions that partially identify the model parameters. The test uses a new average generalized empirical likelihood criterion function designed to incorporate full restriction of the conditional model. We also introduce a new adjustment to the test statistic that makes it asymptotically pivotal whether the candidate models are nested or non-nested. The test uses simple standard normal critical values and is shown to be asymptotically similar, to be consistent against all fixed alternatives, and to have non-trivial power against -local alternatives. Monte Carlo simulations demonstrate that the finite sample performance of the test is in accordance with the theoretical prediction.

In this paper, we develop tests for a change in an unconditional small quantile (Value-at-Risk, VaR, in financial time series analysis) based on an estimator motivated by extreme value theory. This so-called Weissman estimator allows tests to be applied for extreme VaR, where extant tests mostly fail. In view of applications, we allow for weakly dependent observations. Our test statistics rely on self-normalization, which obviates the need to estimate the complicated asymptotic variance. Consistency is shown under local alternatives, where multiple breaks can occur. A simulation study shows that in finite samples our tests compare favourably in the tail region with extant tests based on order statistic estimators and also with tail index break tests. Two empirical examples serve to illustrate the practical use of our tests.