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半参数部分线性变系数众数回归AI 解读

We in this paper propose a semiparametric partially linear varying coefficient (SPLVC) modal regression, in which the conditional mode function of the response variable given covariates admits a partially linear varying coefficient structure. In comparison to existing regressions, the newly developed SPLVC modal regression captures the “most likely” effect and provides superior prediction performance when the data distribution is skewed. The consistency and asymptotic…

结构突变下的最优预测AI 解读

Summary This paper develops an optimal combined estimator to forecast out‐of‐sample under structural breaks. When it comes to forecasting, using only the postbreak observations after the most recent break point may not be optimal. In this paper, we propose a new estimation method that exploits the prebreak information. In particular, we show how to combine the estimator using the full‐sample (i.e., both the prebreak and postbreak data) and the estimator using only the…

股权风险溢价预测：全局历史平均、局部历史平均与约束AI 解读

The equity premium, return on equity minus return on risk-free asset, is expected to be positive. We consider imposing such positivity constraint in local historical average (LHA) in nonparametric kernel regression framework. It is also extended to the semiparametric single index model when multiple predictors are used. We construct the constrained LHA estimator via an indicator function which operates as “model-selection” between the unconstrained LHA and the bound of the…

单调性约束下的非参数与半参数回归：估计与预测AI 解读

This paper considers nonparametric and semiparametric regression models subject to monotonicity constraint. We use bagging as an alternative approach to Hall and Huang (2001). Asymptotic properties of our proposed estimators and forecasts are established. Monte Carlo simulation is conducted to show their finite sample performance. An application to predicting equity premium is taken for illustration. We introduce a new forecasting evaluation criterion based on the second…

函数系数工具变量模型的局部线性 GMM 估计及教育回报应用AI 解读

We consider the local linear generalized method of moment (GMM) estimation of functional coefficient models with a mix of discrete and continuous data and in the presence of endogenous regressors. We establish the asymptotic normality of the estimator and derive the optimal instrumental variable that minimizes the asymptotic variance-covariance matrix among the class of all local linear GMM estimators. Data-dependent bandwidth sequences are also allowed for. We propose a…

基于非参数拟合优度的条件异方差检验AI 解读

In this paper we propose a new nonparametric test for conditional heteroskedasticity based on a measure of nonparametric goodness-of-fit (R 2 ) that is obtained from the local polynomial regression of the residuals from a parametric regression on some covariates. We show that after being appropriately standardized, the nonparametric R 2 is asymptotically normally distributed under the null hypothesis and a sequence of Pitman local alternatives. We also prove the consistency…

动态条件协方差的估计与预测：半参数多变量模型AI 解读

We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct specification of PCC models, and study its asymptotic properties. We evaluate the finite sample performance of our test and SCC estimator and compare…

时间序列条件方差的半参数估计量AI 解读

We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecification our estimator can…

计量经济学估计量的偏度与峰度AI 解读

We derive the approximate results for two standardized measures of deviation from normality, namely, the skewness and excess kurtosis coefficients, for a class of econometric estimators. The results are built on a stochastic expansion of the moment condition used to identify the econometric estimator. The approximate results can be used not only to study the finite sample behaviour of a particular estimator, but also to compare the finite sample properties of two…

条件不相关性检验AI 解读

We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples.

非参数联立方程模型的局部多项式估计AI 解读

We define a new procedure for consistent estimation of nonparametric simultaneous equations models under the conditional mean independence restriction of Newey et al. [1999. Nonparametric estimation of triangular simultaneous equation models. Econometrica 67, 565-603]. It is based upon local polynomial regression and marginal integration techniques. We establish the asymptotic distribution of our estimator under weak data dependence conditions. Simulation evidence suggests…

This paper proposes a bias-adjusted version of Breusch and Pagan (1980) Lagrange multiplier (LM) test statistic of error cross-section independence, in the case of panel models with strictly exogenous regressors and normal errors. The exact mean and variance of the test indicator of the LM test statistic are provided for the purpose of the bias-adjustments. It is shown that the centring of the LM statistic is correct for fixed T and N. Importantly, the proposed bias-adjusted…

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非参数自相关误差下非参数回归的更有效估计AI 解读

We define a three-step procedure for more efficient estimation of the nonparametric regression mean with nonparametric autocorrelated errors. The procedure is based upon a nonparametric prewhitening transformation of the dependent variable that has to be estimated from the data by a local polynomial technique. We establish the asymptotic distribution of our estimator under weak dependence conditions and show that it is more efficient than the conventional local polynomial…

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不确定性下的矩估计与生产决策AI 解读

The purpose of this paper is to examine production decisions under output price uncertainty. Using a nonparametric estimation technique to estimate the first four moments of the unknown price distribution and applying duality, we provide a simple empirical framework for the analysis of supply and demand decisions under price uncertainty. The model is used to examine the importance of higher moments in the firm's production decisions and to investigate underlying attitudes…

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计量经济学中的改进推断方法AI 解读

Introduction. The Inferential and Decision Framework. The Measure of Performance. Some Alternative Statistical Models, Estimators and Tests. Inequality Estimation and Hypothesis Testing. Inequality Estimation and Hypothesis Testing of the Location Parameter of a Normal Random Variable. General Linear Statistical Model and a Single Linear Inequality Constraint. Inequality Restricted Estimation of Two or More Location Parameters: The Orthonormal Case. An Inequality Restricted…

含风险项模型的计量分析AI 解读

Abstract In this paper we have attempted to provide an integrated approach to the estimation of models with risk terms. It was argued that there exist orthogonality conditions between variables in the information set and higher‐order moments of the unanticipated variable density. These could be exploited to provide consistent estimators of the parameters associated with the risk term. Specifically, it was recommended that an IV estimator should be applied, with instruments…