期货市场套期保值线性策略的困境:理论评述

对传统套期保值理论框架中的线性策略进行理论评述,阐述期货市场上存在的非线性特征对传统线性策略所构成的挑战。最后,提出依据连接函数构建非线性套期保值策略的思路。通过案例分析表明,非线性策略能够结合市场不对称相关特征,具备解决市场极端变化以及不对称相关问题的能力。     

Three Corrected Score Tests for Generalized Linear Models with Dispersion Covariates

We develop three corrected score tests for generalized linear models with dispersion covariates, thus generalizing the results of Cordeiro , Ferrari and Paula (1993) and Cribari-Neto and Ferrari (1995) . We present, in matrix notation, general formulae for the coefficients which define the corrected statistics. The formulae only require simple operations on matrices and can be used to obtain analytically closed-form corrections for score test statistics in a variety of special generalized linear models with dispersion covariates. They also have advantages for numerical purposes since our formulae are readily computable using a language supporting numerical linear algebra. Two examples, namely, iid sampling without covariates on the mean or dispersion parameter oand one-way classification models, are given. We also present some simulations where the three corrected tests perform better than the usual score test, the likelihood ratio test and its Bartlett corrected version. Finally, we present a numerical example for a data set discussed by Simonoff and Tsai (1994) .     

Too Little or too much? Exploring U‐shaped Relationships between Corporate Environmental Performance and Corporate Financial Performance

Despite 40 years of research on the relationship between corporate environmental performance (CEP) and corporate financial performance (CFP), there is no generally accepted theoretical framework that explains the contradictory results that have emerged. This unsatisfactory status may be attributed to the fact that linear models dominate the research. Based on an international sample of 2361 firm‐years from 2008 to 2012, we find empirical evidence of a non‐linear, specifically a U‐shaped, relationship between carbon performance and profitability as well as between waste intensity and profitability. The same result holds for the relationship between carbon performance and stock market performance, but solely for manufacturing industries. Our empirical findings provide evidence for the theoretical framework of a ‘too‐little‐of‐a‐good‐thing’ (TLGT) effect, which indicates that the type of relationship (positive, negative) depends on the level of CEP. More precisely, there is a negative CEP–CFP relationship for companies with low CEP and a positive association for high CEP. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment     

Methods for Scalar‐on‐Function Regression

Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images and so on are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorising the basic model types as linear, non‐linear and non‐parametric. We discuss publicly available software packages and illustrate some of the procedures by application to a functional magnetic resonance imaging data set.     

Instrumental variable estimation based on grouped data

The paper considers the estimation of the coefficients of a single equation in the presence of dummy intruments. We derive pseudo ML and GMM estimators based on moment restrictions induced either by the structural form or by the reduced form of the model. The performance of the estimators is evaluated for the non-Gaussian case. We allow for heteroscedasticity. The asymptotic distributions are based on parameter sequences where the number of instruments increases at the same rate as the sample size. Relaxing the usual Gaussian assumption is shown to affect the normal asymptotic distributions. As a result also recently suggested new specification tests for the validity of instruments depend on Gaussianity. Monte Carlo simulations confirm the accuracy of the asymptotic approach.     

The out-of-sample forecasting performance of nonlinear models of regional housing prices in the US

This article provides out-of-sample forecasts of linear and nonlinear models of US and four Census subregions’ housing prices. The forecasts include the traditional point forecasts, but also include interval and density forecasts, of the housing price distributions. The nonlinear smooth-transition autoregressive model outperforms the linear autoregressive model in point forecasts at longer horizons, but the linear autoregressive and nonlinear smooth-transition autoregressive models perform equally at short horizons. In addition, we generally do not find major differences in performance for the interval and density forecasts between the linear and nonlinear models. Finally, in a dynamic 25-step ex-ante and interval forecasting design, we, once again, do not find major differences between the linear and nonlinear models. In sum, we conclude that when forecasting regional housing prices in the United States, generally the additional costs associated with nonlinear forecasts outweigh the benefits for forecasts only a few months into the future.     

Was the recent downturn in US real GDP predictable?

This article uses a small set of variables – real GDP, the inflation rate and the short-term interest rate – and a rich set of models – atheoretical (time series) and theoretical (structural), linear and nonlinear, as well as classical and Bayesian models – to consider whether we could have predicted the recent downturn of the US real GDP. Comparing the performance of the models to the benchmark random-walk model by root mean-square errors, the two structural (theoretical) models, especially the nonlinear model, perform well on average across all forecast horizons in our ex post, out-of-sample forecasts, although at specific forecast horizons certain nonlinear atheoretical models perform the best. The nonlinear theoretical model also dominates in our ex ante, out-of-sample forecast of the Great Recession, suggesting that developing forward-looking, microfounded, nonlinear, dynamic stochastic general equilibrium models of the economy may prove crucial in forecasting turning points.     

Nonparametric fixed effects model for panel data with locally stationary regressors

We develop methods for inference in nonparametric time-varying fixed effects panel data models that allow for locally stationary regressors and for the time series length $T$ and cross-section size $N$ both being large. We first develop a pooled nonparametric profile least squares dummy variable approach to estimate the nonparametric function, and establish the optimal convergence rate and asymptotic normality of the resultant estimator. We then propose a test statistic to check whether the bivariate nonparametric function is time-varying or the time effect is separable, and derive the asymptotic distribution of the proposed test statistic. We present several simulated examples and two real data analyses to illustrate the finite sample performance of the proposed methods.     

农村剩余劳动力跨省迁移的实证分析

本文将农民作为市场经济的理性主体,利用第五次人口普查的数据,使用线性化的logit模型,对影响农村劳动力跨省迁移的概率和流向等因素进行实证分析。结果表明,迁出地的人均耕地、劳动者受教育状况、农业劳动生产率、城镇居民收入以及两地距离等对农村劳动力的跨省迁移具有显著影响。     

地方经济发展与企业资本结构选择——来自我国上市公司的经验证据

本文选择1994～2004我国上市公司数据为研究样本,检验地方经济发展以及地方经济发展因素与公司内因素的交互作用如何影响企业资本结构。本文发现,人均居民消费水平越高,企业资产负债率越高;人均GDP较高的地区,人均居民消费水平对资产负债率影响较小,企业资产负债率较高;企业非债务税盾越大,人均GDP对其资产负债率影响越小;城乡储蓄年度余额越高的地区,企业资产负债率越高;随着地区人均GDP的提高,城乡储蓄对企业资产负债率的影响减小;资本形成总额越高、企业资产负债率越高。