中国区域生态效率评价及影响因素实证分析——以2000-2006年省际数据为例

以2000-2006年中国29个省际截面数据为样本,采用因子分析赋权的研究方法,评价中国区域生态效率的差异性。进一步以区域生态效率评价值为因变量,利用线性回归模型,分析环保资金投入,环境政策及产业结构对生态效率的影响。研究结论表明:(1)中国区域生态效率差异较为明显,总体呈东高西低的格局;(2)环保资金投入与产业结构调整均对生态效率改善有积极影响;(3)以排污费为代表的环境经济政策对生态效率改善影响并不显著。并依据以上研究发现,提出了相应的政策建议。     

Robustness of linear models in dynamic multivariate predictions

Linear aggregation models employing unit and equal weights have been shown to be superior to human decisions in a surprising range of decision situations. In addition, decisions based on these models have often been found to be superior to those based on linear regression models (LRMs). This general issue was explored for repetitive decisions in production planning. The problem considered differs in several aspects from the types of problems investigated previously: (1) the problem is dynamic rather than static; (2) a set (or vector) of interactive decisions dependent on previous decisions is required to be made, where a decision in stage t, the dependent variable, becomes an independent variable in stage t + 1; and (3) the criterion function is cost with a quadratic loss function (rather than the correlation measure of R²). Moreover, since repetitive decisions were involved, the parameters of the models were estimated using past human decisions. These were used to predict specific values of the decision variables (rather than rank order), which in turn were employed recursively to predict values of the decision variables at subsequent stages. While decisions from equal weighting rules were found to be superior to human decisions and not greatly inferior to decisions from linear regression models, decisions from unit weighting rules performed poorly. The rationale for such performance is discussed, indicating that previous theoretical and empirical research on linear weighting models is not generally applicable to dynamic, multivariate interactive decisions problems with lagged variables.     

On the scientific study of small samples: Challenges confronting quantitative and qualitative methodologies

Often phenomena that are important to understand and predict are very rare. Rare events can prove difficult to analyze systematically because they do not generate many sampling observations. In this article I examine how small sample sizes can be studied scientifically. The article begins with an explanation of the distinction between research and science. I then bring to the fore the importance of counterfactual comparisons and outline the nature of the methodological problems posed by the study of small samples. These problems include challenges related to using a single case, small sample sizes, selecting on the dependent variable, regression toward the mean, explaining a variable with a constant, and using the same data to both generate and test hypotheses. I provide potential resolutions to these problems which are: (a) employing matched controls; (b) shifting or widen the category of inquiry; (c) selecting variables based on variance in the independent variable; (d) including counterfactuals; (e) ensuring that both independent and dependent variables demonstrate variation; and (f) testing potential hypotheses against data sets that are fully independent of those used to generate the hypotheses. I conclude with a discussion of future directions for undertaking a more scientific approach to using small samples.     

The Accuracy of Cross-Validation Results in Forecasting

The widespread use of regression analysis as a business forecasting tool and renewed interest in the use of cross-validation to aid in regression model selection make it essential that decision makers fully understand methods of cross-validation in forecasting, along with the advantages and limitations of such analysis. Only by fully understanding the process can managers accurately interpret the important implications of statistical cross-validation results in their determination of the robustness of regression forecasting models. Through a multiple regression analysis of a large insurance company's customer database, the Herzberg equation for determining the criterion of validity [11] and analysis of samples of different size from the two regions covered by the database, we illustrate the use of statistical cross-validation and test a set of factors hypothesized to be related to the statistical accuracy of validation. We find that increasing sample size will increase reliability. When the magnitude of population model differences is small, validation results are found to be unreliable, and increasing sample size has little or no effect on reliability. In addition, the relative fit of the model for the derivative sample and the validation sample has an impact on validation accuracy, and should be used as an indicator of when further analysis should be undertaken. Furthermore, we find that the probability distribution of the population independent variables has no effect on validation accuracy.     

SPECIFICATION AND ESTIMATION OF LOGICALLY CONSISTENT LINEAR MODELS

The implications of constrained dependent and independent variables for model parameters are examined. In the context of linear model systems, it is shown that polyhedral constraints on the dependent variables will hold over the domain of the independent variables when a set of polyhedral constraints is satisfied by the model parameters. This result may be used in parameter estimation, in which case all predicted values of the dependent variables are consistent with constraints on the actual values. Also, the implicit constraints that define the set of parameters for many commonly used linear stochastic models with an error term yield values of the dependent variables consistent with the explicit constraints. Models possessing these properties are termed “logically consistent”.     

基于季节调整和Holt-Winters的月度负荷预测方法

针对负荷序列中异常数据会导致模型误设或参数估计发生偏差的问题,提出利用季节调整方法,先对原始负荷序列进行季节调整,获得消除离群值、节假日影响的季节调整后序列和季节成分序列;然后用改进的Holt-Winters方法对季节调整后成分进行预测,用虚拟回归方法预测季节成分序列;最后对各成分预测结果重构得到最终预测结果的月度负荷预测方法。通过实例检验,提出的方法能明显提高预测精度,预测效果要优于季节性Holt-Winters、SARIMA、神经网络、支持向量机等模型。     

SIMULATION EXPLORATION OF THE POWER OF MARGINAL REGRESSION ANALYSIS*

In either simple or multivariate regression models, measured coefficients represent a kind of average of co-variant relationships implicit in the data. For time-series data, if co-variant relationships follow no particular pattern over the sample period, use of this average in forecasting is rational. However, if relationships among independent and dependent variables is undergoing change over time, then measured regression coefficients are likely to be poor tools for prediction. This paper is an exploratory study of this general problem area. It develops implications as to the predictive impact of the problem and offers preliminary suggestions as to a method of developing predictions under such circumstances.     

PIECEWISE APPROXIMATION FUNCTIONS AN EDUCATIONAL NOTE*

This paper gives a survey of piecewise approximation functions. Aside from the use of dummy variables, little research has employed such functions. Piecewise functions permit the dependent variable of a functional relationship to take on different forms with respect to the independent variable on various subintervals of the domain of the independent variable. Three types of piecewise approximation functions are considered: dummy variables, piecewise linear regressions and cubic splines. The theoretical and empirical properties of such functions are discussed within the context of an illustrative example involving the increase in ridership over time on urban mass transit systems. Dummy variables take account of shifts in the structural relation in a discontinuous fashion. Piecewise regressions and cubic splines allow for structural shifts without discontinuities arising. Tests are performed to determine whether a linear, quadratic, or cubic piecewise continuous function should be employed. In addition a test is employed to see if structural change occurs in the functional relation at the shift points.     

基于多尺度组合模型的铜价预测研究

铜价预测是国际大宗商品市场研究的一个重要领域。本文运用经验模态分解法（EMD）、人工神经网络（ANN）、支持向量机（SVM）和时间序列方法,基于分解-重构-集成的思想,构建了一个多尺度组合预测模型。在模型构建过程中,提出了运用游程判定法对分量序列进行重构的新思路。然后,运用此模型对LME铜价波动特点和走势进行分析：将铜价序列分解并重构成高频、低频和趋势三个部分,并从不规则因素、重大事件以及长期趋势三个角度解释了重构项的波动特征;实证分析表明,与灰色模型GM（1,1）、Elman神经网络方法等单模型,以及ARIMA-SVM组合模型相比,多尺度组合模型取得了最好的预测效果。     

Varying Risk Comparison Elements: Effects on Public Reactions

Expanding a limited empirical base on effects of risk comparisons, a pilot experiment explored how varying elements of such comparisons might influence public response. The scenario used was a hypothetical trial of an asbestos-installing firm for putting students and staff at a junior high school at "unreasonable risk," first used by Slovic et al. (1990). Study participants played the role of jurors in the trial, asked to rate the risk, the firm's guilt, whether it should be made liable for future health effects, whether the school should be occupied in its current condition, and whether the asbestos should be removed at a cost of 3 million dollars. Opportunity samples of New Jersey residents (n= 309) received information intended to vary four comparison attributes: number of dimensions of comparison; single versus multiple ways of expressing mortality; a narrative to explain and justify the risk comparison; and the hypothetical role of the participant (juror vs. parent). The dependent variable was a hazard scale constructed from four of the five postcomparison judgments. ANOVA found format variations swamped in their effect by concern about asbestos, with the absence of a narrative and the parental role being the only attributes that increased negative risk reactions. Multiple regression analyses found that multidimensionality, narrative, role, and the presence of any risk comparison at all had significant effects when these (plus death formats) were the only independent variables. However, only multidimensionality (which increased negative reactions) retained significance when asbestos concern, risk beliefs (no safe level of exposure to a carcinogen; any exposure leads to cancer), and demographic variables were added to the analysis. Concern and risk beliefs alone explained 33% of variance in hazard scores; adding demographics and risk comparison variables only raised explained variance to 36% (having children at home and being exposed to multidimensional risk comparisons raised scores; age and income lowered them). The results underline the potentially small effect of risk comparison information on risk views (at least between subjects), but offer some insight into aspects of message design left unexplored in empirical literature to date.     

USING SPECTRAL ANALYSIS FOR FORECAST MODEL SELECTION

Spectral analysis can be used to determine the general class of forecasting models which should be considered for purposes of generating forecasts of a marketing time series. Power spectral estimates and cross-spectral estimates can aid in selecting the most appropriate model when several variations of the predetermined class are under consideration.     

基于可变区间权重的中期用电量半参数预测模型

由于数据变化规律的多样性,中期电力负荷的波动有着不同于短期、长期负荷的特点。基于电力系统复杂性的研究视角,重点讨论了中期负荷预测过程中模型的不确定性、参数的时变特性以及负荷波动的周期性规律。根据中期负荷的数据特性,建立了基于非参数修匀的半参数模型,定义了函数区间的划分粒度以及模型权重的求解方法,提出了基于可变区间权重的动态预测方法,给出了基于经验模态分解和波动能量分析的噪声序列提取、检验方法。试验研究结果表明,气候因素对用电消耗的影响最大,经济因素次之;从选取的指标来看,不同时期的影响因素对于模型的解释能力是时变的;所提方法能够对电力负荷进行精确的多粒度、多维度分析,进而掌握其局部变化规律,可有效用于电力系统中期负荷预测。     

ADMISSION STANDARDS FOR GRADUATE STUDY IN MANAGEMENT

This paper reports the results of a study undertaken to evaluate the validity of requirements for admission to Florida State University's Graduate Program in Management offered by its School of Business in the Cape Kennedy Area of Florida. A multiple regression study was made using as independent variables students' undergraduate grade point average (UGPA), Graduate Record Examination aptitude test score (GRE), and age (AGE) at time of admission. The dependent variable was graduate grade point average (GGPA). The study revealed a significant positive correlation between GRE and GGPA. The correlation between AGE and GGPA was also significant but was positive for values of AGE up to approximately 29 and negative thereafter. UGPA was not even marginally significant.     

A quadratic interval logit model for forecasting bankruptcy

This paper proposes a quadratic interval logit model (or quadratic interval logistic regression analysis) based on a quadratic programming approach to deal with binary response variables. This model combines the advantages of logit (or logistic regression) and Tanaka's quadratic interval regression model. As a demonstration, we applied this model to forecasting corporate distress in the UK. The results show that this model can support the logit model to discriminate between groups, and it provides more information to researchers.     

Quantile Regression under Misspecification,with an Application to the U.S. Wage Structure

Quantile regression (QR) fits a linear model for conditional quantiles just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean‐squared error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using quantile regression with discrete covariates suggests that QR may have a similar property, but the exact nature of the linear approximation has remained elusive. In this paper, we show that QR minimizes a weighted mean‐squared error loss function for specification error. The weighting function is an average density of the dependent variable near the true conditional quantile. The weighted least squares interpretation of QR is used to derive an omitted variables bias formula and a partial quantile regression concept, similar to the relationship between partial regression and OLS. We also present asymptotic theory for the QR process under misspecification of the conditional quantile function. The approximation properties of QR are illustrated using wage data from the U.S. census. These results point to major changes in inequality from 1990 to 2000.     

基于动态模型平均的大豆期货价格预测研究

针对大豆期货价格波动的复杂性及影响因素的多元性，本文将动态模型平均理论引入大豆期货价格分析与预测研究中，通过动态选择解释变量和系数时变程度，在有效控制模型和系数不确定性的同时，最大限度综合利用大豆期货市场内外部信息，以提高大豆期货价格预测准确度。具体的，本文提出一套基于动态模型平均理论的大豆期货价格影响因素与预测分析框架，从期货市场和经济环境等两方面准确地识别出大豆期货价格影响因素的时变特征，进而构建大豆期货价格预测模型，并通过预测误差指标和Diebold-Mariano检验法评估其与基准模型的预测能力。研究结果表明，动态模型平均理论在有效剖析大豆期货价格影响因素的时变特征的同时，能明显提升大豆期货价格预测准确度。     

个人-组织匹配在外资医药企业员工离职倾向决定中的调节效应研究

本文采用我国外资医药企业员工样本,运用层级回归方法对员工离职倾向的主要结构化变量和环境变量的解释能力进行了检验,在此基础上提出并检验了个人-组织匹配度对员工离职倾向的解释能力,进而验证了个人-组织匹配度在员工离职倾向决定过程中的调节效应.     

Panel Data Discrete Choice Models with Lagged Dependent Variables

In this paper, we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables, lags of the endogenous dependent variable as well as unobservable individual‐specific effects. For the binary logit model with the dependent variable lagged only once, Chamberlain (1993) gave conditions under which the model is not identified. We present a stronger set of conditions under which the parameters of the model are identified. The identification result suggests estimators of the model, and we show that these are consistent and asymptotically normal, although their rate of convergence is slower than the inverse of the square root of the sample size. We also consider identification in the semiparametric case where the logit assumption is relaxed. We propose an estimator in the spirit of the conditional maximum score estimator (Manski (1987)) and we show that it is consistent. In addition, we discuss an extension of the identification result to multinomial discrete choice models, and to the case where the dependent variable is lagged twice. Finally, we present some Monte Carlo evidence on the small sample performance of the proposed estimators for the binary response model.     

Structural Nonparametric Cointegrating Regression

Nonparametric estimation of a structural cointegrating regression model is studied. As in the standard linear cointegrating regression model, the regressor and the dependent variable are jointly dependent and contemporaneously correlated. In nonparametric estimation problems, joint dependence is known to be a major complication that affects identification, induces bias in conventional kernel estimates, and frequently leads to ill‐posed inverse problems. In functional cointegrating regressions where the regressor is an integrated or near‐integrated time series, it is shown here that inverse and ill‐posed inverse problems do not arise. Instead, simple nonparametric kernel estimation of a structural nonparametric cointegrating regression is consistent and the limit distribution theory is mixed normal, giving straightforward asymptotics that are useable in practical work. It is further shown that use of augmented regression, as is common in linear cointegration modeling to address endogeneity, does not lead to bias reduction in nonparametric regression, but there is an asymptotic gain in variance reduction. The results provide a convenient basis for inference in structural nonparametric regression with nonstationary time series when there is a single integrated or near‐integrated regressor. The methods may be applied to a range of empirical models where functional estimation of cointegrating relations is required.     

基于用户在线交易意愿的虚拟社区电子商务实证研究

虚拟社区电子商务的市场发展还在起步阶段,提高用户在线交易意愿是实现虚拟社区电子商务的关键。本文构建了以虚拟社区网站质量为前因认知变量,用户满意度和信任度为中介态度变量,社区用户黏度和用户在线交易意愿为行为因变量的影响关系模型。通过在线调查某虚拟社区用户并获得204份有效问卷,实证分析了虚拟社区网站质量对用户满意度和信任度的显著影响,证实了社区黏度是用户满意度与在线交易意愿影响关系的完全中介变量,但是用户信任度对社区黏度和在线交易意愿没有直接和显著的影响作用。本研究为虚拟社区电子商务发展及市场营销活动提供了相应的策略。