飞机机体研制费用的偏最小二乘回归分析

本文分析了普通最小二乘回归方法在飞机研制费用建模中所面临的困难和偏最小二乘回归方法在此问题上的优势；简述了偏最小乘法回归方法的原理和建模步骤；结合实例对飞机机体研制费用进行了回归分析，结果表明，偏最小二乘回归模型有效克服了自变量的多重相关性问题，解释性好，精度高，对国产军用飞机的研制费用建模是一种十分有效的方法。     

成分数据偏最小二乘 Logistic回归模型及其应用

成分数据由于存在定和约束和多重共线性，在进行传统的logistic回归建模时遇到了困难。本文在对称logratio变换和偏最小二乘logistic回归技术的基础上，提出了成分数据偏最小二乘logistic回归模型，较好地解决了成分数据的logistic回归建模问题。应用此方法，本文研究了中国三次产业就业结构与人民生活水平之间的关系。结果表明，该模型在有关结构问题的logistic回归建模中具有很好的适用性。     

基于PLS的涡扇发动机直接使用经济性建模的分析

在本文中，笔者拟在气动热力学研究基础上分析影响直接使用经济性的因素，考虑到偏最小二乘回归方法在处理小样本多元数据方面的优势．建立了基于偏最小二乘回归的涡扇发动机直接使用经济性参数模型，确定了发动机工作过程参数和外形参数对直接使用经济性参数的影响重要程度，并进行了实例计算，说明了模型的有效性。     

区域物流通道容量预测研究

将偏最小二乘回归模型与灰色GM(1,1)校正模型进行有机结合,建立组合预测模型,取得了令人满意的预测精度,为区域物流通道容量预测提供了一种新的思路和方法。     

区域物流通道容量预测研究

将偏最小二乘回归模型与灰色GM(1,1)校正模型进行有机结合,建立组合预测模型,取得了令人满意的预测精度,为区域物流通道容量预测提供了一种新的思路和方法.     

冻融作用下混凝土氯离子扩散模型的研究

通过对混凝土氯离子扩散系数影响因素分析总结,采用偏最小二乘回归分析模型,对冻融作用下混凝土氯离子扩散模型进行探究,认为偏最小二乘法可以克服扩散系数影响因素之间多重共线性和样本数量少的问题,根据所建的数学模型表达式系数,在一定情况下,增加降低水灰比、水胶比,或者增加掺合料掺量、含气量,都可以降低冻融作用下混凝土中氯离子扩散系数。     

联立方程模型的工具变量方法质疑与商榷

本文质疑联立方程模型前定变量的工具变量性质：前定变量并不保证与当期行为解释变量的相关性,由其构建的工作回归元所完成的分阶段最小二乘估计因而并非两阶段最小二乘估计。建议按照简约式方程构建工作回归元,其具有模型数理逻辑支持下的可替代意义。工作回归元的不同导致结构式方程分阶段最小二乘估计的不同结果,之于恰好识别方程则揭示了业内关于间接最小二乘估计方法的一个误区。     

基于自相关的自回归模型参数的折扣最小二乘估计

本文简要介绍了基于自相关的自回归模型参数的折扣最小二乘估计。     

基于岭回归的基金投资风格识别研究

文章在分析Sharpe投资风格识别模型的基础上,指出了它的缺陷及改进方法,针对其中的风格资产多重共线性问题,通过比较岭回归、主成分回归与偏最小二乘回归等常用三种处理方法,提出了基于岭回归的弱式风格识别模型,并以开放式股票指数型基金为研究样本,实证结果表明：该方法能较好对基金投资风格进行识别,并发现股票指数型基金大都呈现大盘成长型投资风格,没有发生投资风格漂移现象,这与指数型基金投资理念相一致。     

基于PLS路径模型的中国省域研究生教育竞争力综合评价研究

本文通过构建研究生教育竞争力指标体系与综合评价模型,采用偏最小二乘法结构方程模型(PLS)对2011年我国31个省域研究生教育竞争力进行了实证考察与评估。     

Forecast combination through dimension reduction techniques

This paper considers several methods of producing a single forecast from several individual ones. We compare “standard” but hard to beat combination schemes (such as the average of forecasts at each period, or consensus forecast and OLS-based combination schemes) with more sophisticated alternatives that involve dimension reduction techniques. Specifically, we consider principal components, dynamic factor models, partial least squares and sliced inverse regression.Our source of forecasts is the Survey of Professional Forecasters, which provides forecasts for the main US macroeconomic aggregates. The forecasting results show that partial least squares, principal component regression and factor analysis have similar performances (better than the usual benchmark models), but sliced inverse regression shows an extreme behavior (performs either very well or very poorly).     

Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study

Functional data analysis is a field of growing importance in Statistics. In particular, the functional linear model with scalar response is surely the model that has attracted more attention in both theoretical and applied research. Two of the most important methodologies used to estimate the parameters of the functional linear model with scalar response are functional principal component regression and functional partial least‐squares regression. We provide an overview of estimation methods based on these methodologies and discuss their advantages and disadvantages. We emphasise that the role played by the functional principal components and by the functional partial least‐squares components that are used in estimation appears to be very important to estimate the functional slope of the model. A functional version of the best subset selection strategy usual in multiple linear regression is also analysed. Finally, we present an extensive comparative simulation study to compare the performance of all the considered methodologies that may help practitioners in the use of the functional linear model with scalar response.     

Forecasting national activity using lots of international predictors: An application to New Zealand

We assess the marginal predictive content of a large international dataset for forecasting GDP in New Zealand, an archetypal small open economy. We apply “data-rich” factor and shrinkage methods to efficiently handle hundreds of predictor series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. We find that exploiting a large international dataset can improve forecasts relative to data-rich approaches based on a large national dataset only, and also relative to more traditional approaches based on small datasets. This is in spite of New Zealand’s business and consumer confidence and expectations data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best in handling the international data.     

Partially generalized least squares and two-stage least squares estimators

A class of partially generalized least squares estimators and a class of partially generalized two-stage least squares estimators in regression models with heteroscedastic errors are proposed. By using these estimators a researcher can attain higher efficiency than that attained by the least squares or the two-stage least squares estimators without explicitly estimating each component of the heteroscedastic variances. However, the efficiency is not as high as that of the generalized least squares or the generalized two-stage least squares estimator calculated using the knowledge of the true variances. Hence the use of the term partial.     

Does it pay to venture abroad? Exporting behavior and the performance of firms in Indian industry

Using contemporary data for a firm-level sample of over 600 Indian firms, this paper investigates the impact that an export-orientation has on the profitability of the firms studied. The results, based on a two-stage least squares method, establish a positive and significant relationship between firms' levels of exports and profitability. For firms from developing and transition economies like India it does pay to venture abroad, and the ability to sell goods overseas has a significant impact on firms' economic performance. © 1998 John Wiley & Sons, Ltd.     

Quantile models and estimators for data analysis

Quantile regression is used to estimate the cross sectional relationship between high school characteristics and student achievement as measured by ACT scores. The importance of school characteristics on student achievement has been traditionally framed in terms of the effect on the expected value. With quantile regression the impact of school characteristics is allowed to be different at the mean and quantiles of the conditional distribution. Like robust estimation, the quantile approach detects relationships missed by traditional data analysis. Robust estimates detect the influence of the bulk of the data, whereas quantile estimates detect the influence of co-variates on alternate parts of the conditional distribution. Since our design consists of multiple responses (individual student ACT scores) at fixed explanatory variables (school characteristics) the quantile model can be estimated by the usual regression quantiles, but additionally by a regression on the empirical quantile at each school. This is similar to least squares where the estimate based on the entire data is identical to weighted least squares on the school averages. Unlike least squares however, the regression through the quantiles produces a different estimate than the regression quantiles.     

BALANCED VARIABLE ADDITION IN LINEAR MODELS

This paper studies what happens when we move from a short regression to a long regression in a setting where both regressions are subject to misspecification. In this setup, the least‐squares estimator in the long regression may have larger inconsistency than the least‐squares estimator in the short regression. We provide a simple interpretation for the comparison of the inconsistencies and study under which conditions the additional regressors in the long regression represent a “balanced addition” to the short regression.     

Nonstationary nonlinear heteroskedasticity in regression

This paper considers the regression with errors having nonstationary nonlinear heteroskedasticity. For both the usual stationary regression and the nonstationary cointegrating regression, we develop the asymptotic theories for the least squares methods in the presence of conditional heterogeneity given as a nonlinear function of an integrated process. In particular, we show that the nonstationarity of volatility in the regression errors may induce spuriousness of the underlying regression, if excessive nonstationary volatility is present in the errors. Mild nonstationary volatilities do not render the underlying regression spurious, but their presence makes the least squares estimator asymptotically biased and inefficient and the usual chi-square test invalid.     

Firm Characteristics and Chinese Stocks

This paper presents a comprehensive study on predicting the cross section of Chinese stock market returns with a large panel of 75 individual firm characteristics. We use not only the traditional Fama-MacBeth regression, but also the “big-data” econometric methods: principal component analysis (PCA), partial least squares (PLS), and forecast combination to extract information from all the 75 firm characteristics. These characteristics are important return predictors, with statistical and economic significance. Furthermore, firm characteristics that are related to trading frictions, momentum, and profitability are the most effective predictors of future stock returns in the Chinese stock market.     

Exact inference for the linear model with groupwise heteroscedastic spherical disturbances

Exact nonparametric inference on a single coefficient in a linear regression model, as considered by Bekker (Working Paper, Department of Economics, University of Groningen, 1997), is elaborated for the case of spherically distributed heteroscedastic disturbances. Instead of approximate inference based on feasible weighted least squares, exact inference is formulated based on partial rotational invariance of the distribution of the vector of disturbances. Thus, classical exact inference based on t-statistics is generalized to exact inference that remains valid in a groupwise heteroscedastic context. The approach is applied to a basic two-sample problem, and to the random- and fixed-effects models for panel data.