The discrete Kalman filter applied to linear regression models: statistical considerations and an application

In this paper we show how the Kalman filter, which is a recursive estimation procedure, can be applied to the standard linear regression model. The resulting "Kalman estimator" is compared with the classical least-squares estimator.
The applicability and (dis)advantages of the filter are illustrated by means of a case study which consists of two parts. In the first part we apply the filter to a regression model with constant parameters and in the second part the filter is applied to a regression model with time-varying stochastic parameters. The prediction-powers of various "Kalman predictors" are compared with "least-squares predictors" by using T heil 's prediction-error coefficient U.     

An adaptive empirical likelihood test for parametric time series regression models

We propose an adaptive empirical likelihood (EL) test for a parametric regression model against a class of alternatives for weakly dependent time series observations. The test is formulated by maximizing a standardized version of the EL statistic over a set of smoothing bandwidths. It is demonstrated that the proposed test is able to distinguish the null hypothesis from a series of local alternatives at an optimal rate.     

Natural conjugate priors for the instrumental variables regression model applied to the Angrist–Krueger data

We propose a natural conjugate prior for the instrumental variables regression model. The prior is a natural conjugate one since the marginal prior and posterior of the structural parameter have the same functional expressions which directly reveal the update from prior to posterior. The Jeffreys prior results from a specific setting of the prior parameters and results in a marginal posterior of the structural parameter that has an identical functional form as the sampling density of the limited information maximum likelihood estimator. We construct informative priors for the Angrist–Krueger [1991. Does compulsory school attendance affect schooling and earnings? Quarterly Journal of Economics 106, 979–1014] data and show that the marginal posterior of the return on education in the US coincides with the marginal posterior from the Southern region when we use the Jeffreys prior. This result occurs since the instruments are the strongest in the Southern region and the posterior using the Jeffreys prior, identical to maximum likelihood, focusses on the strongest available instruments. We construct informative priors for the other regions that make their posteriors of the return on education similar to that of the US and the Southern region. These priors show the amount of prior information needed to obtain comparable results for all regions.     

A note on the estimation of seemingly unrelated regression systems

In a system of simultaneous equations, one obtains the same results for the overidentified equations by applying three-stage least squares to those equations alone as one would obtain by applying three-stage least squares to the entire system. This paper obtains an analogous result for a system of seemingly unrelated regressions. In doing so it generalizes earlier results of several other authors.     

有效刺激消费需求的自回归模型实证研究——基于广东省1981～2008年消费与收入数据

本文分析与居民消费有关的一些因素,认为能刺激消费需求的根本原因是可支配收入的增加,并用实证研究证实了居民当期消费在80%的程度上依赖于当期收入,这与传统的消费取决于能力的理论是吻合的.而有限分布滞后模型则验证了当期收入对后三期消费的抑制作用,这异于传统理论,本文对这一现象从消费意愿及消费品结构的角度作了探讨.     

Recent contributions to censored regression models

A brief survey of estimation of parameters in a censored regression model (known as the Tobit model) and some details of the properties of LAD (least absolute deviation) estimates and tests of significance of linear hypotheses are given.     

Top-down strategies based on adaptive fuzzy rule-based systems for daily time series forecasting

This paper presents a data-driven approach applied to the long term prediction of daily time series in the Neural Forecasting Competition. The proposal comprises the use of adaptive fuzzy rule-based systems in a top-down modeling framework. Therefore, daily samples are aggregated to build weekly time series, and consequently, model optimization is performed in a top-down framework, thus reducing the forecast horizon from 56 to 8 steps ahead. Two different disaggregation procedures are evaluated: the historical and daily top-down approaches. Data pre-processing and input selection are carried out prior to the model adjustment. The prediction results are validated using multiple time series, as well as rolling origin evaluations with model re-calibration, and the results are compared with those obtained using daily models, allowing us to analyze the effectiveness of the top-down approach for longer forecast horizons.     

华润万佳的"并购不适症"

一路高歌的华润万佳,近来似乎要走入全面整合的“休眠期”了。 徐刚走了。这位华润万佳的领军人物,这位在2002年11月第四届中国连锁经营协会年会上荣获“中国连锁业突出成就奖”的明日之星,在这个多雨的初春     

A bayesian approach to time-varying cross-sectional regression models

This paper presents a Bayesian approach to regression models with time-varying parameters, or state vector models. Unlike most previous research in this field the model allows for multiple observations for each time period. Bayesian estimators and their properties are developed for the general case where the regression parameters follow an ARMA(s,q) process over time. This methodology is applied to the estimation of time-varying price elasticity for a consumer product, using biweekly sales data for eleven domestic markets. The parameter estimates and forecasting performance of the model are compared with various alternative approaches.     

Algorithms to compute CM- and S-estimates for regression

Constrained M-estimators for regression were introduced by Mendes and Tyler in 1995 as an alternative class of robust regression estimators with high breakdown point and high asymptotic efficiency. To compute the CM-estimate, the global minimum of an objective function with an inequality constraint has to be localized. To find the S-estimate for the same problem, we instead restrict ourselves to the boundary of the feasible region. The algorithm presented for computing CM-estimates can easily be modified to compute S-estimates as well. Testing is carried out with a comparison to the algorithm SURREAL by Ruppert.     

连云港港口货物吞吐量自适应过滤预测模型及其Excel宏实现

利用自适应过滤预测法对连云港港口2008年前几个月货物吞吐量数据建立港口吞吐量预测模型,通过VBA编程实现Excel的宏按钮操作,按照预测误差方差最小原则交互实现迭代功能,选择对最小预测误差方差贡献最小的一期对应的权值作为最佳权值进行预测,获得了精确度较高的预测结果。     

Some aspects of bivariate regression subject to linear constraints

This paper considers a two-equation linear model when a subset of the parameters in one of the equations is subject to zero constraints. Inference procedures are presented both in the Bayesian and sampling theory framework. Specifically, posterior distributions and confidence distributions of the parameters, as well as various test procedures, are derived and illustrated with examples. The effect of the zero constraints on these procedures are discussed, and a comparison of the Bayesian with the sampling results is given.     

Algorithms for optimal design with application to multiple polynomial regression

The approximate theory of optimal linear regression design leads to specific convex extremum problems for numerical solution. A conceptual algorithm is stated, whose concrete versions lead us from steepest descent type algorithms to improved gradient methods, and finally to second order methods with excellent convergence behaviour. Applications are given to symmetric multiple polynomial models of degree three or less, where invariance structures are utilized. A final section is devoted to the construction of efficientexact designs of sizeN from the optimal approximate designs. For the multifactor cubic model and some of the most popular optimality criteria (D-, A-, andI-criteria) fairly efficient exact designs are obtained, even for small sample sizeN. AMS Subject Classification: 62K05.Abbreviated Title: Algorithms for Optimal Design.Invited paper presented at the International Conference on Mathematical Statistics,ProbaStat '94, Smolenice, Slovakia.