Modern Strategies for Time Series Regression

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia.     

Three Factors to Signal Non‐Response Bias With Applications to Categorical Auxiliary Variables

Non‐response causes bias in survey estimates. The unknown bias can be reduced, for example as in this paper by the use of a calibration estimator built on powerful auxiliary information. Still, some bias will always remain. A bias reduction indicator is proposed and expressed as a product of three factors reflecting familiar statistical ideas. These factors provide a useful perspective on the components that constitute non‐response bias in estimates. To illustrate the indicator, we focus on the important case with information defined by one or more categorical auxiliary variables, each expressed by two or more properties or traits. Together, the auxiliary variables may represent a large number of traits, more or less important for bias reduction. An examination of the three factors of the bias reduction indicator brings the insight that the ultimate auxiliary vector for calibration need not or should not contain all available traits; some are unimportant or detrimental to bias reduction. The question becomes one of selection of traits, not of complete auxiliary variables. Empirical examples are given, and a stepwise procedure for selecting important traits is proposed.     

Integrating Quantitative and Qualitative Analysis Using Latent and Manifest Variables

The relative virtues of quantitative and qualitative research have been vigorously debated. Several researchers recommend combining methodologies but there is little evidence in the literature to suggest how different research methods might be integrated (Bryman, 1988). The current study addresses this deficiency in the research by examining the use of latent variables in quantitative and qualitative research as a means of blending the two approaches. A study of entrepreneurial Locus of Control where quantitative and qualitative data were available illustrates the methodological issues. Analysis of quantitative data was conducted using LISREL (7.20) and qualitative data were categorised using NUD.IST (Non-numerical Unstructured Data Indexing Searching and Theorising computer software). Detailed comparisons are made between the methods described in this paper and other approaches to content analysis.     

Some general results for moments in bivariate distributions

The purpose of this paper is to present a closed formula to compute the moments of a general function from the knowledge of its bivariate survival function. The result is derived by utilizing an integration by parts formula for two variables, which is not readily available in the literature. Many of the existing results are obtained as special cases. Finally, two examples are presented to illustrate the results. In both the examples, mixed moments as well as moments for the series system and parallel system are obtained. The integration by parts formula in two variables, derived here, is of interest in its own right and we hope that it will be useful in other investigations. The integration by parts formula in two variables is derived as a special case of a general formula in n variables.     

Estimators of long-memory: Fourier versus wavelets

Semi-parametric estimation methods of the long-memory exponent of a time series have been studied in several papers, some applied, others theoretical, some using Fourier methods, others using a wavelet-based technique. In this paper, we compare the Fourier and wavelet approaches to the local regression method and to the local Whittle method. We provide an overview of these methods, describe what has been done and indicate the available results and the conditions under which they hold. We discuss their relative strengths and weaknesses both from a practical and a theoretical perspective. We also include a simulation-based comparison. The software written to support this work is available on demand and we illustrate its use at the end of the paper.     

Modeling Clustered Ordered Categorical Data: A Survey

This article surveys various strategies for modeling ordered categorical (ordinal) response variables when the data have some type of clustering, extending a similar survey for binary data by Pendergast, Gange, Newton, Lindstrom, Palta & Fisher (1996). An important special case is when repeated measurement occurs at various occasions for each subject, such as in longitudinal studies. A much greater variety of models and fitting methods are available than when a similar survey for repeated ordinal response data was prepared a decade ago (Agresti, 1989). The primary emphasis of the review is on two classes of models, marginal models for which effects are averaged over all clusters at particular levels of predictors, and cluster-specific models for which effects apply at the cluster level. We present the two types of models in the ordinal context, review the literature for each, and discuss connections between them. Then, we summarize some alternative modeling approaches and ways of estimating parameters, including a Bayesian approach. We also discuss applications and areas likely to be popular for future research, such as ways of handling missing data and ways of modeling agreement and evaluating the accuracy of diagnostic tests. Finally, we review the current availability of software for using the methods discussed in this article.     

自回归模型在实践中的问题分析

自回归模型是一种被广泛使用的计量经济模型，但在实践中通常会引起多重共线性，从而导致外生解释变量的系数发生较大变化，一些变量不再显著，模型失去经济意义。本文首先分析这些现象是如何发生的，然后通过一个典型的例子给予说明，最后提出了改进模型的建议。     

Regression with imputed covariates: A generalized missing-indicator approach

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may then be tackled by either model reduction procedures or model averaging methods. We illustrate our approach by considering the problem of estimating the relation between income and the body mass index (BMI) using survey data affected by item non-response, where the missing values on the main covariates are filled in by imputations.     

Restrictions on variables

Accounting identities impose exact restrictions on the endogenous variables of econometric models. Such restrictions are usually met by choosing a closing entry or by building an allocation model. Selecting a closing entry may be difficult or arbitrary, while allocation models admit little flexibility in the choice of explanatory variables and lagged adjustment schemes. This paper studies a third solution which in a sense lies inbetween: freely chosen equations for all variables are adjusted additively such that the restrictions will hold. The adjustment involves some new parameters which can be estimated simultaneously with the original parameters using Maximum Likelihood techniques.An application is provided for a financial model of the Dutch private sector. Our approach here proves superior to any choice of closing entry in the system.     

Analytic derivation of sociometric dimensions

Dimensional analysis has been defined variously as the synthesis of dimensionless variables and as a way of spatially representing data. While in sociometrics, one may not be able to devise models resembling those of the physical sciences because of the complexity of many social situations, one may in some cases successfully apply the techniques of classic dimensional analysis and get empirically valid results. More often we derive with analytic methods from the social sciences different kinds of dimensional frameworks in which to represent data. This paper will compare these approaches to the study of relationships and give examples of where each might be appropriate. Finally, a classification of the analytical methods will be presented.     

SPLIT–PLOT DESIGN: MODEL AND ANALYSIS FOR COUNT DATA

The analysis of count response data from designed experiments is well–known for independent response variables having the Poisson distribution. For experimental designs where responses are dependent, no general results seem to be available. An example of this type of design is the split–plot design, where sub–plot responses are essentially dependent within whole plots.
In this paper, a model will be proposed for split–plot count data and a separate analysis for whole plot and sub–plot data will be presented. It is interesting to note, that the same model is used in the quite different context of consumer buying behaviour. It is derived by Goodhardt, Ehrenberg and Chatfield and it was called the 'Dirichlet model', as the Dirichlet multinomial distribution, together with the negative binomial distribution, build up the model.     

Limited-dependent rational expectations models with future expectations

This paper examines limited-dependent rational expectations (LD-RE) models containing future expectations of the dependent variable. Limited dependence is of a two-limit tobit variety which may, for example, arise as a result of a policy of imposing limits on the movement of the dependent variable by means of marginal as well as intramarginal interventions. We show that when the forcing variables are serially independent the model has an analytical solution which can be computed by backward recursion. With serially correlated forcing variables, we discuss an approximate solution method, as well as a numerically exact method that, in principle, can be implemented by stochastic simulation, although in practice it is limited by available computational capacity. The paper discusses some properties of the approximate solutions and reports the results of a limited number of Monte Carlo experiments in order to illustrate the computational feasibility of using the exact solution when the fundamentals are serially independent and the approximate solution when they are serially correlated.     

Sparse Partial Least Squares in Time Series for Macroeconomic Forecasting

Factor models have been applied extensively for forecasting when high‐dimensional datasets are available. In this case, the number of variables can be very large. For instance, usual dynamic factor models in central banks handle over 100 variables. However, there is a growing body of literature indicating that more variables do not necessarily lead to estimated factors with lower uncertainty or better forecasting results. This paper investigates the usefulness of partial least squares techniques that take into account the variable to be forecast when reducing the dimension of the problem from a large number of variables to a smaller number of factors. We propose different approaches of dynamic sparse partial least squares as a means of improving forecast efficiency by simultaneously taking into account the variable forecast while forming an informative subset of predictors, instead of using all the available ones to extract the factors. We use the well‐known Stock and Watson database to check the forecasting performance of our approach. The proposed dynamic sparse models show good performance in improving efficiency compared to widely used factor methods in macroeconomic forecasting. Copyright © 2014 John Wiley & Sons, Ltd.     

The development of delinquency during adolescence: a comparison of missing data techniques

Conclusions on the development of delinquent behaviour during the life-course can only be made with longitudinal data, which is regularly gained by repeated interviews of the same respondents. Missing data are a problem for the analysis of delinquent behaviour during the life-course shown with data from an adolescents’ four-wave panel. In this article two alternative techniques to cope with missing data are used: full information maximum likelihood estimation and multiple imputation. Both methods allow one to consider all available data (including adolescents with missing information on some variables) in order to estimate the development of delinquency. We demonstrate that self-reported delinquency is systematically underestimated with listwise deletion (LD) of missing data. Further, LD results in false conclusions on gender and school specific differences of the age–crime relationship. In the final discussion some hints are given for further methods to deal with bias in panel data affected by the missing process.     

Estimating predictions,prediction errors and their standard deviations using constructed variables

Increasing use has been made of predictive tests for assessing model adequacy, but it is sometimes difficult to generate predictions and their standard errors in dynamic or simultaneous equation models. Following earlier suggestions by Salkever and Fuller, this paper shows how the requisite information may be obtained by the use of specially constructed variables in a regression framework. The main use of the method will be in those situations where prediction information is not available as a standard option in econometric packages.     

Identification with latent variables

This is an essay on a unified approach to the identifiability problem in static models with and without hidden endogenous variables. As is well known, when some of these variables are unobserved, the prior information requirements for models when all endogenous variables are observed, are still there. In addition, extra prior information that takes the place of the means and covariances of the missing variables will have to be supplied directly or indirectly by the statistical researcher. In the paper we characterize the quality and quantity of the required information for the general linear static model and apply it when the model is i) an econometric demand and supply model with missing observations on the quantity transacted, ii) a factor analysis model with observed characteristics of the test takers and iii) a LISREL Model without fixed exogenous variables. With unknown true parameters, the exact rank conditions are seldom verifiable but we do recommend an implementable check-list that is adequate for almost all parameters.     

基于Fuzzing的软件漏洞发掘技术

随着社会信息化的不断发展,软件的安全问题也越来越突出了。目前,绝大多数的软件都或多或少的存在着一些安全漏洞,而这些漏洞一旦被那些不怀好意的黑客利用,将会给软件公司和使用这些软件的人造成巨大的损失。所以,对软件的漏洞发掘工作就显得十分有必要,本文在总结Fuzzing技术的一些缺陷后,在测试数据生成、异常定位和自动分析方面对其进行了改进,设计了基于Fuzzing的漏洞发掘框架,并对一些核心模块加以实现,达到了预期目的。     

Statistics in Business and Industry: Implementation

There are many statistical tools available to be utilized for general business improvement and industrial problem solving. However, there is a wide gap between the tools available and those that are actually used in business and industrial organizations. Thus it is important for statisticians to direct serious attention to bridging this gap if statistics is to be relevant in business and industry. In this paper we look at some ideas for implementing statistical methods in business and industry.