Modelling of repairable systems with various degrees of repair

The availability of a stochastic repairable system depends on the failure behaviour and on repair strategies. In this paper, we deal with a general repair model for a system using auxiliary counting processes and corresponding intensities which include various degrees of repair (between minimal repair and perfect repair). For determining the model parameters we need estimators depending on failure times and repair times: maximum likelihood (ML) estimator and Bayes estimators are considered. Special results are obtained by the use of Weibull-type intensities and random observation times.     

Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation

Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot be implemented uncritically for spatial data. Generally, most of the studies in spatial data analysis can be divided into two branches: the model-driven approach and the data-driven approach. The main aim of this paper is the comparison of both approaches. To carry out such a task, crime rate data in Columbus (Ohio), coming from a well-known database, have been used. The main aim of this paper is to illustrate how spatial effects can be viewed as spatial econometric models, which assess the limitations of standard techniques in a spatial context, suggesting alternative methods to deal with this problem. An application to the crime rate in Columbus (Ohio) has been carried out.

The effect of inflation uncertainty on inflation: Stochastic volatility in mean model within a dynamic framework

This paper investigates the effect of inflation uncertainty innovations on inflation over time by considering the monthly United States data for the time period 1976–2006. In order to investigate the effect of inflation uncertainty innovation on inflation, a Stochastic Volatility in Mean model (SVM) has been employed. SVM models are generally used to capture the innovation to inflation uncertainty, which cannot be achieved in the framework of popular deterministic ARCH type of models. Empirical evidence provided here suggests that innovations in inflation volatility increases inflation persistently. This evidence is robust across various definitions of inflation and different sub-periods.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Combining forecasts: An application to elections

We summarize the literature on the effectiveness of combining forecasts by assessing the conditions under which combining is most valuable. Using data on the six US presidential elections from 1992 to 2012, we report the reductions in error obtained by averaging forecasts within and across four election forecasting methods: poll projections, expert judgment, quantitative models, and the Iowa Electronic Markets. Across the six elections, the resulting combined forecasts were more accurate than any individual component method, on average. The gains in accuracy from combining increased with the numbers of forecasts used, especially when these forecasts were based on different methods and different data, and in situations involving high levels of uncertainty. Such combining yielded error reductions of between 16% and 59%, compared to the average errors of the individual forecasts. This improvement is substantially greater than the 12% reduction in error that had been reported previously for combining forecasts.     

A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research

Mediation as a theory testing approach has witnessed considerable adoption among Operations Management (OM) researchers. Although mediation-testing methods have evolved tremendously in the past decade, their dissemination in the OM field has not seen parallel growth. These advanced techniques facilitate the testing of existing and complex hypotheses in a more precise manner. With the intent of critically evaluating existing and alternative methods for conducting mediation analysis needed to support sophisticated empirical research, this paper first reviews OM studies that tested for mediation in the past eleven years (2002–2012) from top-tier OM journals. Four commonly used mediation approaches were identified. Based on principles of good theory building, type of mediation model, and properties of empirical data, we evaluate the existing methodologies and make recommendations on how to improve the rigor of OM mediation testing. Using published OM studies in top journals as examples, we then illustrate the relevance and advantages of these recommendations, as well as their ease of use. Furthermore, we empirically show that more robust and insightful results can be achieved by adopting these techniques, which in turn have the promise of leading to better theory building and testing in the field of operations management.     

Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals,financial stress and economic uncertainty

We develop models for examining possible predictors of growth of China's foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US–China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.     

中国妇女人工流产影响因素的多水平分析

使用多水平模型对1992—1997年中国妇女人工流产行为进行了分析,目的是考察和检验宏观（省级水平）和微观（个人水平）因素对中国妇女分孕次人工流产概率的独立影响及其显著性。多水平模型在模拟具有层次结构性的数据上要优于传统的回归模型,在社会科学领域正在被越来越广泛地应用。多水平模型结果显示:无论是宏观因素还是微观因素,都对妇女人工流产有独立而显著的影响,微观影响在所有孕次都是显著的,而宏观影响则取决于孕次。同时宏观因素对微观上的人工流产社会经济差异也具有显著影响,并与孕次有很大关系。本文的多水平模型有助于全面认识我国妇女人工流产的影响因素。     

An Empirical Analysis of the Extreme Cherry Picking Behavior of Consumers in the Frequently Purchased Goods Market

Extreme cherry pickers are customers who seek price deals and excessively avail themselves of deep discount offers, which generates negative profits for retailers. This study uses market transaction and primary consumer survey data to provide insights into the determinants, prevalence, and profit impacts of such behavior in the frequently purchased goods market. We find that the extreme cherry picking segment is small (about 2% of all shoppers), but its relative value varies across stores, and consumers manifest this behavior only in secondary stores. An inverse U-shaped relationship marks consumers’ opportunity costs for cross-store price search and likelihood of extreme cherry picking behavior. Finally, we also find that a loss leader promotional strategy adds to retailers’ bottom lines, despite the pure loss generated by extreme cherry pickers.     

居民消费价格指数ARCH族模型检验

居民消费价格指数(CPI)在一定程度上反映了通货膨胀或紧缩程度,往往成为各种研究关注的重点。应用GARCH类模型对月度CPI的波动性进行了建模.结果表明该模型能有效拟合月度CPI的波动性特征;并根据分析得出我国市场经济还不成熟。