A factor analytical method to interactive effects dynamic panel models with or without unit root

In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.

Comparison of static and dynamic engine models on the transient performance of a passenger vehicle powertrain

This paper presents experimental and computational results obtained on the Ford Barra 190 4.0 litres I6 gasoline engine and on the Ford Falcon car equipped with this engine. Measurements of steady engine performance, fuel consumption and exhaust emissions were first collected using an automated test facility for a wide range of cam and spark timings vs. throttle position and engine speed. Simulations were performed for a significant number of measured operating points at full and part load by using a coupled Gamma Technologies GT-POWER/GT-COOL engine model for gas exchange, combustion and heat transfer. The fluid model was made up of intake and exhaust systems, oil circuit, coolant circuit and radiator cooling air circuit. The thermal model was made up of finite element components for cylinder head, cylinder, piston, valves and ports and wall thermal masses for pipes. The model was validated versus measured steady state air and fuel flow rates, cylinder pressure parameters, indicated and brake mean effective pressures, and temperature of metal, oil and coolant in selected locations. Computational results agree well with experiments, demonstrating the ability of the approach to produce fairly accurate steady state maps of BMEP and BSFC, as well as to optimize engine operation changing geometry, throttle position, cam and spark timing. Measurements of the transient performance and fuel consumption of the full vehicle were then collected over the NEDC cycle. Simulations were performed by using a coupled Gamma Technologies GT-POWER/GT-COOL/GT-DRIVE model for instantaneous engine gas exchange, combustion and heat transfer and vehicle motion. The full vehicle model is made up of transmission, driveshaft, axles, and car components and the previous engine model. The model was validated with measured fuel flow rates through the engine, engine throttle position, and engine speed and oil and coolant temperatures in selected locations. Instantaneous engine states following a time dependent demand for torque and speed differ from those obtained by interpolating steady state maps of BSFC vs. BMEP and speed. Computational results agree well with experiments, demonstrating the utility of the approach in providing a more accurate prediction of the fuel consumption over test cycles.

Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber

Thermal stabilization process of polyacrylonitrile (PAN) is the slowest and the most energy-consuming step in carbon fiber production. As such, in industrial production of carbonfiber, this step is considered as amajor bottleneck in the whole process. Stabilization process parameters are usually many in number and highly constrained, leading to high uncertainty. The goal of this paper is to study and analyze the carbon fiber thermal stabilization process through presenting several effective dynamic models for the prediction of the process. The key point with using dynamic models is that using an evolutionary search technique, the heat of reaction can be optimized. The employed components of the study are Levenberg–Marquardt algorithm (LMA)-neural network (LMA-NN), Gauss–Newton (GN)-curve fitting, Taylor polynomial method, and a genetic algorithm. The results show that the procedure can effectively optimize a given PAN fiber heat of reaction based on determining the proper values of heating rampand temperature

Testing the factor structure of the behavioral-intentions battery: an empirical study of the Australian banking industry

The five-factor ‘Behavioural-Intentions Battery’ was developed by Zeithaml, Berry and Parasuraman (1996), to measure customer behavioural and attitudinal intentions. The structure of this model was re-examined by Bloomer, de Ruyter and Wetzels (1999) across different service industries. They concluded that service loyalty is a multi dimensional construct consisting of four, not five, distinct dimensions. To date, neither model has been tested within a banking environment. This research independently tested the ‘goodness of fit’ of both the four and five-factor models, to data collected from branch bank customers. Data were collected via questionnaire with a sample of 348 banking customers. A confirmatory factor analysis was conducted upon the two opposing factor structures, revealing that the five-factor structure has a superior model fit; however, the fit is ‘marginal’.

Latent variable models of need for uniqueness

The theory of uniqueness has been invoked to explain attitudinal and behavioral nonconformity with respect to peer-group, social-cultural, and statistical norms, as well as the development of a distinctive view of self via seeking novelty goods, adopting new products, acquiring scarce commodities, and amassing material possessions. Present research endeavors in psychology and consumer behavior are inhibited by uncertainty regarding the psychometric properties of the Need for Uniqueness Scale, the primary instrument for measuring individual differences in uniqueness motivation. In an important step toward facilitating research on uniqueness motivation, we used confirmatory factor analysis to evaluate three a priori latent variable models of responses to the Need for Uniqueness Scale. Among the a priori models, an oblique three-factor model best accounted for commonality among items. Exploratory factor analysis followed by estimation of unrestricted three- and four-factor models revealed that a model with a complex pattern of loadings on four modestly correlated factors may best explain the latent structure of the Need for Uniqueness Scale. Additional analyses evaluated the associations among the three a priori factors and an array of individual differences. Results of those analyses indicated the need to distinguish among facets of the uniqueness motive in behavioral research.

The effects of training on performance and performance-related states in individual elite athletes : a dynamic approach

There are difficulties undertaking controlled training studies with elite athletes. Thus, data from non-elite performers are often presented in scientific journals and subsequently used to guide general training principles. This information may not be transferable or specific enough to inform training practices in an individual elite athlete. However, the nature of athletic participation at elite levels provides the opportunity to collect training data, performance-related variables, and performance data of elite athletes over long periods. In this paper, we describe how dynamic linear models provide an opportunity to use these data to inform training. Data from an elite female triathlete collected over a 111-day training period were used to model the relationship between training and self-reported fatigue. The dynamic linear model analysis showed the independent effects of the three modes of triathlon training on fatigue, how these can change across time, and the possible influence of other unmeasured variables. This paper shows the potential for the use of dynamic linear models as an aid to planning training in elite athletes.

Single-subject research designs and data analyses for assessing elite athletes’ conditioning

Research in conditioning (all the processes of preparation for competition) has used group research designs, where multiple athletes are observed at one or more points in time. However, empirical reports of large inter-individual differences in response to conditioning regimens suggest that applied conditioning research would greatly benefit from single-subject research designs. Single-subject research designs allow us to find out the extent to which a specific conditioning regimen works for a specific athlete, as opposed to the average athlete, who is the focal point of group research designs. The aim of the following review is to outline the strategies and procedures of single-subject research as they pertain to the assessment of conditioning for individual athletes. The four main experimental designs in single-subject research are: the AB design, reversal (withdrawal) designs and their extensions, multiple baseline designs and alternating treatment designs. Visual and statistical analyses commonly used to analyse single-subject data, and advantages and limitations are discussed. Modelling of multivariate single-subject data using techniques such as dynamic factor analysis and structural equation modelling may identify individualised models of conditioning leading to better prediction of performance. Despite problems associated with data analyses in single-subject research (e.g. serial dependency), sports scientists should use single-subject research designs in applied conditioning research to understand how well an intervention (e.g. a training method) works and to predict performance for a particular athlete.

On the estimation and inference in factor-augmented panel regressions with correlated loadings

In an influential paper, Pesaran [Pesaran, M.H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica 74, 967–1012] proposes a very simple estimator of factor-augmented regressions that has since then become very popular. In this note we demonstrate how the presence of correlated factor loadings can render this estimator inconsistent.

A factor analytical approach to price discovery

Existing econometric approaches for studying price discovery presume that the number of markets are small, and their properties become suspect when this restriction is not met. They also require making identifying restrictions and are in many cases not suitable for statistical inference. The current paper takes these shortcomings as a starting point to develop a factor analytical approach that makes use of the cross-sectional variation of the data, yet is very user-friendly in that it does not involve any identifying restrictions or obstacles to inference.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).

Measuring chronic condition self-management in an Australian community: factor structure of the revised Partners in Health (PIH) scale

Purpose To evaluate the factor structure of the revised Partners in Health (PIH) scale for measuring chronic condition self-management in a representative sample from the Australian community.

Methods A series of consultations between clinical groups underpinned the revision of the PIH. The factors in the revised instrument were proposed to be: knowledge of illness and treatment, patient–health professional partnership, recognition and management of symptoms and coping with chronic illness. Participants (N = 904) reporting having a chronic illness completed the revised 12-item scale. Two a priori models, the 4-factor and bi-factor models were then evaluated using Bayesian confirmatory factor analysis (BCFA). Final model selection was established on model complexity, posterior predictive p values and deviance information criterion.

Results Both 4-factor and bi-factor BCFA models with small informative priors for cross-loadings provided an acceptable fit with the data. The 4-factor model was shown to provide a better and more parsimonious fit with the observed data in terms of substantive theory. McDonald’s omega coefficients indicated that the reliability of subscale raw scores was mostly in the acceptable range.

Conclusion The findings showed that the PIH scale is a relevant and structurally valid instrument for measuring chronic condition self-management in an Australian community. The PIH scale may help health professionals to introduce the concept of self-management to their patients and provide assessment of areas of self-management. A limitation is the narrow range of validated PIH measurement properties to date. Further research is needed to evaluate other important properties such as test–retest reliability, responsiveness over time and content validity.

Distributions of caregiving tasks among family members: the place of gender and availability.

To extend family-oriented approaches to caregiving, participants in 2 studies were asked to distribute tasks among a set of adult children, first with information only about gender and then with systematically varied information about commitments to paid work, marriage, and/or parenting. Making the distributions, using a computer-based program, were 2 groups of older adults (ages 60 to 90 years). In Study 1, gender composition was kept constant (2 sons and 2 daughters). In Study 2, it was varied. The results showed several ways in which people combine attention to gender and to availability. The results also pointed to the need to consider both the number and type of tasks allocated. The results are discussed in terms of implications for the way caregiving is regarded, the development of multiple-factor models for variations among family members, and the possible replications and extensions to other circumstances and populations.

Market makers v's the general public : a first look at S & P500 futures trade data

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for market trade and also two way trade between market makers (CT1) and the general public (CT4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. As we disaggregate from the market records to CT1 and CT4 records and further into year to year samples volume to futures volatility leading effects and also futures volatility to cash volatility leading effects dominate. The results raise important issues for risk management and dynamic hedging models employing intra-day trader data. A number of important issues for further analysis are also raised in this paper.

Inactivity and the dynamics of relationship development : a proposed model

Much of the relationship development literature assumes that business relationships evolve along a standard path that often ends in failure. However, this overly restrictive assumption ignores that firms can reactivate dormant relationships. To relax this assumption, we focus on this dormant stage and posit that it reflects either naturally occurring pauses or consecutive shifts – first divergent and then convergent – in partnering needs. Ultimately, we proffer an inactivity-inclusive model that augments current dynamic process models and may help firms to manage all their relationships, active and inactive.