Emotion recognition and intellectual disability : development of the kinetic emotion recognition assessment and evaluation of the emotion specificity hypothesis : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Clinical Psychology at Massey University, Albany, New Zealand

Deficits in social adaptive functioning are a defining criterion of intellectual disability (ID) (American Psychiatric Association, 2013), and a key predictor of social inclusion and subsequent quality of life (Kozma, Mansell, & Beadle-Brown, 2009). Impairment in facial emotion recognition is often cited as the component skill responsible for the social difficulties observed. This position has been formally conceptualised by the emotion specificity hypothesis (ESH; Rojahn, Rabold, & Schneider, 1995), which proposes that individuals with ID manifest a specific deficit in facial emotion recognition beyond that which can be explained by difficulties in general intellectual functioning. Despite apparent widespread acceptance, there is not yet sufficient evidence to substantiate these claims. Moore (2001) proposes that emotion perception capacities may be intact in people with ID, and that reported deficits are instead, due to emotion recognition tasks making extensive cognitive demands that disadvantage those with lesser cognitive abilities. The aim of the present study was to clarify the nature of facial emotion recognition abilities in adults with mild ID. To this end, the Kinetic Emotion Recognition Assessment (KERA), a video-based measure of facial emotion recognition, was developed and a pilot study completed. The measure was designed to assess emotion recognition abilities, while attempting to reduce information-processing demands beyond those required to perceive the emotional content of stimuli. The new instrument was assessed for its psychometric properties in individuals with ID and neurotypical control participants. Initial findings supported the interrater reliability and overarching construct validity of the measure, offering strong evidence in favour of content, convergent and predictive validity. Item difficulty and discrimination analysis confirmed that the KERA included items of an appropriate level of difficulty to capture the range of emotion recognition capacities expected of individuals with mild ID. The secondary focus of the study was to assess how subtle methodological changes in the assessment of emotion recognition ability may affect emotion recognition performance, and in turn provide insight into how we might reinterpret existing ESH literature. To this end, the KERA was also applied in an investigation of the potential moderating effects of dynamic cues and emotion intensity, in addition to the assessment of the ESH. The results offer strong evidence that individuals with ID experience relative impairment in emotion recognition abilities when compared with typically developing controls. However, it remains to be seen whether the observed difficulties are specific to emotional expression or associated with more generalised facial processing. Preliminary findings also suggest that like their typically developing peers, individuals with ID benefit from higher intensity emotional displays; while in contrast, they observe no advantage from the addition of movement cues. Finally, the overarching motivation for the reassessment and improved measurement of the ESH, was in the interests of improving real-world outcomes associated with emotion recognition capacities. Accordingly, emotion recognition data were also interpreted in the context of three measures of social functioning to explore the link between social competence and emotion recognition ability. Results indicated that emotion recognition abilities are linked to outcomes in social adaptive functioning, particularly for females

Short-Term Forecasting of Czech Quarterly GDP Using Monthly Indicators

We evaluate the out-of-sample forecasting performance of six competing models at horizons of up to three quarters ahead in a pseudo-real time setup. All the models use information in monthly indicators released ahead of quarterly GDP. We estimate two models – averaged vector autoregressions and bridge equations – relying on just a few monthly indicators. The remaining four models condition the forecast on a large set of monthly series. These models comprise two standard principal components models, a dynamic factor model based on the Kalman smoother and a generalized dynamic factor model. We benchmark our results to the performance of a naïve model and the historical near-term forecasts of the Czech National Bank’s staff. The findings are also compared with a related study conducted by ECB staff (Barhoumi et al., 2008). In the Czech case, standard principal components is the most precise model overall up to three quarters ahead. However, the CNB staff’s historical forecasts were the most accurate one quarter ahead.Bridge models, dynamic factor models, GDP forecasting, principal components, real-time evaluation.

Predictive Ability of Business Cycle Indicators under Test: A Case Study for the Euro Area Industrial Production

In this paper we assess the information content of seven widely cited early indicators for the euro area with respect to forecasting area-wide industrial production. To this end, we use various tests that are designed to compare competing forecast models. In addition to the standard Diebold-Mariano test, we employ tests that account for specific problems typically encountered in forecast exercises. Specifically, we pay attention to nested model structures, we alleviate the problem of data snooping arising from multiple pairwise testing, and we analyze the structural stability in the relative forecast performance of one indicator compared to a benchmark model. Moreover, we consider loss functions that overweight forecast errors in booms and recessions to check whether a specific indicator that appears to be a good choice on average is also preferable in times of economic stress. We find that on average three indicators have superior forecast ability, namely the EuroCoin indicator, the OECD composite leading indicator, and the FAZ-Euro indicator published by the Frankfurter Allgemeine Zeitung. If one is interested in one-month forecasts only, the business climate indicator of the European Commission yields the smallest errors. However, the results are not completely invariant against the choice of the loss function. Moreover, rolling local tests reveal that the indicators are particularly useful in times of unusual changes in industrial production while the simple autoregressive benchmark is difficult to beat during time of average production growth

Emerging Markets, Financial Openness and Financial Development

We examine the effect of financial openness on the development of financial systems in a panel of 35 emerging markets during the period of 1976 to 2003. A group of indicators including variables from banking sector, stock market, and national capital accounts are used as measures of financial openness and financial development. In addition, aggregate index measures are developed to incorporate information from different areas of the financial system. Our empirical results generally suggest that financial openness is the key determinant of cross-country differences in the development of financial systems. When testing financial openness against the development of the banking sector and stock market separately, we found strong and robust evidence that this link between openness and development exists in stock markets. Although a similar link is sometimes found with banking sectors, it is not robust to different indicators of financial openness and model specifications.emerging markets, financial openness, financial development

Measuring Business Cycles: A Modern Perspective

In the first half of this century, special attention was given to two features of the business cycle: (1) the comovement of many individual economic series and (2) the different behavior of the economy during expansions and contractions. Both of these attributes were ignored in many subsequent business cycle models, which were often linear representations of a single macroeconomic aggregate. However, recent theoretical and empirical research has revived interest in each attribute separately. Notably, dynamic factor models have been used to obtain a single common factor from a set of macroeconomic variables, and nonlinear models have been used to describe the regime-switching nature of aggregate output. We survey these two strands of research and then provide some suggestive empirical analysis in an effort to unite the two literatures and to assess their usefulness in a statistical characterization of business- cycle dynamics.

THE ORGANIZATIONAL KNOWLEDGE DYNAMICS (OKD) MODEL. CASE STUDY VODAFONE ROMANIA

According to the new economic models, knowledge has to be incorporated in production functions as a key factor. Therefore, in the new knowledge based economy the main challenge is to develop, combine and integrate the knowledge of thousands of employees within an organizational framework. The main purpose of this paper is to present a new model of organizational knowledge dynamics developed by the authors by using the Analytic Hierarchy Process (AHP) methodology. The research approach is both theoretical and empirical. The developed model was tested within the Romanian business environment and the results prove the existence of high correlations between the results of the model and the actual strategies with regard to knowledge of the company, thus enhancing the efficiency of the model.Analytic Hierarchy Process (AHP), knowledge modelling, Organizational Knowledge Dynamics (OKD) Model.