Writers on the business cycle often emphasize that non-linear models are needed to account for certain of its features. Thus it is often said that either the asymmetry of the duration of business cycle expansions and contractions or the variability of these quantities demand a non-linear model. Such comments are rarely made precise however and mostly consist of references to such assertions from the past. Thus the asymmetry in the cycle is mostly accompanied by references to Keynes (1936) and Burns and Mitchell (1946). But these authors were looking at what we call today the classical cycle i.e. movements in the level of GDP, and so the fact that there are long expansions and short contractions can arise simply due to the presence of long-run growth in the economy, and it is not obvious that it has much to do with non-linearity. This thesis aims to introduce various statistics that can be used to characterise the specific shape of the non-linearity observed in macroeconomic time series. Chapter 2 introduces a range of statistics and presents the dating algorithm used in this thesis, which is based on the BBQ algorithm of Harding and Pagan (2002). Chapter 3 tests the adequacy of linear models versus the SETAR model of van Dijk and Franses(2003) and the bounceback model of Kim, Morley and Piger (2005) in capturing observed non-linear features of the data. Chapter 4 extends this work by examining the three state Markov model of Hamilton (1989), again using the ??bounce-back?? model of Kim C., Morley, J. and J. Piger, (2005), and the more complicated ??tension?? model of DeJong, D., Dharmarajan, H., Liesenfeld, R. and Richard, J., (2005). Chapter 4 also extends Chapter 3 by estimating the above mentioned models on US GDP, Australian non-farm GDP, US investment and Australian dwellings investment. They are then simulated in order to gauge the cycle properties. Chapter 5 analyses the business cycle implications of two related multivariate dynamic factor models presented in papers by Kim and Piger (2001, 2002). Finally Chapter 6 concludes.

Writers on the business cycle often emphasize that non-linear models are needed to account for certain of its features. Thus it is often said that either the asymmetry of the duration of business cycle expansions and contractions or the variability of these quantities demand a non-linear model. Such comments are rarely made precise however and mostly consist of references to such assertions from the past. Thus the asymmetry in the cycle is mostly accompanied by references to Keynes (1936) and Burns and Mitchell (1946). But these authors were looking at what we call today the classical cycle i.e. movements in the level of GDP, and so the fact that there are long expansions and short contractions can arise simply due to the presence of long-run growth in the economy, and it is not obvious that it has much to do with non-linearity. This thesis aims to introduce various statistics that can be used to characterise the specific shape of the non-linearity observed in macroeconomic time series. Chapter 2 introduces a range of statistics and presents the dating algorithm used in this thesis, which is based on the BBQ algorithm of Harding and Pagan (2002). Chapter 3 tests the adequacy of linear models versus the SETAR model of van Dijk and Franses(2003) and the bounceback model of Kim, Morley and Piger (2005) in capturing observed non-linear features of the data. Chapter 4 extends this work by examining the three state Markov model of Hamilton (1989), again using the ??bounce-back?? model of Kim C., Morley, J. and J. Piger, (2005), and the more complicated ??tension?? model of DeJong, D., Dharmarajan, H., Liesenfeld, R. and Richard, J., (2005). Chapter 4 also extends Chapter 3 by estimating the above mentioned models on US GDP, Australian non-farm GDP, US investment and Australian dwellings investment. They are then simulated in order to gauge the cycle properties. Chapter 5 analyses the business cycle implications of two related multivariate dynamic factor models presented in papers by Kim and Piger (2001, 2002). Finally Chapter 6 concludes.

Responding to Joint Attention (RJA) involves an infant's ability to follow a gaze or point by a partner. Prenatal cocaine exposure (PCE), which places a child in danger of numerous risks, has been accepted as having subtle effects on developmental outcomes such as social competence and associated socio-emotional outcomes. The current study looked at a sample of 166 children prenatally exposed to cocaine who were attending an early intervention program. The study established group and individual trajectories of responding to joint attention from 12, 15, and 18 months of age. Hierarchical modeling identified two groups, a delay group and an average group, while individual trajectories identified a linear pattern of growth of RJA. Both individual and group trajectories indicated that children with higher RJA from 12 to 18 months demonstrated better social competence at three years of age and first grade. The delay and average group showed significant differences on later social competence measures, but not problem behaviors, such that RJA, a positive behavior, may connect more closely with later positive behaviors than with behavior problems. RJA may therefore be useful in a preventative intervention targeted at enhancing positive social behaviors and as an important and simple screening tool for possible delay early in a child's life, helping to deliver early intervention services in a targeted and effective manner.

Agricultural productivity, ecosystem health, and wetland restoration rely on soil organic carbon (SOC) as vital for microbial activity and plant health. This study assessed: (1) accuracy of topographic-based non-linear models for predicting SOC; and (2) the effect of analytic strategies and soil condition on performance of spectral-based models for predicting SOC. SOC data came from 28 agriculturally converted Delmarva Bays sampled down to 1 meter. R2 was used as an indicator of model performance. For topographic-based modeling, correlation coefficients and condition indices reduced 50 terrain-related values to three datasets of 16, 11, and 7 variables. Five types of non-linear models were examined: Generalized Linear Mondel (GLM) ridge, GLM LASSO, Generalized Additive Model (GAM) non-penalized, GAM cubic splice, and partial least-squares regression. Carbon stocks varied widely, 50 to 219 Mg/ha, with the average around 93 Mg/ha. Topography shared a weak relationship to SOC with most attributes showing a correlation coefficient less than 0.3. GLM ridge and both GAMs achieved moderate accuracy at least once, usually using the 16 or 11 variable datasets. GAMs consistently performed the best. Prior to carbon analysis, hyperspectral signatures were recorded for the topmost soil horizons under different conditions: moist unground, dry unground, and dry ground. Twenty-four math treatment and smoothing technique combinations were run on each hyperspectral dataset. R2 varied greatly within datasets depending on analytic strategy, but all datasets returned an R2 greater than 0.9 at least twice. Moist unground soil models outperformed the others when comparing the best models among datasets. / Master of Science

Elements from differential association and importation theory were incorporated into a contextual model to explain drug abuse inside prison. Data came from self-administered questionnaires given to more than 1,000 inmates in 30 different correctional institutions throughout Kentucky, Tennessee, and Ohio. Hierarchical linear modeling was used to examine the impact of correctional context on individual behavior. Results indicated that drug abuse inside prison varies across different correctional institutions. The effect of prior street-drug use on drug abuse in prison also varied across contexts. Moreover, an aggregate measure of crowding explained both drug abuse in prison as well as the effect of prior street-drug use on substance abuse in prison.

Malaysia’s preferential policies have reduced the educational attainment gap between ethnic groups. However, we know less about their effects on ethnic differences in academic achievement. With this point in mind, the overall goal of this study is to examine inter-ethnic differences in mathematics and science achievement based on the cohort of eighth grade (Form 2) Malaysian students who participated in the Third International Mathematics and Sciences Study Repeat Project (TIMMS-R). It sought to determine the extent to which theoretical propositions of the structural and cultural perspectives developed to explain achievement differences in the United States are applicable in Malaysia. Malaysia is an interesting setting for the purpose of the present study for three reasons. First, the interethnic differences in educational outcomes were historically linked to occupational structure and class-and ethnicity-based residential segregation during the Brisish colonial rule. Second, Malaysia is one of the few countries (i.e. Fiji, Nigeria, Sri Lanka, Uganda, India, and New Zealand) that have strong public policies to rectify the historical ethnic inequalities in access to education. However, the difference between Malaysia and these countries seems to be in the relative status of the formerly disadvantaged ethnic group in question. Finally, as a new member of the New Industrialized Countries (NICs), Malaysia is in the process of making the transition from an agricultural economy to an indutrialized nation. As such, the importance of mathematics and science education increases along with socioeconomic and technological advance and the discrepancies in mathematics and science achievement can have important implications on socioeconomic disparity among ethnic groups. The primary contribution of this dissertation is that it holistically examines how individual, family and school characteristics affect mathematics and science achievement of the eighth graders in Malaysia. The multilevel modeling analyses showed that Non-Malay students performed significantly better in mathematics achievement than Malay students, even after controlling for family and school characteristics as well as students’ perceived importance of mathematics and educational expectations. Overall, the results suggest that the structural and cultural perspectives work differently for Malay and Non-Malay students.

Les dépenses militaires constituent un phénomène économique important puisque 1630 milliards de dollars sont affectés au secteur de la défense en 2010 au niveau mondial. Les économistes se sont alors intéressés aux conséquences économiques de telles dépenses. Pourtant depuis près de 40 ans, force est de constater qu'aucun consensus n'émerge de cette littérature. Une telle absence se traduit par des difficultés pour formuler des conclusions de politique économique adaptée. L'objet de cette thèse est alors de déterminer quelles pourraient être les causes de la diversité des résultats de la littérature passée. Nous proposons alors deux pistes expliquant ce phénomène: d'une part une diversité de modélisations ayant des hypothèses diamétralement opposées quant à l'impact de la défense sur la croissance et d'autre part l'existence de non-linéarité, de nature à modifier cet impact pour un même modèle.La thèse se compose alors de quatre chapitres. Dans le chapitre un, nous proposons une revue de littérature permettant de mettre en exergue les régularités, en termes de résultats, associées à chaque modèle théorique. Le chapitre deux constitue alors une vérification empirique, pour un échantillon donné de pays de l'OCDE, de la contingence des conclusions quant à l'utilisation de différentes modélisations. Notre stratégie empirique permet de considérer les modèles comme complémentaires plutôt qu'en concurrence. Dans le troisième chapitre, nous étudions les phénomènes de non-linéarité au travers d'une démarche et d'une méthode empirique originales. Nous montrons que la régression non-linéaire est préférable à la régression linéaire et qu'elle conduit à caractériser un effet asymétrique de la défense sur la croissance. Le dernier chapitre a pour but d'étudier plus précisément le cas de la France afin de confirmer, en termes de politique économique, la stratégie développée dans les chapitres précédents. / Military spending constitutes an important economic phenomenon because in 2010, 1630b dollar are affected to the defense sector all around the world. Economists are interested in the economic consequences of such spending. However, since 40 years, one has to note that there is no consensus from this literature. This absence leads to difficulties to formulate relevant economic policy conclusions. The aim of this thesis is to determine what are the causes of the diversity of results in the past literature. Two ways are then considered: on the one hand, a diversity of models with hypothesis diametrically opposed concerning the impact of the defense sector on economic growth and on the other hand, non-linear behavior which may modify this impact for a same model.This dissertation contains four chapters. In the first one, we detail a literature review in order to draw the principal regularities in terms of results for each theoretical model. The chapter two constitutes an empirical verification of the contingency of results from different models, for a sample of OECD countries. Our empirical strategy leads to consider the complementary rather than the competition between models. In the third chapter, we examine the non-linear phenomenon with original steps and empirical method. We show that non-linear regression outperforms linear regression and characterizes asymmetric effects of the military spending on growth. The last chapter aims to analyze the case of France in order to confirm the usefulness of the strategy developed in past chapters for raising economic policy recommendations.

Current deficits in the rehabilitation psychology literature involving longitudinal studies investigating positive outcomes following acquired disabilities have deserved research attention. In the current study, data on happiness as an enduring mood tone, as measured by the Life Satisfaction Index (LSI) was collected from 1271 individuals (“insiders”) having incurred either a traumatic brain injury (TBI), spinal cord injury (SCI), severe burn, or intra-articular fracture (IAF) or from someone who felt close enough to speak on their behalf (“outsiders”). Data on happiness, functional independence as measured by the Functional Independence Measure (FIM), and other variables of interest were collected at 12 months, 24 months, 48 months, and 60 months after being medically discharged. Hierarchical Linear Modeling (HLM) analyses showed that trajectories of happiness remained stable across participants and did not change significantly over five years post-discharge regardless of injury type, FIM, or insider/outsider status. Happiness was significantly predicted by FIM, injury type, and whether the respondent was an insider or outsider. Those who were more impaired and less functionally independent were less happy. Those with a TBI were consistently less happy than those with an IAF or SCI and outsiders reported greater happiness on behalf of the insider than did the insiders themselves. This study shows that there is stability in happiness levels that can be sustained at least five years post-discharge and that there are discrepancies between insider and outsider reports of subjective happiness. Proxy reports can be used as valuable and valid secondary sources of information but should not be used as substitutes for first hand reports unless absolutely necessary.

The aim of this study is to compare the performance of a linear model to a nonlinear model focusing on flow separation based on a critical slope value. Specifically, the WindPRO WAsP model will be compared with the WindSIM CFD model over a simulated terrain to determine the point the two models differ in relation to the inclination of the terrain. The results of this study will verify if the proposed critical slope value of roughly 17 degrees is truly representative of the limitation of the WAsP model in producing accurate results as compared to a CFD model.  Multiple similar studies have been performed using existing sites with actual met mast data as a comparison to the model outputs. Many of these cases have come up with varying results due primarily to the large number of uncontrolled factors influencing the data. This study will be designed in a fully simulated environment where all variables can be controlled, allowing for the manipulation of a single variable to understand its’ specific influence over the model. The primary variable being tested in this study will be the slope of the terrain with all other factors held constant.   Based on the outcome of 7 alternative runs with ridge heights of 100, 120, 140, 160, 180, 200, and 300 meters and respective maximum slope values of 10.31, 12.32, 14.29, 16.23, 18.14, 20, and 28.63 degrees a defined separation point at a hub height of 94 meters could not be found. Each run demonstrated correlation between wind speeds and terrain slope variations but a considerable difference in estimated wind resources was present between the linear and non-linear CFD models where any slope in terrain is present. This, as expected, increases where terrain inclination increases, but a clearly defined difference between the two models is not evident at the previously established critical slope value of approximately 17 degrees (30%).

Internalizing problems are prevalent in childhood and adolescence in both community and out-of-home populations. Internalizing symptoms are frequently associated with problems in other areas of functioning as well. For children in out-of-home care, who face additional adversities such as maltreatment and witnessing traumatic events, internalizing problems have shown increased prevalence while less frequently addressed in research. The current studies used longitudinal data collected across 7 years from a sample of 1,765 children, 5 to 14 years old, in out-of-home care in Maryland, USA. Data consisted mainly of Child and Adolescent Needs and Strengths (CANS) assessments, as well as demographic information (age, sex, and race/ethnicity) and out-of-home placement type. In Study 1 we examined the trajectories of anxiety and depression across age and time in care separately and evaluated a comprehensive model of resilience for each outcome using hierarchical linear modeling. This exploratory model included both indicators of internal resilience (i.e. cognitive, emotional, spiritual, physical, behavioural) and environmental risk and resilience factors (i.e. family, acculturation, community, placement, school functioning, social functioning) related to internalizing problems in children and adolescents. Results showed anxiety was fairly stable over time in care and age, with few significant predictors aside from already well-known risk factors. Depression results showed a slight increase across age and decrease across time in care with several more significant predictors than the anxiety model. While both models showed overlap in predictors, they also included predictors unique to each outcome. In Study 2 we examined the reciprocal relationships across time between anxiety, depression, and significant risk and protective factors from Study 1. Using time lagged hierarchical linear models we found few significant relationships related to anxiety, and largely unidirectional relationships between depression and relevant factors over time. Two factors, traumatic stress and placement in residential treatment care, displayed reciprocal relationships with depression over time. However, our results largely did not support the direct resilience feedback mechanisms proposed between variables for either outcome, but revealed other possible mechanisms at work (i.e. dual cascades developmental model) to explain maladaptation towards depression in particular, but also anxiety. Findings are discussed in terms of theoretical implications, future research directions, and applied implications for prevention/intervention programs for internalizing problems for children in out-of-home care.