Det debatteras inom psykoterapiforskningen om hur förändring går till. Accep-tans beskrivs av två relationella teoretiker, Safran och Muran (2000), som hjärtat i psykoterapi. Acceptans tycks inte ha studerats som common factor inom psykoterapiforskningen. Frågeställningarna är: Hur kan förändringsproce-ssen beskrivas i psykodynamisk psykoterapi med hjälp av begreppet acceptans? Har patienterna nått en ökad acceptans, vilken slags acceptans ar de uppnått och hur hänger det samman med förändring? Framträder acceptans i intervjuer-na på ett sådant sätt så att man kan se det som en common factor av betydelse?  Arbetet har en kvalitativ ansats där intervjuer med sex vuxna som genomgått långtids psykoanalys eller psykodynamisk terapi analyserades med tematisk analys. Resultat: Fem teman i framträdde i analysen: a) Acceptans av flera aspekter av själv, b) Acceptans av egen begränsning, c) Acceptans av ansvar och agens, d) Acceptans av olikhet, e) Terapeutens acceptans av patienten. Resultatet visar på att all förändring inte är och inte kan beskrivas i termer av acceptans men det tyder på att acceptans kan ses som en viktig underliggande mekanism som bidrar till förändring. Det pekar också i riktning mot att studiet av acceptans som utfallsmått kan bidra till att studera strukturell förändring efter psykoterapi men ytterligare forskning skulle behövas för att undersöka detta vidare. / There is a debate in psychotherapy research on how change takes place. Accep-tance is described in relational theory by Safran and Muran (2000) as the heart of change in psychotherapy. It seems acceptance has not been studied as a common factor in research of psychotherapy. The question at issue is: How can the process of change in psychodynamic psychotherapy be described using the concept of acceptance? Have the patient reached an increased level of accep-tance, what kind of acceptance is it and how is it linked to change? Does accep-tance appear in such a way in the interviews that one can see it as a common factor of importance? This study has a qualitative approach, where interviews with six adults have been analyzed using a thematic approach. Five main themes emerged: a) Acceptance of more aspects of self, b) Acceptance of personal limits, c) Acceptance of responsibility and agency, d) Acceptance of difference, e) The therapist's acceptance of the patient. The result shows that not all change is or can be described in terms of acceptance but it indicates that acceptance can be seen as an important underlying mechanism that contributes to change. It also indicates that the study of acceptance as measure of outcome could contribute to studying structural change after psychotherapy but further research would be needed.

In this thesis, we use the dynamic factor model in state space, proposed by Stock and Watson (1989), to estimate the fluctuations of common factor by using lots of macroeconomic variables. Besides, with the combination of two stage dynamic factor analysis model which is proposed by Aruba et. al (2010), we want to discuss the possibility for the correlation of economic fluctuations across countries to change with different time periods. The thesis verifies the following three conclusions: First, the correlations of the economic fluctuations across countries are significant due to the regional economics. Second, the global or regional common shocks will increase the correlations of the economic fluctuations across countries. Finally, developed countries and emerging countries response differently during the Financial Tsunami from 2008 to 2009.

The objective of this study is to discover the sources of securities return in forecasting stock return from different sides of potential factors including fundamental and market information. We test currency sensitivity, earnings variability, earnings yield, growth, leverage, trading activity, momentum, size, value, volatility, capital spending discipline, free cash flow, efficiency, solvency, earnings quality, corporate finance policy and technical 17 factors basing on different factor dimensions in this study. We construct a Taiwan multi-factor model by using the most significant factors for universal stocks according to 0HMSCI Barra¡¦s Multiple-Factor Modeling process, and then apply market neutral investment to build portfolios for performance back-testing. As a result, the most significant top five factors in forecasting are respectively ¡§Volatility2,¡¨ ¡§Earnings Quality1,¡¨ ¡§Trading1,¡¨ ¡§Volatility1¡¨ and ¡§Growth1¡¨ factors. In addition, we find the most useless bottom four factors in forecasting are respectively ¡§Size1,¡¨ ¡§Earning Yield1,¡¨ ¡§Value1,¡¨ and ¡§Capital Spending1.¡¨ No matter which strategies we adopt to build the portfolio, the Sharpe ratios of back-testing performance are all higher than the Benchmark, and all bring stable and consistent performance. It actually proves that this model is robust.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

We analyze the relation between volatility spillovers and jumps in financial markets. For this, we compared the volatility spillover index proposed by Diebold and Yilmaz (2009) with a global volatility component, estimated through a multivariate stochastic volatility model with jumps in the mean and in the conditional volatility. This model allows a direct dating of events that alter the global volatility structure, based on a permanent/transitory decomposition in the structure of returns and volatilities, and also the estimation of market risk measures. We conclude that the multivariate stochastic volatility model solves some limitations in the spillover index and can be a useful tool in measuring and managing risk in global financial markets. / Analisamos a relação existente entre spillovers e saltos na volatilidade nos mercados financeiros. Para isso, comparamos o índice de spillover de volatilidade proposto por Diebold and Yilmaz (2009), com um componente de volatilidade global, estimado através de um modelo multivariado de volatilidade estocástica com saltos na média e na volatilidade condicional. Este modelo permite uma datação direta dos eventos que alteram a estrutura de volatilidade global, baseando-se na decomposição das estruturas de retorno e volatilidade entre efeitos permanentes/transitórios, como também a estimação de medidas de risco de mercado. Concluímos que este modelo resolve algumas das limitações do índice de spillover além de fornecer um método prático para mensurar e administrar o risco nos mercados financeiros globais.

This Thesis attempts to construct a Taiwan equity multi-factor model using fundamental cross-sectional approach step by step. It is found that the model involves 28 explanatory factors (including 20 industry factors) and its explanatory power is 58.6% on average. The results of the estimations can be considered very satisfactory. Moreover, based on MFM, this study simulates applications of equity market neutral strategies through quantitative techniques over the period Jan.2003 ¡V Dec.2003. The results verified that the three major characteristics of equity market neutral portfolio performance are: 1) providing absolute return; 2) lack of correlation to the equity benchmark; and 3) low volatility due to hedged portfolio structures.

We analyze the relation between volatility spillovers and jumps in financial markets. For this, we compared the volatility spillover index proposed by Diebold and Yilmaz (2009) with a global volatility component, estimated through a multivariate stochastic volatility model with jumps in the mean and in the conditional volatility. This model allows a direct dating of events that alter the global volatility structure, based on a permanent/transitory decomposition in the structure of returns and volatilities, and also the estimation of market risk measures. We conclude that the multivariate stochastic volatility model solves some limitations in the spillover index and can be a useful tool in measuring and managing risk in global financial markets. / Analisamos a relação existente entre spillovers e saltos na volatilidade nos mercados financeiros. Para isso, comparamos o índice de spillover de volatilidade proposto por Diebold and Yilmaz (2009), com um componente de volatilidade global, estimado através de um modelo multivariado de volatilidade estocástica com saltos na média e na volatilidade condicional. Este modelo permite uma datação direta dos eventos que alteram a estrutura de volatilidade global, baseando-se na decomposição das estruturas de retorno e volatilidade entre efeitos permanentes/transitórios, como também a estimação de medidas de risco de mercado. Concluímos que este modelo resolve algumas das limitações do índice de spillover além de fornecer um método prático para mensurar e administrar o risco nos mercados financeiros globais.

京都大学 / 0048 / 新制・課程博士 / 博士(社会健康医学) / 甲第18648号 / 社医博第60号 / 新制||社医||8(附属図書館) / 31562 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 村井 俊哉, 教授 佐藤 俊哉, 教授 福原 俊一 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

Although previous studies on structural equation modeling (SEM) have indicated that the second-order latent growth model (SOLGM) is a more appropriate approach to longitudinal intervention effects, its application still requires researchers to collect at least three-wave data (e.g. randomized pretest, posttest, and follow-up design). However, in some circumstances, researchers can only collect two-wave data for resource limitations. With only two-wave data, the SOLGM can not be identified and researchers often choose alternative SEM models to fit two-wave data. Recent studies show that the two-wave longitudinal common factor model (2W-LCFM) and latent change score model (2W-LCSM) can perform well for comparing latent change between groups. However, there still lacks empirical evidence about how accurately these two-wave models can estimate the group effects of latent change obtained by three-wave SOLGM (3W-SOLGM). The main purpose of this dissertation, therefore, is trying to examine to what extent the fixed effects of the tree-wave SOLGM can be recovered from the parameter estimates of the two-wave LCFM and LCSM given different simulation conditions. Fundamentally, the supplementary study (study 2) using three-wave LCFM was established to help justify the logistics of different model comparisons in our main study (study 1). The data generating model in both studies is 3W-SOLGM and there are in total 5 simulation factors (sample size, group differences in intercept and slope, the covariance between the slope and intercept, size of time-specific residual, change the pattern of time-specific residual). Three main types of evaluation indices were used to assess the quality of estimation (bias/relative bias, standard error, and power/type I error rate). The results in the supplementary study show that the performance of 3W-LCFM and 3W-LCSM are equivalent, which further justifies the different models' comparison in the main study. The point estimates for the fixed effect parameters obtained from the two-wave models are unbiased or identical to the ones from the three-wave model. However, using two-wave models could reduce the estimation precision and statistical power when the time-specific residual variance is large and changing pattern is heteroscedastic (non-constant). Finally, two real datasets were used to illustrate the simulation results. / Doctor of Philosophy / To collect and analyze the longitudinal data is a very important approach to understand the phenomenon of development in the real world. Ideally, researchers who are interested in using a longitudinal framework would prefer collecting data at more than two points in time because it can provide a deeper understanding of the developmental processes. However, in real scenarios, data may only be collected at two-time points. With only two-wave data, the second-order latent growth model (SOLGM) could not be used. The current dissertation compared the performance of two-wave models (longitudinal common factor model and latent change score model) with the three-wave SOLGM in order to better understand how the estimation quality of two-wave models could be comparable to the tree-wave model. The results show that on average, the estimation from two-wave models is identical to the ones from the three-wave model. So in real data analysis with only one sample, the point estimate by two-wave models should be very closed to that of the three-wave model. But this estimation may not be as accurate as it is obtained by the three-wave model when the latent variable has large variability in the first or last time point. This latent variable is more likely to exist as a statelike construct in the real world. Therefore, the current study could provide a reference framework for substantial researchers who could only have access to two-wave data but are still interested in estimating the growth effect that supposed to obtain by three-wave SOLGM.

Why do we observe such dramatic differences in labour productivity across countries in the macro data? This thesis argues that the growth empirics literature oversimplifies the complexity of the production process across countries and neglects data cross-section and time-series properties, leading to bias in the empirical estimates. Chapter 1 presents two general empirical frameworks for cross-country productivity analysis and demonstrates that they encompass the growth empirics literature of the past decades. We introduce our central argument of cross-country heterogeneity in the impact of observables and unobservables on output and develop this against the background of the pertinent time-series and cross-section properties of macro panel data. Chapter 2 uses data from 48 countries to estimate manufacturing production functions. We discuss standard and novel estimators, focusing on their treatment of parameter heterogeneity and data time-series and cross-section properties. We develop the Augmented Mean Group (AMG) estimator and show its similarity to the Pesaran (2006) Common Correlated Effects (CCE) approach. Our results confirm parameter heterogeneity across countries in the impact of observable inputs on output. We check the robustness of this finding and highlight its implications for empirical measures of TFP. Chapter 3 investigates the heterogeneity of agricultural production technology using data for 128 countries. We develop an extension to the CCE estimators which allows us to suggest that TFP is structured such that countries with similar agro-climatic environment are influenced by the same unobserved factors. This finding offers a possible explanation for the failure of technology-transfer from advanced countries of the temperate 'North' to developing countries of the arid/equatorial 'South'. Our Monte Carlo simulations in Chapter 4 investigate the performance of the AMG, CCE and standard (micro-)panel estimators. Failure to account for cross-section dependence is shown to result in serious distortion of the empirical estimates. We highlight scenarios in which the AMG is biased and offer simple remedies.