El objetivo principal de la investigación emprendida en la presente tesis doctoral es elaborar una técnica de construcción de un indicador económico compuesto o un conjunto de dichos indicadores que, correspondiendo al concepto teorético del ciclo económico (comercial), permitirán detectar y predecir los puntos de giro del ciclo económico.Como el punto de partida hemos escogido la definición del ciclo económico propuesta por Burns y Mitchell (1946). Según nuestra opinión, el analisis dinámico factorial es el método idóneo para captar los puntos de giro del ciclo económico en el sentido de Burns y Mitchell. Por un lado, tiene en cuenta los movimientos comunes de varias series macroeconómicas que bajan y suben simultaneamente durante las fases de recesiones y expansiones, respectivamente. Por otro lado, refleja las asimetrías que existen entre las dos fases cíclicas, como, por ejemplo, las tasas de crecimiento y la volatilidad distintas durante las recesiones y expansiones. Ambos rasgos estan subrayados por Burns y Mitchell como características definitivas del ciclo económico.El análisis dinámico factorial en su estado actual exige sin duda ciertas modificaciones y algunas extensiones para obtener las estimaciones insesgadas y consistentes de los indicadores económicos compuestos y para utilizar la información disponible de la mejor manera posible.Nuestra investigación está dirigida, en primer lugar, hacia los economistas prácticos que han optado por utilizar el análisis dinámico factorial para la construcción del indicador del ciclo económico tanto a nivél regional como nacional.La tesis esta compuesta por cinco capítulos donde el primer y el último capítulos son, respectivamente, la introducción y la conclusión. En ellos se exponen los objetivos del estudio y los resultados alcanzados en el curso de la investigación.En el capítulo dos describimos varios metodos de análisis de las fluctuaciones económicas que han sido propuestos durante los últimos 20 años. Por un lado, consideramos los modelos con la dinámica nolineal, concretamente el cambio de regímenes o el Markov switching. Por otro lado, examinamos los modelos lineales del análisis dinámico factorial. Al final del capítulo analizamos el modelo del factor común latente con la dinámica nolineal (con cambios de regímenes) que está construido como una combinación de estos dos metodos principales.En el capítulo tres introducimos un modelo general dinámico multifactorial con la dinámica lineal y nolineal. Este modelo permite captar la dimensión intertemporal (indicador avanzado versus indicador coincidente) de los factores comunes inobservables. Se examinan dos modelos dinámicos alternativos con un factor común inobservable avanzado y un factor común inobservable coincidente. En el primer modelo el factor común coincidente esta influido por el factor común avanzado a través del mecanismo de causalidad de Granger. Mientras que en el segundo modelo los dos factores estan relacionados via la matríz de las probabilidades de transición. Debido a que el factor avanzado contiene información sobre los cambios futuros de las fases cíclicas, ambos modelos permiten hacer predicciones de los puntos de giro del ciclo económico.En el capítulo cuatro elaboramos las técnicas sumplementarias necesarias para resolver algunos problemas de datos que son bastante frecuentes en la actividad de un economista empírico. Los dos problemas más importantes son los cambios estructurales y la falta de observaciones, particularmente cuando los datos que estan disponibles con distintas frecuencias (por ejemplo: los datos mensuales y trimestrales). Estos problemas quiebran la continuidad de la serie temporal y reducen el número de observaciones válidas para el análisis estadístico. Se demuestra que estos problemas se resuelven modificando el modelo de análisis dinámico factorial, con lo que se obtienen estimaciones más eficientes de los parametros del modelo. / The main objective of our research undertaken in this thesis is to elaborate a technique of constructing a composite economic indicator or a set of such indicators which would correspond to the theoretical concept of business cycle and reflect a phenomenon which may be interpreted as the cyclical dynamics of the economy.As a point of departure we have chosen the definition of business cycle proposed by Burns and Mitchell (1946). We believe that the most appropriate method to capture the Burns and Mitchell's cycle would be the dynamic factor analysis.The dynamic factor analysis in its current state requires undoubtedly some refinements and extensions to obtain unbiased and consistent estimates of the composite economic indicators and to use the available information in the best possible way.Our research is mostly oriented towards the practitioners who have opted for using the dynamic factor approach in the construction of the business cycle indicator both at the regional and national levels.The thesis is comprised of five chapters where the first and the last chapters are the introduction and conclusion delineating the objectives of the study and summarizing the results achieved during research.Chapter two describes various approaches to the analysis of economic fluctuations proposed during the last 20 years. On the one hand, it concentrates on models with nonlinear, namely Markov-switching, dynamics, on the other hand, it is concerned with dynamic factor models. Finally, it shows the combined techniques which unify these two principal approaches, thus, modeling common latent factor with regime-switching dynamics.In chapter three we introduce a general multifactor dynamic model with linear and regime-switching dynamics. This model allows capturing the intertemporal (leading versus coincident) dimension of the latent common factors. Two alternative multifactor dynamic models with a leading and a coincident unobserved common factors are examined: a model where the common coincident factor is Granger-caused by the common leading factor and a model where the leading relationship is translated into a set of specific restrictions imposed on the transition probabilities matrix.Chapter four concentrates on the supplementary devices which allow to overcome some data problems which are very frequent in the practitioner's life. Among the most prominent are the structural breaks and missing observations. It is shown that some of these troubles can be coped with by modifying the dynamic common factors models, which leads to more efficient estimates of the parameters of the models.

In over twenty years of operations the Canadian Innovation Centre has evaluated, through its Inventor's Assistance Program (IAP), the commercial potential of over 12,000 early stage inventions. Prior to 1989, the Canadian Innovation Centre (CIC) used a version of the Preliminary Innovation Evaluation System (PIES), developed by Gerald Udell at the Oregon Innovation Center in 1974, to evaluate the commercial potential of early stage inventions. Since 1989, the CIC has used a modified version of PIES in their evaluation process. I first estimate the ability of this program's analysts to forecast the probability that an invention will become commercialized. I also estimate a model to predict the probability that an invention will become commercialized based on the IAP's evaluation of several underlying early stage characteristics of the invention. I find that such a statistical model is based on a limited set of variables and predicts future commercial success almost as well as the IAP's forecast of success. I then use factor analysis to determine if the ratings provided by the CIC evaluation service are representative of the underlying theoretical variable structure of PIES or their modified version. Factor analysis is applied to two distinct periods that are separated by a distinct alteration of the theoretical variable structure in 1989. While I find that the factor analysis provides evidence that the post 1989 theoretical structure does provide interpretation of some of the dimensions in the ranking variables, when a combination of the post 1989 and the pre 1989 structure are examined interpretability of the extracted factors is significantly improved. Finally, I compare the model estimated on the underlying early stage characteristics with a model estimated on the extracted factors. When the predictive accuracy of the two models is compared, I find that both procedures produce models that predict almost equally well. The models and the IAP perform better than R&D managers' predictions of their own R&D projects' successes. The thesis provides recommendations for the assessment and maintenance of evaluation models for inventions, innovations and R&D projects.

In over twenty years of operations the Canadian Innovation Centre has evaluated, through its Inventor's Assistance Program (IAP), the commercial potential of over 12,000 early stage inventions. Prior to 1989, the Canadian Innovation Centre (CIC) used a version of the Preliminary Innovation Evaluation System (PIES), developed by Gerald Udell at the Oregon Innovation Center in 1974, to evaluate the commercial potential of early stage inventions. Since 1989, the CIC has used a modified version of PIES in their evaluation process. I first estimate the ability of this program's analysts to forecast the probability that an invention will become commercialized. I also estimate a model to predict the probability that an invention will become commercialized based on the IAP's evaluation of several underlying early stage characteristics of the invention. I find that such a statistical model is based on a limited set of variables and predicts future commercial success almost as well as the IAP's forecast of success. I then use factor analysis to determine if the ratings provided by the CIC evaluation service are representative of the underlying theoretical variable structure of PIES or their modified version. Factor analysis is applied to two distinct periods that are separated by a distinct alteration of the theoretical variable structure in 1989. While I find that the factor analysis provides evidence that the post 1989 theoretical structure does provide interpretation of some of the dimensions in the ranking variables, when a combination of the post 1989 and the pre 1989 structure are examined interpretability of the extracted factors is significantly improved. Finally, I compare the model estimated on the underlying early stage characteristics with a model estimated on the extracted factors. When the predictive accuracy of the two models is compared, I find that both procedures produce models that predict almost equally well. The models and the IAP perform better than R&D managers' predictions of their own R&D projects' successes. The thesis provides recommendations for the assessment and maintenance of evaluation models for inventions, innovations and R&D projects.

Objectives<p>The geometric configuration of skull is complex and unique to each individual. The main objectives of this study are two fold: 1) to provide a new technique to define the outline of skull profile and 2) to find the common factors defining the ultimate skull configuration in adult population. The secondary objective was to explore the effect of age and sex on skull shape formation.<p>Materials & Methods <p>Ninety-three lateral skull x-ray from the CT scan films were selected and digitized. The lateral skull surface was divided into 3 regions based on the presumed location of coronal and lambdoid sutures. A software program (Canvas 7) was used to match the outer surface of lateral skull with circular curves. Three main curvatures (frontal, parietal, occipital) were consistently identified to overlap the skull periphery. The radius, cord length and inclination of each curvature were measured.. Factor analysis technique was also used to reduce the number of variables explaining the overall shape of skull. Student t-test and regression analysis was also used to explore the effect of sex and age on skull shape. <p>Results <p>There were total of 93 patients in this study (54% male). The average values for three defined curvatures of the skull profile were recorded. Factor analysis produced 3 factors. The first factor explained 32% of total variance and was related to the overall size of the head as represented by total length and the radius of the curvature in vertex and back of the head. The second factor covered 26% of the variance representing the inverse correlation between the angle of the frontal and parietal curves. The third factor revealed the direct correlation of occipital and parietal angle. In all of these factors, the frontal zone variation was independent or opposite of the parieto-occipital zone. A strong direct association between the total length of skull, occipital curve radius and length with the sex was shown. No age related variable was identified.<p>Conclusions <p>There is a large variation in the values of different part of the skull. The skull profile topography can be defined mathematically by two distinct territories: frontal and parieto-occipital zones. These territories hinge on the coronal suture. Therefore, coronal suture may play a dominant role in final skull configuration.

This study aims to investigate the important indicators that contribute to happiness among Beijing residence. The residents of Beijing were taken as the target population for the survey. A questionnaire was used as the main statistical instrument to collect the data from the residents in Beijing. In so doing the investigation employs Factor analyses and chi-square analyses as the main statistical tools used for the analyses in this research. The study found that Beijing residents gained greater happiness in the family, interpersonal relationships, and health status. The analysis also shows that generally, the residence of Beijing feels happier and also in terms of gender basis, females in Beijing feel happier as compare to their male counterpart. It will find that gender, age and education are statistically significant when dealing with happiness.

Abstract Dental implants can be a breakthrough in dental treatment. It has become an important index of the operation performance for the dental clinics. Patients¡¦ knowledge of dental implant medical quality is different from dentists¡¦ due to asymmetric information. This study sample consists of two major groups: 200 patients and 20 dentists, recruited from 15 dental clinics providing services of dental implants in Kaohsiung city. The data was collected by a questionnaire and try to investigate the relationship between the baseline data , the knowledge and attitude of patients about dental implants. The purposes of this study is to identify the difference between patients and dentists about dental implant medical quality . Providing information for patients seeking dental implant care, and for dental implant marketing of dental clinics. The results show that the willingness to accept dental implants is higher in patients well-educated and good oral hygiene . The most popular resources of getting dental implant information are from TV and their dentists. 74% of patients do not know the brand of their dental implants. 84.2% of patients would like to recommend dental implants with the reasons of their dental experiences and the trust of the dentists. The satisfaction rate of dental implant exceeds 70%. As for selecting dentists, medical skills, medical ethics, medical equipments and public praise are the more important factors. Male and young patients are more affected by objective factors; female patients take more consideration of their own feelings. However old patients more concern about interpersonal relationship. Highly-educated patients attach more importance of profession; Lowly-educated patients more care about the environment of the clinics. About medical quality, the most important factors in dentists are medical skills and sterilization, followed by dentist-patients relationship. The first three in patients are medical skills, sterilization and specialist certification. Of all six common factors in patients and dentists, the most different factor is dentist-patient relationship. It shows obviously high in dentists, and it maybe due to the elevation of consumer¡¦s awareness in Taiwan. Key word: dental implant, dental service quality , dental clinic choice, factor analysis

In recent years, many construct wetlands in Taiwan have been built for the purposes of wastewater treatment, river water purification, and ecology conservation. To evaluate the effectiveness of constructed wetlands on water purification, frequent water quality monitoring is needed. In this study, the multivariate statistical analysis was applied to evaluate the contaminant removal efficiency in a constructed wetland, and the time series method was then used to predict the trend of the indicative pollutant concentration in the wetland. Multivariate statistical analysis simplifies the original data into representative factors, or hive off the similarity between data to cluster, and then identify clustering outcomes. In this study, an artificial wetlands at the site around an old bridge locates at the Kaoping River Basin was used as the study site. The statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate water quality characteristics of its. Results from this study show that the removal efficiency for the total coliforms (TC) of System A and B was 98%, 55% for biochemical oxygen demand (BOD), 53% for chemical Oxygen demand (COD), 55% for ammonia nitrogen (NH3-N), and 39% for total nitrogen (TN). Moreover, suspended solids (SS) couldn¡¦t be removed in both A and B systems. The box-and-whisker plot indicates that the water quality of inflow was unstable and variable; however, outflow was turning stable with its flow direction. The major pollutant indicators, except SS, were all in a decreasing tendency. The paired t-test shows p value of each item were lower than 0.05, except total phosphorus (TP) in System A, nitrate nitrogen (NO3-N) and Chlorophyll a (Chl-a) in System B. The correlation parameters from TN, nitrogen oxides (NOx), NO3-N and nitrite nitrogen (NO2-N) and so on were all higher than 0.7. The factor analysis of SPSS shows that 17 water-quality items of the study site could obtain four to six principal components, including nitrate nutrition factor, phosphorus nutrition factor, eutrophication factor, organic factor, and environmental background factor, the major influencing components are nutrition factor and eutrophication factor. The ponds of the study site were classified into two or three clusters depend on in-and-out flow location. This study attempted to establish a forecasting model of wetland pollutants concentration through the time series (ARIMA), results show that the outcome of the B7 pond was better than others. Results indicate that the ARIMA model can be used to simulate the trend of treatment efficiency using the wetland system. Experience and results obtained from this study would provide solutions for water quality control.

The trend of globalization makes the political, economic, educational, culture and creative develop very fast. It also because of the vitality of the network information pushes the global continuously. So society produces an idea of " think globally, act locally ". Base on this research background, Bill McKibben (2005) brings up an idea of Deep Economy. It means Continues forever the development in local place. The reference shows the relations among country, market and civil society in globalization. The cultural and creative industries development stimulates city competitiveness. Professor Kiyoshi Miyazakithinks that the integrated community construction must include people, cultural, place, production and view. These conditions makes local develop appreciation. How is the real aspects for Deep Economy? In this study, it uses the Delphi expert questionnaire method (Delphi) to investigate this problem. It is invited industry, government and school tripartite expertsto conduct interviews and questionnaires. These factorsare discussed in the two-stage indicators convergence results by score of experts. After aggregating data, it cansanalysis to construct the five principal components by exploratory factor analysis.These data shows that Deep Economy should have five faces which are "Local Empowerment", "Identity Participation","Knowledge Learning","Cultural Connotation" and ¡§Creative Economy". This study has well proposals for the future development for Deep Economy. Keywords¡G Deep Economy¡BGlobalization¡BCulture and Creative¡BDelphi¡BExploratory Factor Analysis

The purpose of the present study was to examine the invariance of the factor structure and item response model parameter estimates obtained from a set of 27 items selected from the 2002 and 2003 forms of Your First College Year (YFCY). The first major research question of the present study was: How similar/invariant are the factor structures obtained from two datasets (i.e., identical items, different people)? The first research question was addressed in two parts: (1) Exploring factor structures using the YFCY02 dataset; and (2) Assessing factorial invariance using the YFCY02 and YFCY03 datasets. After using exploratory and confirmatory and factor analysis for ordered data, a four-factor model using 20 items was selected based on acceptable model fit for the YFCY02 and YFCY03 datasets. The four factors (constructs) obtained from the final model were: Overall Satisfaction, Social Agency, Social Self Concept, and Academic Skills. To assess factorial invariance, partial and full factorial invariance were examined. The four-factor model fit both datasets equally well, meeting the criteria for partial and full measurement invariance. The second major research question of the present study was: How similar/invariant are person and item parameter estimates obtained from two different datasets (i.e., identical items, different people) for the homogenous graded response model (Samejima, 1969) and the partial credit model (Masters, 1982)? To evaluate measurement invariance using IRT methods, the item discrimination and item difficulty parameters obtained from the GRM need to be equivalent across datasets. The YFCY02 and YFCY03 GRM item discrimination parameters (slope) correlation was 0.828. The YFCY02 and YFCY03 GRM item difficulty parameters (location) correlation was 0.716. The correlations and scatter plots indicated that the item discrimination parameter estimates were more invariant than the item difficulty parameter estimates across the YFCY02 and YFCY03 datasets.

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