Retirement Transition and Well-being : A 16-year Longitudinal Study

The ageing of the labour force and falling employment rates have forced policy makers in industrialized countries to find means of increasing the well-being of older workers and of lengthening their work careers. The main objective of this thesis was to study longitudinally how health, functional capacity, subjective well-being, and lifestyle change as people grow older, and what effect retirement has on these factors and on their relationships. The present study is a follow-up questionnaire study of Finnish municipal workers, conducted in 1981 to 1997 at the Finnish Institute of Occupational Health. In 1981, a postal questionnaire was sent to 7344 municipal workers in different parts of Finland. The respondents were born between 1923 and 1937. A total of 6257 persons responded to the first questionnaire. In the end, a total of 3817 persons had responded to all four (1981, 1985, 1992, 1997) questionnaires. (The response rate was 69% of the living participants). Cross-tabulations, comparison of means, logistic regression analyses and general linear models with repeated measures were used to derive the results. The transition from work life to retirement, and the following years as a pensioner were associated with many changes. Involvement in various activities increased during the transition stage but later decreased to the previous level. Physical exercise was an exception: it became increasingly popular over the years. Perceived health improved markedly from the working stage to the retirement transition stage, even though morbidity increased steadily during the follow-up. On the other hand, functional capacity decreased over the follow-up, especially among those who were occupationally active until the retirement stage. Subjective well-being remained stable during the follow-up period. There were, however, great differences based on the type of work, favouring those whose work had been mental in nature. The impact of activity level on maintaining well-being became greater during the follow-up, whereas the effect of physical functioning diminished. Good physical functioning and an active life-style contributed to staying on at work until normal retirement age. Also work-related factors, i.e. possibilities for development and influence at work, responsibility for others, meaningful work, and satisfaction with working time arrangements were positively related to continuing working. The transition from work to retirement had a positive impact on a person s health and functional capacity. The study results support the view that it should be possible to ease one s work pace during the last years of a work career. This might lower the threshold between work and retirement and convince people that there will still be time to enjoy retirement also a few years later.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

Tetrahydrofuran Clathrate Hydrate Formation

We report on the formation of tetrahydrofuran clathrate hydrate studied by x-ray Raman scattering measurements at the oxygen K edge. A comparison of x-ray Raman spectra measured from water-tetrahydrofuran mixtures and tetrahydrofuran hydrate at different temperatures supports stochastic hydrate formation models rather than models assuming hydrate precursors. This is confirmed by molecular dynamics simulations and density functional theory calculations of x-ray Raman spectra. In addition, changes in the spectra of tetrahydrofuran hydrate with temperatures close to the hydrate's dissociation temperature were observed and may be connected to changes in hydrate's local structure due to the formation of hydrogen bonds between guest and water molecules.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Dynamic nature of proteins - interpretation of residual dipolar couplings

Protein conformations and dynamics can be studied by nuclear magnetic resonance spectroscopy using dilute liquid crystalline samples. This work clarifies the interpretation of residual dipolar coupling data yielded by the experiments. It was discovered that unfolded proteins without any additional structure beyond that of a mere polypeptide chain exhibit residual dipolar couplings. Also, it was found that molecular dynamics induce fluctuations in the molecular alignment and doing so affect residual dipolar couplings. The finding clarified the origins of low order parameter values observed earlier. The work required the development of new analytical and computational methods for the prediction of intrinsic residual dipolar coupling profiles for unfolded proteins. The presented characteristic chain model is able to reproduce the general trend of experimental residual dipolar couplings for denatured proteins. The details of experimental residual dipolar coupling profiles are beyond the analytical model, but improvements are proposed to achieve greater accuracy. A computational method for rapid prediction of unfolded protein residual dipolar couplings was also developed. Protein dynamics were shown to modulate the effective molecular alignment in a dilute liquid crystalline medium. The effects were investigated from experimental and molecular dynamics generated conformational ensembles of folded proteins. It was noted that dynamics induced alignment is significant especially for the interpretation of molecular dynamics in small, globular proteins. A method of correction was presented. Residual dipolar couplings offer an attractive possibility for the direct observation of protein conformational preferences and dynamics. The presented models and methods of analysis provide significant advances in the interpretation of residual dipolar coupling data from proteins.

Dynamic Factor Models and Forecasting Finnish Macroeconomic Variables

In recent years, thanks to developments in information technology, large-dimensional datasets have been increasingly available. Researchers now have access to thousands of economic series and the information contained in them can be used to create accurate forecasts and to test economic theories. To exploit this large amount of information, researchers and policymakers need an appropriate econometric model.Usual time series models, vector autoregression for example, cannot incorporate more than a few variables. There are two ways to solve this problem: use variable selection procedures or gather the information contained in the series to create an index model. This thesis focuses on one of the most widespread index model, the dynamic factor model (the theory behind this model, based on previous literature, is the core of the first part of this study), and its use in forecasting Finnish macroeconomic indicators (which is the focus of the second part of the thesis). In particular, I forecast economic activity indicators (e.g. GDP) and price indicators (e.g. consumer price index), from 3 large Finnish datasets. The first dataset contains a large series of aggregated data obtained from the Statistics Finland database. The second dataset is composed by economic indicators from Bank of Finland. The last dataset is formed by disaggregated data from Statistic Finland, which I call micro dataset. The forecasts are computed following a two steps procedure: in the first step I estimate a set of common factors from the original dataset. The second step consists in formulating forecasting equations including the factors extracted previously. The predictions are evaluated using relative mean squared forecast error, where the benchmark model is a univariate autoregressive model. The results are dataset-dependent. The forecasts based on factor models are very accurate for the first dataset (the Statistics Finland one), while they are considerably worse for the Bank of Finland dataset. The forecasts derived from the micro dataset are still good, but less accurate than the ones obtained in the first case. This work leads to multiple research developments. The results here obtained can be replicated for longer datasets. The non-aggregated data can be represented in an even more disaggregated form (firm level). Finally, the use of the micro data, one of the major contributions of this thesis, can be useful in the imputation of missing values and the creation of flash estimates of macroeconomic indicator (nowcasting).