21 resultados para weather variables


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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).

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Hypertension is one of the major risk factors for cardiovascular morbidity. The advantages of antihypertensive therapy have been clearly demonstrated, but only about 30% of hypertensive patients have their blood pressure (BP) controlled by such treatment. One of the reasons for this poor BP control may lie in the difficulty in predicting BP response to antihypertensive treatment. The average BP reduction achieved is similar for each drug in the main classes of antihypertensive agents, but there is a marked individual variation in BP responses to any given drug. The purpose of the present study was to examine BP response to four different antihypertensive monotherapies with regard to demographic characteristics, laboratory test results and common genetic polymorphisms. The subjects of the present study are participants in the pharmacogenetic GENRES Study. A total of 208 subjects completed the whole study protocol including four drug treatment periods of four weeks, separated by four-week placebo periods. The study drugs were amlodipine, bisoprolol, hydrochlorothiazide and losartan. Both office (OBP) and 24-hour ambulatory blood pressure (ABP) measurements were carried out. BP response to study drugs were related to basic clinical characteristics, pretreatment laboratory test results and common polymorphisms in genes coding for components of the renin-angiotensin system, alpha-adducin (ADD1), beta1-adrenergic receptor (ADRB1) and beta2-adrenergic receptor (ADRB2). Age was positively correlated with BP responses to amlodipine and with OBP and systolic ABP responses to hydrochlorothiazide, while body mass index was negatively correlated with ABP responses to amlodipine. Of the laboratory test results, plasma renin activity (PRA) correlated positively with BP responses to losartan, with ABP responses to bisoprolol, and negatively with ABP responses to hydrochlorothiazide. Uniquely to this study, it was found that serum total calcium level was negatively correlated with BP responses to amlodipine, whilst serum total cholesterol level was negatively correlated with ABP responses to amlodipine. There were no significant associations of angiotensin II type I receptor 1166A/C, angiotensin converting enzyme I/D, angiotensinogen Met235Thr, ADD1 Gly460Trp, ADRB1 Ser49Gly and Gly389Arg and ADRB2 Arg16Gly and Gln27Glu polymorphisms with BP responses to the study drugs. In conclusion, this study confirmed the relationship between pretreatment PRA levels and response to three classes of antihypertensive drugs. This study is the first to note a significant inverse relation between serum calcium level and responsiveness to a calcium channel blocker. However, this study could not replicate the observations that common polymorphisms in angiotensin II type I receptor, angiotensin converting enzyme, angiotensinogen, ADD1, ADRB1, or ADRB2 genes can predict BP response to antihypertensive drugs.

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This dissertation empirically explored interest as a motivational force in university studies, including the role it currently plays and possible ways of enhancing this role as a student motivator. The general research questions were as follows: 1) What role does interest play in university studies? 2) What explains academic success if studying is not based on interest? 3) How do different learning environments support or impede interest-based studying? Four empirical studies addressed these questions. Study 1 (n=536) compared first-year students explanations of their disciplinary choices in three fields: veterinary medicine, humanities and law. Study 2 (n=28) focused on the role of individual interest in the humanities and veterinary medicine, fields which are very different from each other as regards their nature of studying. Study 3 (n=52) explored veterinary students motivation and study practices in relation to their study success. Study 4 (n=16) explored veterinary students interest experience in individual lectures on a daily basis. By comparing different fields and focusing on one study field in more detail, it was possible to obtain a many-sided picture of the role of interest in different learning environments. Questionnaires and quantitative methods have often been used to measure interest in academic learning. The present work is based mostly on qualitative data, and qualitative methods were applied to add to the previous research. Study 1 explored students open-ended answers, and these provided a basis for the interviews in Study 2. Study 3 explored veterinary students portfolios in a longitudinal setting. For Study 4, a diary including both qualitative and quantitative measures was designed to capture veterinary students interest experience. Qualitative content analysis was applied in all four studies, but quantitative analyses were also added. The thesis showed that university students often explain their disciplinary choices in terms of interest. Because interest is related to high-quality learning, the students seemed to have a good foundation for successful studies. However, the learning environments did not always support interest-based studying; Time-management and coping skills were found to be more important than interest in terms of study success. The results also indicated that interest is not the only motivational variable behind university studies. For example, future goals are needed in order to complete a degree. Even so, the results clearly indicated that it would be worth supporting interest-based studying both in professionally and generally oriented study fields. This support is important not only to promote high-quality learning but also meaningful studying, student well-being, and life-long learning.

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The urban heat island phenomenon is the most well-known all-year-round urban climate phenomenon. It occurs in summer during the daytime due to the short-wave radiation from the sun and in wintertime, through anthropogenic heat production. In summertime, the properties of the fabric of city buildings determine how much energy is stored, conducted and transmitted through the material. During night-time, when there is no incoming short-wave radiation, all fabrics of the city release the energy in form of heat back to the urban atmosphere. In wintertime anthropogenic heating of buildings and traffic deliver energy into the urban atmosphere. The initial focus of Helsinki urban heat island was on the description of the intensity of the urban heat island (Fogelberg 1973, Alestalo 1975). In this project our goal was to carry out as many measurements as possible over a large area of Helsinki to give a long term estimate of the Helsinki urban heat island. Helsinki is a city with 550 000 inhabitants and located on the north shore of Finnish Bay of the Baltic Sea. Initially, comparison studies against long-term weather station records showed that our regular, but weekly, sampling of observations adequately describe the Helsinki urban heat island. The project covered an entire seasonal cycle over the 12 months from July 2009 to June 2010. The measurements were conducted using a moving platform following microclimatological traditions. Tuesday was selected as the measuring day because it was the only weekday during the one year time span without any public holidays. Once a week, two set of measurements, in total 104, were conducted in the heterogeneous temperature conditions of Helsinki city centre. In the more homogeneous suburban areas, one set of measurements was taken every second week, to give a total of 52.The first set of measurements took place before noon, and the second 12 hours, just prior to midnight. Helsinki Kaisaniemi weather station was chosen as the reference station. This weather station is located in a large park in the city centre of Helsinki. Along the measurement route, 336 fixed points were established, and the monthly air temperature differences to Kaisaniemi were calculated to produce monthly and annual maps. The monthly air temperature differences were interpolated 21.1 km by 18.1 km horizontal grid with 100 metre resolution residual kriging method. The following independent variables for the kriging interpolation method were used: topographical height, portion of sea area, portion of trees, fraction of built-up and not built-up area, volumes of buildings, and population density. The annual mean air temperature difference gives the best representation of the Helsinki urban heat island effect- Due to natural variability of weather conditions during the measurement campaign care must be taken when interpretation the results for the monthly values. The main results of this urban heat island research project are: a) The city centre of Helsinki is warmer than its surroundings, both on a monthly main basis, and for the annual mean, however, there are only a few grid points, 46 out of 38 191, which display a temperature difference of more than 1K. b) If the monthly spatial variation is air temperature differences is small, then usually the temperature difference between the city and the surroundings is also small. c) Isolated large buildings and suburban centres create their own individual heat island. d) The topographical influence on air temperature can generally be neglected for the monthly mean, but can be strong under certain weather conditions.

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Background: Malaria was prevalent in Finland in the 18th century. It declined slowly without deliberate counter-measures and the last indigenous case was reported in 1954. In the present analysis of indigenous malaria in Finland, an effort was made to construct a data set on annual malaria cases of maximum temporal length to be able to evaluate the significance of different factors assumed to affect malaria trends. Methods: To analyse the long-term trend malaria statistics were collected from 1750–2008. During that time, malaria frequency decreased from about 20,000 – 50,000 per 1,000,000 people to less than 1 per 1,000,000 people. To assess the cause of the decline, a correlation analysis was performed between malaria frequency per million people and temperature data, animal husbandry, consolidation of land by redistribution and household size. Results: Anopheles messeae and Anopheles beklemishevi exist only as larvae in June and most of July. The females seek an overwintering place in August. Those that overwinter together with humans may act as vectors. They have to stay in their overwintering place from September to May because of the cold climate. The temperatures between June and July determine the number of malaria cases during the following transmission season. This did not, however, have an impact on the longterm trend of malaria. The change in animal husbandry and reclamation of wetlands may also be excluded as a possible cause for the decline of malaria. The long-term social changes, such as land consolidation and decreasing household size, showed a strong correlation with the decline of Plasmodium. Conclusion: The indigenous malaria in Finland faded out evenly in the whole country during 200 years with limited or no counter-measures or medication. It appears that malaria in Finland was basically a social disease and that malaria trends were strongly linked to changes in human behaviour. Decreasing household size caused fewer interactions between families and accordingly decreasing recolonization possibilities for Plasmodium. The permanent drop of the household size was the precondition for a permanent eradication of malaria.