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).

Kalman Filter Algorithm for Rating and Prediction in Basketball

The Thesis presents a state-space model for a basketball league and a Kalman filter algorithm for the estimation of the state of the league. In the state-space model, each of the basketball teams is associated with a rating that represents its strength compared to the other teams. The ratings are assumed to evolve in time following a stochastic process with independent Gaussian increments. The estimation of the team ratings is based on the observed game scores that are assumed to depend linearly on the true strengths of the teams and independent Gaussian noise. The team ratings are estimated using a recursive Kalman filter algorithm that produces least squares optimal estimates for the team strengths and predictions for the scores of the future games. Additionally, if the Gaussianity assumption holds, the predictions given by the Kalman filter maximize the likelihood of the observed scores. The team ratings allow probabilistic inference about the ranking of the teams and their relative strengths as well as about the teams’ winning probabilities in future games. The predictions about the winners of the games are correct 65-70% of the time. The team ratings explain 16% of the random variation observed in the game scores. Furthermore, the winning probabilities given by the model are concurrent with the observed scores. The state-space model includes four independent parameters that involve the variances of noise terms and the home court advantage observed in the scores. The Thesis presents the estimation of these parameters using the maximum likelihood method as well as using other techniques. The Thesis also gives various example analyses related to the American professional basketball league, i.e., National Basketball Association (NBA), and regular seasons played in year 2005 through 2010. Additionally, the season 2009-2010 is discussed in full detail, including the playoffs.

Do Households Have Rational Expectations on Future Consumption? Evidence from the Finnish Consumer Survey

Ever since its initial introduction some fifty years ago, the rational expectations paradigm has dominated the way economic theory handles uncertainty. The main assertion made by John F. Muth (1961), seen by many as the father of the paradigm, is that expectations of rational economic agents should essentially be equal to the predictions of relevant economic theory, since rational agents should use information available to them in an optimal way. This assumption often has important consequences on the results and interpretations of the models where it is applied. Although the rational expectations assumption can be applied to virtually any economic theory, the focus in this thesis is on macroeconomic theories of consumption, especially the Rational Expectations–Permanent Income Hypothesis proposed by Robert E. Hall in 1978. The much-debated theory suggests that, assuming that agents have rational expectations on their future income, consumption decisions should follow a random walk, and the best forecast of future consumption level is the current consumption level. Then, changes in consumption are unforecastable. This thesis constructs an empirical test for the Rational Expectations–Permanent Income Hypothesis using Finnish Consumer Survey data as well as various Finnish macroeconomic data. The data sample covers the years 1995–2010. Consumer survey data may be interpreted to directly represent household expectations, which makes it an interesting tool for this particular test. The variable to be predicted is the growth of total household consumption expenditure. The main empirical result is that the Consumer Confidence Index (CCI), a balance figure computed from the most important consumer survey responses, does have statistically significant predictive power over the change in total consumption expenditure. The history of consumption expenditure growth itself, however, fails to predict its own future values. This indicates that the CCI contains some information that the history of consumption decisions does not, and that the consumption decisions are not optimal in the theoretical context. However, when conditioned on various macroeconomic variables, the CCI loses its predictive ability. This finding suggests that the index is merely a (partial) summary of macroeconomic information, and does not contain any significant private information on consumption intentions of households not directly deductible from the objective economic variables. In conclusion, the Rational Expectations–Permanent Income Hypothesis is strongly rejected by the empirical results in this thesis. This result is in accordance with most earlier studies conducted on the topic.

Alikasvoksen mittaus ja kartoitus laserkeilauksella

Tasaikäisen metsän alle muodostuvilla alikasvoksilla on merkitystä puunkorjuun, metsänuudistamisen, näkemä-ja maisema-analyysien sekä biodiversiteetin ja hiilitaseen arvioinnin kannalta. Ilma-aluksista tehtävä laserkeilaus on osoittautunut tehokkaaksi kaukokartoitusmenetelmäksi varttuneiden puustojen mittauksessa. Laserkeilauksen käyttöönotto operatiivisessa metsäsuunnittelussa mahdollistaa aiempaa tarkemman tiedon tuottamisen alikasvoksista, mikäli alikasvoksen ominaisuuksia voidaan tulkita laseraineistoista. Tässä työssä käytettiin tarkasti mitattuja maastokoealoja ja kaikulaserkeilausaineistoja (discrete return LiDAR) usealta vuodelta (1–2 km lentokorkeus, 0,9–9,7 pulssia m-2). Laserkeilausaineistot oli hankittu Optech ALTM3100 ja Leica ALS50-II sensoreilla. Koealat edustavat suomalaisia tasaikäisiä männiköitä eri kehitysvaiheissa. Tutkimuskysymykset olivat: 1) Minkälainen on alikasvoksesta saatu lasersignaali yksittäisen pulssin tasolla ja mitkä tekijät signaaliin vaikuttavat? 2) Mikä on käytännön sovelluksissa hyödynnettävien aluepohjaisten laserpiirteiden selitysvoima alikasvospuuston ominaisuuksien ennustamisessa? Erityisesti haluttiin selvittää, miten laserpulssin energiahäviöt ylempiin latvuskerroksiin vaikuttavat saatuun signaaliin, ja voidaanko laserkaikujen intensiteetille tehdä energiahäviöiden korjaus. Puulajien väliset erot laserkaiun intensiteetissä olivat pieniä ja vaihtelivat keilauksesta toiseen. Intensiteetin käyttömahdollisuudet alikasvoksen puulajin tulkinnassa ovat siten hyvin rajoittuneet. Energiahäviöt ylempiin latvuskerroksiin aiheuttivat alikasvoksesta saatuun lasersignaaliin kohinaa. Energiahäviöiden korjaus tehtiin alikasvoksesta saaduille laserpulssin 2. ja 3. kaiuille. Korjauksen avulla pystyttiin pienentämään kohteen sisäistä intensiteetin hajontaa ja parantamaan kohteiden luokittelutarkkuutta alikasvoskerroksessa. Käytettäessä 2. kaikuja oikeinluokitusprosentti luokituksessa maan ja yleisimmän puulajin välillä oli ennen korjausta 49,2–54,9 % ja korjauksen jälkeen 57,3–62,0 %. Vastaavat kappa-arvot olivat 0,03–0,13 ja 0,10–0,22. Tärkein energiahäviöitä selittävä tekijä oli pulssista saatujen aikaisempien kaikujen intensiteetti, mutta hieman merkitystä oli myös pulssin leikkausgeometrialla ylemmän latvuskerroksen puiden kanssa. Myös 3. kaiuilla luokitustarkkuus parani. Puulajien välillä havaittiin eroja siinä, kuinka herkästi ne tuottavat kaiun laserpulssin osuessa puuhun. Kuusi tuotti kaiun suuremmalla todennäköisyydellä kuin lehtipuut. Erityisen selvä tämä ero oli pulsseilla, joissa oli energiahäviöitä. Laserkaikujen korkeusjakaumapiirteet voivat siten olla riippuvaisia puulajista. Sensorien välillä havaittiin selviä eroja intensiteettijakaumissa, mikä vaikeuttaa eri sensoreilla hankittujen aineistojen yhdistämistä. Myös kaiun todennäköisyydet erosivat jonkin verran sensorien välillä, mikä aiheutti pieniä eroavaisuuksia kaikujen korkeusjakaumiin. Aluepohjaisista laserpiirteistä löydettiin alikasvoksen runkolukua ja keskipituutta hyvin selittäviä piirteitä, kun rajoitettiin tarkastelu yli 1 m pituisiin puihin. Piirteiden selitysvoima oli parempi runkoluvulle kuin keskipituudelle. Selitysvoima ei merkittävästi alentunut pulssitiheyden pienentyessä, mikä on hyvä asia käytännön sovelluksia ajatellen. Lehtipuun osuutta ei pystytty selittämään. Tulosten perusteella kaikulaserkeilausta voi olla mahdollista hyödyntää esimerkiksi ennakkoraivaustarpeen arvioinnissa. Sen sijaan alikasvoksen tarkempi luokittelu (esim. puulajitulkinta) voi olla vaikeaa. Kaikkein pienimpiä alikasvospuita ei pystytä havaitsemaan. Lisää tutkimuksia tarvitaan tulosten yleistämiseksi erilaisiin metsiköihin.

Literature Review: Investigating Drug Transport at the Blood-Brain Barrier and the Effects of P-glycoprotein on the Brain Distribution of Drugs.

The blood-brain barrier (BBB) is a unique barrier that strictly regulates the entry of endogenous substrates and xenobiotics into the brain. This is due to its tight junctions and the array of transporters and metabolic enzymes that are expressed. The determination of brain concentrations in vivo is difficult, laborious and expensive which means that there is interest in developing predictive tools of brain distribution. Predicting brain concentrations is important even in early drug development to ensure efficacy of central nervous system (CNS) targeted drugs and safety of non-CNS drugs. The literature review covers the most common current in vitro, in vivo and in silico methods of studying transport into the brain, concentrating on transporter effects. The consequences of efflux mediated by p-glycoprotein, the most widely characterized transporter expressed at the BBB, is also discussed. The aim of the experimental study was to build a pharmacokinetic (PK) model to describe p-glycoprotein substrate drug concentrations in the brain using commonly measured in vivo parameters of brain distribution. The possibility of replacing in vivo parameter values with their in vitro counterparts was also studied. All data for the study was taken from the literature. A simple 2-compartment PK model was built using the Stella™ software. Brain concentrations of morphine, loperamide and quinidine were simulated and compared with published studies. Correlation of in vitro measured efflux ratio (ER) from different studies was evaluated in addition to studying correlation between in vitro and in vivo measured ER. A Stella™ model was also constructed to simulate an in vitro transcellular monolayer experiment, to study the sensitivity of measured ER to changes in passive permeability and Michaelis-Menten kinetic parameter values. Interspecies differences in rats and mice were investigated with regards to brain permeability and drug binding in brain tissue. Although the PK brain model was able to capture the concentration-time profiles for all 3 compounds in both brain and plasma and performed fairly well for morphine, for quinidine it underestimated and for loperamide it overestimated brain concentrations. Because the ratio of concentrations in brain and blood is dependent on the ER, it is suggested that the variable values cited for this parameter and its inaccuracy could be one explanation for the failure of predictions. Validation of the model with more compounds is needed to draw further conclusions. In vitro ER showed variable correlation between studies, indicating variability due to experimental factors such as test concentration, but overall differences were small. Good correlation between in vitro and in vivo ER at low concentrations supports the possibility of using of in vitro ER in the PK model. The in vitro simulation illustrated that in the simulation setting, efflux is significant only with low passive permeability, which highlights the fact that the cell model used to measure ER must have low enough paracellular permeability to correctly mimic the in vivo situation.

Pelastustoimi alueellisessa kehittämisessä : Länsi-Uudenmaan pelastuslaitoksen saavutettavuus

By law, rescue services must anticipate and plan future rescue situations so that the emergency measures taken in the event of an accident can be accomplished quickly and effectively. To reach this goal, rescue services planning must be up to date. The development of rescue services is di-rected by the Rescue Act, and guidelines such as the readiness program, based on that law. The guidelines give the basic principles for organizing rescue services. This paper studies the ability of rescuers to reach different locations now, and in the future, and whether this happens within the time constraints required by the readiness program. The time per-spective of the study includes both the current time and the future. Predictions of possible future situations are based on zoning information. The goal of the study is to find out whether there are any gaps in the network of fire stations or if gaps will develop in the near future. The strong growth and increase in the population of the greater Helsinki area, and of surrounding towns, creates many challenges for city planning, including rescue services. This study targets the two towns of Espoo and Kirkkonummi, where fast growth specifically into new housing areas, makes planning of rescue services challenging. Many new options are available for planning due to technological developments. The combined methods of planning and geo-informatics used in this study help to determine the need for new resources in rescue services. By using these methods, the planning of rescue services could be done at least 10 years into the future.

Periglasiaalisten ilmiöiden alueellinen mallintaminen subarktisessa ympäristössä

Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed and predicted values. Based on explained deviance, and the results of hierarchical partitioning, the most important environmental variables were mean altitude, vegetation and mean slope angle. The effect of modeling resolution was clear, too coarse resolution caused a loss of information, while finer resolution brought out more localized variation. The models ability to explain and predict periglacial phenomena in the study area were mostly good and moderate respectively. Differences between modeling methods were small, although the explained deviance was higher with GLM-models than GAMs. In turn, GAMs produced more realistic spatial predictions. The single most important environmental variable controlling the occurence of periglacial phenomena was mean altitude, which had strong correlations with many other explanatory variables. The ongoing global warming will have great impact especially in cold environments on high latitudes, and for this reason, an important research topic in the near future will be the response of periglacial environments to a warming climate.