Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Outcomes of ruptured abdominal aortic aneurysm

Ruptured abdominal aortic aneurysm (RAAA) is a life-threatening event, and without operative treatment the patient will die. The overall mortality can be as high as 80-90%; thus repair of RAAA should be attempted whenever feasible. The quality of life (QoL) has become an increasingly important outcome measure in vascular surgery. Aim of the study was to evaluate outcomes of RAAA and to find out predictors of mortality. In Helsinki and Uusimaa district 626 patients were identified to have RAAA in 1996-2004. Altogether 352 of them were admitted to Helsinki University Central Hospital (HUCH). Based on Finnvasc Registry, 836 RAAA patients underwent repair of RAAA in 1991-1999. The 30-day operative mortality, hospital and population-based mortality were assessed, and the effect of regional centralisation and improving in-hospital quality on the outcome of RAAA. QoL was evaluated by a RAND-36 questionnaire of survivors of RAAA. Quality-adjusted life years (QALYs), which measure length and QoL, were calculated using the EQ-5D index and estimation of life expectancy. The predictors of outcome after RAAA were assessed at admission and 48 hours after repair of RAAA. The 30-day operative mortality rate was 38% in HUCH and 44% nationwide, whereas the hospital mortality was 45% in HUCH. Population-based mortality was 69% in 1996-2004 and 56% in 2003-2004. After organisational changes were undertaken, the mortality decreased significantly at all levels. Among the survivors, the QoL was almost equal when compared with norms of age- and sex-matched controls; only physical functioning was slightly impaired. Successful repair of RAAA gave a mean of 4.1 (0-30.9) QALYs for all RAAA patients, although non-survivors were included. The preoperative Glasgow Aneurysm Score was an independent predictor of 30-day operative mortality after RAAA, and it also predicted the outcome at 48- hours for initial survivors of repair of RAAA. A high Glasgow Aneurysm Score and high age were associated with low numbers of QALYs to be achieved. Organ dysfunction measured by the Sequential Organ Failure Assessment (SOFA) score at 48 hours after repair of RAAA was the strongest predictor of death. In conclusion surgery of RAAA is a life-saving and cost-effective procedure. The centralisation of vascular emergencies improved the outcome of RAAA patients. The survivors had a good QoL after RAAA. Predictive models can be used on individual level only to provide supplementary information for clinical decision-making due to their moderate discriminatory value. These results support an active operation policy, as there is no reliable measure to predict the outcome after RAAA.

Rannansiirtyminen ja ihmisen toimintaympäristön muutos Mätäjokilaakson alueella esihistoriallisella ajalla

In this thesis, I study the changing ladscape and human environment of the Mätäjoki Valley, West-Helsinki, using reconstructions and predictive modelling. The study is a part of a larger project funded by the city of Helsinki aming to map the past of the Mätäjoki Valley. The changes in landscape from an archipelago in the Ancylus Lake to a river valley are studied from 10000 to 2000 years ago. Alongside shore displacement, we look at the changing environment from human perspective and predict the location of dwelling sitesat various times. As a result, two map series were produced that show how the landscape changed and where inhabitance is predicted. To back them up, we have also looked at what previous research says about the history of the waterways, climate, vegetation and archaeology. The changing landscape of the river valley is reconstructed using GIS methods. For this purpose, new laser point data set was used and at the same time tested in the context landscape modelling. Dwelling sites were modeled with logistic regression analysis. The spatial predictive model combines data on the locations of the known dwelling sites, environmental factors and shore displacement data. The predictions were visualised into raster maps that show the predictions for inhabitance 3000 and 5000 years ago. The aim of these maps was to help archaeologists map potential spots for human activity. The produced landscape reconstructions clarified previous shore displacement studies of the Mätäjoki region and provided new information on the location of shoreline. From the shore displacement history of the Mätäjoki Valley arise the following stages: 1. The northernmost hills of the Mätäjoki Valley rose from Ancylus Lake approximately 10000 years ago. Shore displacement was fast during the following thousand years. 2. The area was an archipelago with a relatively steady shoreline 9000 7000 years ago. 8000 years ago the shoreline drew back in the middle and southern parts of the river valley because of the transgression of the Litorina Sea. 3. Mätäjoki was a sheltered bay of the Litorina Sea 6000 5000 years ago. The Vantaanjoki River started to flow into the Mätäjoki Valley approximately 5000 years ago. 4. The sediment plains in the southern part of the river valley rose from the sea rather quickly 5000 3000 years ago. Salt water still pushed its way into the southermost part of the valley 4000 years ago. 5. The shoreline proceeded to Pitäjänmäki rapids where it stayed at least a thousand years 3000 2000 years ago. The predictive models managed to predict the locations of dwelling sites moderately well. The most accurate predictions were found on the eastern shore and Malminkartano area. Of the environment variables sand and aspect of slope were found to have the best predictive power. From the results of this study we can conclude that the Mätäjoki Valley has been a favorable location to live especially 6000 5000 years ago when the climate was mild and vegetation lush. The laser point data set used here works best in shore displacement studies located in rural areas or if further specific palaeogeographic or hydrologic analysis in the research area is not needed.