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.

Randomised Evaluation of New Technologies within the Population‐Based Cervical Cancer Screening Programme in Finland: Cross‐Sectional Performance and Validity

A randomised and population-based screening design with new technologies has been applied to the organised cervical cancer screening programme in Finland. In this experiment the women invited to routine five-yearly screening are individually randomised to be screened with automation-assisted cytology, human papillomavirus (HPV) test or conventional cytology. By using the randomised design, the ultimate aim is to assess and compare the long-term outcomes of the different screening regimens. The primary aim of the current study was to evaluate, based on the material collected during the implementation phase of the Finnish randomised screening experiment, the cross-sectional performance and validity of automation-assisted cytology (Papnet system) and primary HPV DNA testing (Hybrid Capture II assay for 13 oncogenic HPV types) within service screening, in comparison to conventional cytology. The parameters of interest were test positivity rate, histological detection rate, relative sensitivity, relative specificity and positive predictive value. Also, the effect of variation in performance by screening laboratory on age-adjusted cervical cancer incidence was assessed. Based on the cross-sectional results, almost no differences were observed in the performance of conventional and automation-assisted screening. Instead, primary HPV screening found 58% (95% confidence interval 19-109%) more cervical lesions than conventional screening. However, this was mainly due to overrepresentation of mild- and moderate-grade lesions and, thus, is likely to result in overtreatment since a great deal of these lesions would never progress to invasive cancer. Primary screening with an HPV DNA test alone caused substantial loss in specificity in comparison to cytological screening. With the use of cytology triage test, the specificity of HPV screening improved close to the level of conventional cytology. The specificity of primary HPV screening was also increased by increasing the test positivity cutoff from the level recommended for clinical use, but the increase was more modest than the one gained with the use of cytology triage. The performance of the cervical cancer screening programme varied widely between the screening laboratories, but the variation in overall programme effectiveness between respective populations was more marginal from the very beginning of the organised screening activity. Thus, conclusive interpretations on the quality or success of screening should not be based on performance parameters only. In the evaluation of cervical cancer screening the outcome should be selected as closely as possible to the true measure of programme effectiveness, which is the number of invasive cervical cancers and subsequent deaths prevented in the target population. The evaluation of benefits and adverse effects of each new suggested screening technology should be performed before the technology becomes an accepted routine in the existing screening programme. At best, the evaluation is performed randomised, within the population and screening programme in question, which makes the results directly applicable to routine use.