No description available.

No description available.

No description available.

No description available.

No description available.

Slovenia became an independent republic with its own constitution passed on December 23, 1991. The important step that led to the independence of Slovenia was the December 1990 plebiscite. It was at this plebiscite that the citizens of Slovenia voted for a sovereign and independent state. A public survey called Slovenian Public Opinion (SPO) survey was taken by the government of Slovenia for the plebiscite. The plebiscite counted `YES voters' only those voters who attended and who voted for independence. Non-voters were counted as `NO voters' and `Don't Know' survey responses that could be thought of as missing data that was treated as `YES' or `NO'. Analysis of survey data is done using non-parametric fitting procedure, Bayesian ignorable nonresponse model and Bayesian nonignorable nonresponse model. Finally, a sensitivity analysis is conducted with respect to the different values of a prior parameter. The amazing estimates of the eventual plebiscite outcome show the validity our underlying models.

The Dynamic Stochastic General Equilibrium (DSGE) model, which is based on the New Consensus Macroeconomics (NCM) theoretical framework, has become the workhorse of macroeconomic analysis in academia, research institutes and monetary authorities since the 1980s. The dominating popularity of the DSGE type of models can be witnessed by their extensive use by central banks, such as the Bank of England (BoE), the European Central Bank (ECB), the Federal Reserve (FED) and other central banks. One of the most important and attractive advantages of the DSGE model is its compatibility with a variety of micro- and macro- economic foundations, including short-run nominal rigidities in the goods and services markets, heterogeneities in production, monetary policy and a rich set of exogenous shocks; not that there are no problems with these aspects of the DSGE model as discussed in this thesis. Although a lot of efforts have been made in DSGE modelling in industrialized economies, literature of DSGE modelling in emerging market economies is still at an early stage. The DSGE models especially designed for the economic and social features of these economies are hard to find. In this thesis, we develop a new DSGE model with special consideration of the economic and social features of emerging market economies, and account for some of the DSGE problems. The major development and innovation of this thesis is the heterogeneities not only on the supply side but also in terms of households. Additionally, the housing market and real estate assets are explicitly introduced into our model. Thirdly, we introduce a financial sector into our final model. In this sector, financial frictions are included and entrepreneurs are no longer riskless. Financial intermediates take deposits from households and then lend them to entrepreneurs at an interest rate, which is higherthanthedepositrate. Armed with these developments and improvements,the complete model in this thesis is expected to produce better empirical results and thereby more accurate explanation of economic movements in emerging market economies. Based on these models and data samples, we are able to make empirical analysis on the target economies, namely Brazil, China and India. In conclusion, the models developed in this thesis, based essentially on the DSGE type, can be the pioneer dynamic macroeconomic models for emerging market economies such as Brazil, India, and China. Based on these models, we conduct empirical analyses on data from China, Brazil, and India. We use the Bayesian estimation methodology to identify parameters in our model. The empirical results of these newly developed models show a good coherence with our theoretical hypotheses. Additionally, the performance of these models is consistent with the observed samples and the stylized facts in Brazil, China and India in terms of economic features, such as standard deviations of important economic variables including GDP and fixed asset investment. The results are promising, indicating that our DSGE type of model successfully captures the major economic features and dynamics in these countries with improved accuracy and explanatory power.

The thesis deals with Bayesian approach to statistics and its comparison to frequentist approach. The main aim of the thesis is to compare frequentist and Bayesian approaches to statistics by analyzing statistical inferences, examining the question of subjectivity and objectivity in statistics. Another goal of the thesis is to draw attention to the importance and necessity to teach Bayesian statistics at our University more profound. The thesis includes three chapters. The first chapter presents a Bayesian approach to statistics and its main notions and principles. Statistical inferences are treated in the second chapter. The third chapter deals with comparing Bayesian and frequentist approaches. The final chapter concerns the place of Bayesian approach nowadays in science. Appendix concludes the list of Bayesian textbooks and Bayesian free software.

acase@tulane.edu / The Beta-binomial distribution is often employed as a model for count data in cases where the observed dispersion is greater than would be expected for the standard binomial distribution. Parameter estimation in this setting is typically performed using a Bayesian approach, which requires specifying appropriate prior distributions for parameters. In the context of many applications, incorporating estimates from previous analyses can offer advantages over naive or diffuse priors. An example of this is in the food security setting, where baseline consumption surveys can inform parameter estimation in crisis situations during which data must be collected hastily on smaller samples of individuals. We have developed an approach for Bayesian updating in the beta-binomial model that incorporates adjustable prior weights and enables inference using a bivariate normal approximation for the mode of the posterior distribution. Our methods, which are implemented in the R programming environment, include tools for the estimation of statistical power to detect changes in parameter values. / 1 / Aleksandra Gorzycka

Prediction intervals for class probabilities are of interest in machine learning because they can quantify the uncertainty about the class probability estimate for a test instance. The idea is that all likely class probability values of the test instance are included, with a pre-specified confidence level, in the calculated prediction interval. This thesis proposes a probabilistic model for calculating such prediction intervals. Given the unobservability of class probabilities, a Bayesian approach is employed to derive a complete distribution of the class probability of a test instance based on a set of class observations of training instances in the neighbourhood of the test instance. A random decision tree ensemble learning algorithm is also proposed, whose prediction output constitutes the neighbourhood that is used by the Bayesian model to produce a PI for the test instance. The Bayesian model, which is used in conjunction with the ensemble learning algorithm and the standard nearest-neighbour classifier, is evaluated on artificial datasets and modified real datasets.