This thesis studies the relationship between GDP and Social Progress Index, components of social progress model and their dimensions. Using the dataset of 49 countries and Bayesian Model Averaging (BMA) and clustering analysis we found that there is not straight relationship between GDP and SPI. By testing 15 different models for each of 3 dimension (Basic Human Needs, Foundations of Wellbeing and Opportunity) of SPI we have found that the best variation of components would be to include all of them for each dimension. By using BMA approach we have found that the best model of SPI out of 12 components includes only intercept, tolerance and inclusion variables. The rest of components show quite low probability of inclusion, however, none of them showed 0 posterior probability. JEL Classification A13, C11, E01, I30, Keywords Kuznets, progress, SPI, GDP, BMA Author's e-mail valeria.e.efimenko@gmail.com Supervisor's e-mail daniel.vach@gmail.com

We propose the use of Latent Class Analysis methods to analyze the covariate inclusion patterns across specifications resulting from Bayesian model averaging exercises. Using Dirichlet Process clustering, we are able to identify and describe dependency structures among variables in terms of inclusion in the specifications that compose the model space. We apply the method to two datasets of potential determinants of economic growth. Clustering the posterior covariate inclusion structure of the model space formed by linear regression models reveals interesting patterns of complementarity and substitutability across economic growth determinants.

The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20th century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they could then switch to more adapted crops and farm management systems if required. Such information would also be useful for other sectors of the economy, such as hydropower production, domestic/industrial water consumption, fishing and navigation. A logical solution to the above problem would be seasonal rainfall and streamflow forecasting, which would allow to generate knowledge about the upcoming rainy season based on information available before it's beginning. The research in this thesis sought to improve seasonal rainfall and streamflow forecasting in the Sahel by developing statistical rainfall and streamflow seasonal forecasting models. Sea surface temperature (SST) were used as pools of predictor. The developed method allowed for a systematic search of the best period to calculate the predictor before it was used to predict average rainfall or streamflow over the upcoming rainy season. Eight statistical models consisted of various statistical methods including linear and polynomial regressions were developed in this study. Two main approaches for seasonal streamflow forecasting were developed here: 1) A two steps streamflow forecasting approach (called the indirect method) which first linked the average SST over a period prior to the date of forecast to average rainfall amount in the upcoming rainy season using the eight statistical models, then linked the rainfall amount to streamflow using a rainfall-runoff model (Soil and Water Assessment Tool (SWAT)). In this approach, the forecasted rainfall was disaggregated to daily time step using a simple approach (the fragment method) before being fed into SWAT. 2) A one step streamflow forecasting approach (called as the direct method) which linked the average SST over a period prior to the date of forecast to the average streamflow in the upcoming rainy season using the eight statistical models. To decrease the uncertainty due to model selection, Bayesian Model Averaging (BMA) was also applied. This method is able to explore the possibility of combining all available potential predictors (instead of selecting one based on an arbitrary criterion). The BMA is also capability to produce the probability density of the forecast which allows end-users to visualize the density of expected value and assess the level of uncertainty of the generated forecast. Finally, the economic value of forecast system was estimated using a simple economic approach (the cost/loss ratio method). Each developed method was evaluated using three well known model efficiency criteria: the Nash-Sutcliffe coefficient (Ef), the coefficient of determination (R2) and the Hit score (H). The proposed models showed equivalent or better rainfall forecasting skills than most research conducted in the Sahel region. The linear model driven by the Pacific SST produced the best rainfall forecasts (Ef = 0.82, R2 = 0.83, and H = 82%) at a lead time of up to 12 months. The rainfall forecasting model based on polynomial regression and forced by the Atlantic ocean SST can be used using a lead time of up to 5 months and had a slightly lower performance (Ef = 0.80, R2 = 0.81, and H = 82%). Despite the fact that the natural relationship between rainfall and SST is nonlinear, this study found that good results can be achieved using linear models. For streamflow forecasting, the direct method using polynomial regression performed slightly better than the indirect method (Ef = 0.74, R2 = 0.76, and H = 84% for the direct method; Ef = 0.70, R2 = 0.69, and H = 77% for the indirect method). The direct method was driven by the Pacific SST and had five months lead time. The indirect method was driven by the Atlantic SST and had six months lead time. No significant difference was found in terms of performance between BMA and the linear regression models based on a single predictor for streamflow forecasting. However, BMA was able to provide a probabilistic forecast that accounts for model selection uncertainty, while the linear regression model had a longer lead time. The economic value of forecasts developed using the direct and indirect methods were estimated using the cost/loss ratio method. It was found that the direct method had a better value than the indirect method. The value of the forecast declined with higher return periods for all methods. Results also showed that for the particular watershed under investigation, the direct method provided a better information for flood protection. This research has demonstrated the possibility of decent seasonal streamflow forecasting in the Sirba watershed, using the tropical Pacific and Atlantic SSTs as predictors.The findings of this study can be used to improve the performance of seasonal streamflow forecasting in the Sahel. A package implementing the statistical models developed in this study was developed so that end users can apply them for seasonal rainfall or streamflow forecasting in any region they are interested in, and using any predictor they may want to try.

Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible alternative to GLM while still providing better interpretability than machine learning techniques such as neural networks. In BGNLM, the methods of Bayesian Variable Selection and Model Averaging are applied in an extended GLM setting. Models are fitted to data using MCMC within a genetic framework in an algorithm called GMJMCMC. In this thesis, we present a new implementation of the algorithm as a package in the programming language R. We also present a novel algorithm called S-IRLS-SGD for estimating the MLE of a GLM by subsampling the data. Finally, we present some theory combining the novel algorithm with GMJMCMC/MJMCMC/MCMC and a number of experiments demonstrating the performance of the contributed algorithm.

Frequentist model averaging has started to grow in popularity, and it is considered a good alternative to model selection. It has recently been applied favourably to gen- eralized linear models, where it has mainly been purposed to aid the prediction of probabilities. The performance of averaging estimators has largely been compared to that of models selected using AIC or BIC, without much discussion of model screening. In this paper, we study the performance of model averaging in classification problems, and evaluate performances with reference to a single prediction model tuned using cross-validation. We discuss the concept of model screening and suggest two methods of constructing a candidate model set; averaging over the models that make up the LASSO regularization path, and the so called LASSO-GLM hybrid. By means of a Monte Carlo simulation study, we conclude that model averaging does not necessarily offer any improvement in classification rates. In terms of risk, however, we see that both methods of model screening are efficient, and their errors are more stable than those achieved by the cross-validated model of comparison.

This thesis serves to investigate the varying effects of public health-care expenditure and private health-care expenditure on economic growth in developed and developing countries. I have contributed to the literature by using an expansive geographical dataset, lagged variables to address endogeneity, and model averaging techniques. I do so by first addressing the issue of model uncertainty, which is inherent in growth studies, by using Bayesian Model Averaging as the method of analysis in the thesis. Examination of 126 countries (32 developed and 94 developing) in the period 2000-2018 reveals that there is no variation in the impact of public health expenditure on economic growth between developed and developing countries. Contrary to public health expenditure, private health expenditure has a varying impact on both developed and developing countries. My analysis also reveals that the results hold when lagged variables are used in the model. Public health expenditure has unanimously a negative effect on economic growth in both developed and developing countries. Private health expenditure, on the other hand, has a positive impact on economic growth in developed and developing countries. Furthermore, I found that the results are robust to different model specifications. JEL Classification I15, O11,...

The current literature on international migration is diverse, and there is an ongoing debate as to the size and magnitude of the development-migration nexus, and no consensus about this effect has been reached. In this thesis, I explore quantitatively the effect of GDP (as a measure of development) on migration using a meta-analysis approach by synthesizing the empirical findings on this effect, adjusting for the biases, and controlling for the design of the studies. To examine the phenomenon in a systematic way, I collected 179 regression coefficients from 40 different articles, where the results suggest a weak presence of publication selection. Nevertheless, when correcting for publication bias, the effect of development on migration is rather small. Additionally, to explain the inherent model uncertainty, the Bayesian model averaging (BMA) was conducted. The results suggest that studies controlling for the variables of direct foreign investment and age results in a larger effect of development on migration and that the presence of country- level differences boosts migration inflows, particularly in OECD countries.

Using an early warning system (EWS) methodology, this thesis analyses the predictability of stock market crises from the perspective of behavioural fnance. Specifcally, in our EWS based on the multinomial logit model, we consider in- vestor sentiment as one of the potential crisis indicators. Identifcation of the relevant crisis indicators is based on Bayesian model averaging. The empir- ical results reveal that price-earnings ratio, short-term interest rate, current account, credit growth, as well as investor sentiment proxies are the most rele- vant indicators for anticipating stock market crises within a one-year horizon. Our thesis hence provides evidence that investor sentiment proxies should be a part of the routinely considered variables in the EWS literature. In general, the predictive power of our EWS model as evaluated by both in-sample and out-of-sample performance is promising. JEL Classifcation G01, G02, G17, G41 Keywords Stock market crises, Early warning system, In- vestor sentiment, Crisis prediction, Bayesian model averaging Title Predicting stock market crises using investor sentiment indicators

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第18763号 / 経博第514号 / 新制||経||273(附属図書館) / 31714 / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 准教授 奥井 亮, 講師 末石 直也 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

This dissertation consists of three essays. The first essay estimates a demand function for compressed natural gas as a fuel substitute to diesel fuel for firms with hybrid fleets. The data is from the Energy Information Administration, for the years 1989 to 2009, for 47 states. Results show that an increase of $0.10 in the price of diesel fuel will increase compressed natural gas demand by 5.59%. The second essay focuses on regional trade agreements (RTAs). A number of studies have found that RTAs significantly increase members' trade flows. While recent studies have begun to explore the reasons for this, none have examined whether the RTA trade effect varies systematically with the number and type of policy areas covered by the agreement. While the empirical trade literature has shed considerable light on the trade-creating ability of RTAs (Grant and Lambert, 2008), much less is known about why these agreements are so successful. In this study, we draw on a new database from the World Trade Organization of trade policy areas covered by RTAs to examine whether the degree of trade liberalization is an important determinant of the RTA trade effect. An augmented, theoretically consistent gravity equation is developed to explore the effects of RTAs on trade, conditional on the policy areas they include. In particular, we investigate two policy areas that are particularly important for agricultural trade, sanitary and phytosanitary measures (SPS) and technical barriers to trade (TBT). The results suggest that harmonization of non-tariff measures inside RTAs matters: Agreements that liberalize these policies increase members' agricultural trade by an additional 62 percent compared to agreements that do not. We conclude that studying the components of RTAs -- in particular, the policy areas covered by these agreements -- is important when analyzing the determinants of RTA trade effects. The third essay uses Bayesian Model Averaging (BMA) to study the effect of membership in the General Agreement on Tariffs and Trade (GATT), the predecessor to the World Trade Organization (WTO), and the WTO on trade flows. Existing GATT/WTO literature is not univocal as to whether membership in the GATT/WTO increases trade flows. In this study, Bayesian model averaging (BMA) is used in the presence of theoretical uncertainty to address whether membership in the GATT/WTO plays a role in the gravity model. Several datasets are examined: a dataset from a previous study; and two datasets compiled for this study, world trade and agricultural trade. Results show, for all three sets of data, that membership in the GATT/WTO does belong in the gravity equation and increases trade flows. / Ph. D.