The dissertation consists of three chapters that study topics related to structural change, spatial and network data. The first chapter considers the problem of testing for a structural change in the spatial lag parameter in a panel model. We propose a likelihood ratio test of the null hypothesis of no change against the alternative hypothesis of a single change. The limiting distribution of the test is derived under the null hypothesis when the number of time periods is large. We also propose a break-date estimator to determine the location of the change point following evidence against the null hypothesis. We present Monte Carlo evidence to show that the proposed procedure performs well in finite samples. We use US state budget data to investigate changes in budget spillovers and the interdependence of fiscal policy within US states. The second chapter proposes a theory of cross-country migration in the form of labor mobility based on regional and sectoral productivity shocks in a multi-country, multi-sector setting. The productivity model when applied to US state data explains both the nominal and relative flow of workers across the U.S. well, which is taken as the frictionless benchmark. On the other hand, when applied to Europe the model explains the relative flow network well, but predicts a higher nominal flow. This missing mass of migrants is explained by socio-cultural-political barriers. We use dyadic regressions to assess the effects of institutional and cultural "distance" between countries in explaining the "European immobility puzzle". The third chapter shows that the "iron-law" of convergence (2\%) still holds for the world. We document a structural break in Africa's convergence rate and argue that Africa was not converging before 2000. The world convergence rate before 2000 was driven by Asian and Latin American countries. We show that recent institutional and infrastructural developments have led the African countries on the path of "catching up". We use Least-Absolute-Shrinkage-and-Selection-Operator (LASSO) to select the variables and a double selection method to estimate the treatment effect in a partially linear model. We compare LASSO variable selections with those obtained using Gradient-Boosting-Method (GBM) and Random Forest.

The aim of this thesis is to study the effect of smoking and drinking on wages in Sweden. The data used in this study is the Swedish Level-of-living survey (LNU) from 1991. A multinomial logit model and the marginal effects from the estimated model are used to study the effect of smoking and drinking on wages in Sweden. The empirical results conclude that medium drinking increases the probability of having a high income, i.e. there is a positive significant relationship between medium drinking and wages in Sweden. Also concluded from the empirical results is that there is not a significant relationship between smoking and wages in Sweden.

<p>The aim of this thesis is to study the effect of smoking and drinking on wages in Sweden. The data used in this study is the Swedish Level-of-living survey (LNU) from 1991. A multinomial logit model and the marginal effects from the estimated model are used to study the effect of smoking and drinking on wages in Sweden. The empirical results conclude that medium drinking increases the probability of having a high income, i.e. there is a positive significant relationship between medium drinking and wages in Sweden. Also concluded from the empirical results is that there is not a significant relationship between smoking and wages in Sweden.</p>

Profile analysis is a multivariate statistical method for comparing the mean vectors for different groups. It consists of three tests, they are the tests for parallelism, level and flatness. The results from each test give information about the behaviour of the groups and the variables in the groups. The test statistics used when there are more than two groups are likelihood-ratio tests. However, issues in the form indeterminate test statistics occur in the high-dimensional setting, that is when there are more variables than observations. This thesis investigates a method to approach this problem by reducing the dimensionality of the data using scores, that is linear combinations of the variables. Three different ways of choosing this score are compared: the eigendecomposition and two variations of the non-negative matrix factorization. The methods are compared using simulations for five different type of mean parameter settings. The results show that the eigendecomposition is the best technique for choosing the score, and that using more scores only slightly improves the results. Moreover, the results for the parallelism and the flatness tests are shown to be very good, but the results for the level hypothesis deviate from the expectation.

In this thesis, we present the use of logistic regression method to develop a credit scoring modelusing the raw data of 4447 customers of a bank. The data of customers is collected under 14independent explanatory variables and 1 default indicator. The objective of this thesis is toidentify optimal coefficients. In order to clean data, the raw data set was put through variousdata calibration techniques such as Kurtosis, Skewness, Winsorization to eliminate outliers.On this winsorized dataset, LOGIT analysis is applied in two rounds with multiple statisticaltests. These tests aim to estimate the significance of each independent variable and modelfitness. The optimal coefficients can be used to obtain the credit scores for new customers witha new data set and rank them according to their credit risk.

本篇文章將採用長期記憶模型之一的HYGARCH模型，搭配1985年廣場協議後的日圓匯率期貨資料來估計日圓期貨匯率買入和放空部位的日報酬風險值，探討控管日圓匯率期貨在使用上的風險。為了更準確地計算風險值，本文採用常態分配、學生t分配以及偏態學生t分配來作模型估計以及風險值之計算。 本文實證的結果將有兩方面的貢獻：首先，實證結果顯示當我們採用厚尾分配估計風險值時，樣本內風險值的估計誤差會與信賴水準的高低呈正比的現象，證明在極端的風險值估計上，厚尾分配均有較佳的表現。其次，與其他使用HYGARCH模型研究日圓匯率的文章相較，本文在風險控管層面上所提供的偏態學生t分配，於估計風險值時，比起只考慮厚尾的對稱學生t分配將來得更為有效，其不但在估計誤差上較小，而且根據Kupiec檢定法，其在樣本內的風險值估計也有較好的表現。此外，本文也將多方證明此資料的偏態分配屬於右偏。 / In order to manage the exposure of the dollar/yen futures returns with regarding the long memory behavior in volatility, we use the HYGARCH(1,d,1) model with the data after the Plaza Accord to compute daily Value-at-Risk (VaR) of long and short trading positions. To take into account the fat-tail situation in financial time series, we estimate the model under the normal, Student-t, and skewed Student-t distributions. The contribution of this article is twofold. First, the empirical results show that the bias of in-sample VaR increases as the confidence level increases when VaR is calculated with a fat-tail distribution. Second, we provide a better distribution, the skewed Student-t innovation, for estimating the HYGARCH model for the Japanese yen in respect of risk management because the bias under the skewed Student-t innovation is smaller than that under the Student-t distribution, and in-sample VaR of the models with a skewed Student-t distribution outperforms based on Kupiec test. In addition, we get the innovation skewed to the right through the in-sample VaR analysis.

股票市場的報酬率相對於金融市埸是非常重要的，因為其背後的真實機率分配對各種資產定價及選擇權的評價模型都有決定性的影響。本文考慮台灣股票市埸具有漲跌停板的限制來驗證實證中股票報酬機率分配的「厚尾」的現象，希望透過我們的研究能對財務理論在國內金融市埸的應用有更進一步的了解。我們選定了常態分配、對數常態分配及一般化第二種貝它分配 (GB2)來當作是台灣股票報酬率的真實機率分配，以動差法比較再以概似比檢定法(LR test)選出一表現最好的機率分配。由選取的25支國內股票中發現一般化第二種貝它分配 (GB2)可以解釋偏態和峰態對報酬率的影響並且也是概似比檢定法所選出的最適報酬率分配，由此可知一般化第二種貝它分配 (GB2)較為適合作為台灣股票報酬的真實機率分配。