In an effort to assess the predictive ability of exchange rate models when data on African countries is sampled, this paper studies nonlinear modelling and prediction of the nominal exchange rate series of the United States dollar to currencies of thirty-eight African states using the smooth transition autoregressive (STAR) model. A three step analysis is undertaken. One, it investigates nonlinearity in all nominal exchange rate series examined using a chain of credible statistical in-sample tests. Significantly, evidence of nonlinear exponential STAR (ESTAR) dynamics is detected across all series. Two, linear models are provided another chance to make it right by shuffling to data on African countries to investigate their predictive power against the tough random walk without drift model. Linear models again failed significantly. Lastly, the predictive ability of nonlinear models against both the random walk without drift and the corresponding linear models is investigated. Nonlinear models display useful forecasting gains over all contending models.

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

This thesis contains three main parts. In the first part, we adapt a model developed for the US economy to the unique Chinese economic and institutional context. The uniqueness is mainly from two perspectives: the dual-channel housing financing system in China and the existence of the shadow banking sector (which differs from the shadow banking in developed economies) in China’s housing market. It would be difficult to obtain a clear picture of the Chinese housing market and macroeconomy without a thorough understanding of these two characteristics. This is due to the crucial role played by shadow banking and other informal finance institutions within the context of China in both the development and purchase of housing, in supporting productive economic activities in general, and that the housing market is in turn intricately connected to the health of the Chinese economy, being a key ‘barometer’. The second part of the research is the quantification of the determinants of the scale of shadow banking in China. The quantification is crucial since policy makers need to be aware of how sensitive shadow banking is to various factors. We develop a theoretical framework to explain the evolution of the scale of shadow banking in China. As part of this research, we investigate whether the real interest rate of household saving deposits, the required reserve ratio and bank loans to business and household are the main factors in explaining the evolution of China’s shadow banking. In the third part of research, we employ a credit risk and macroeconomic stress test to investigate the vulnerability of the commercial banks in China. Our originality here is the integration of both the role of shadow banking and housing market related loans in the commercial banks’ stress test scenarios at the macro level. Since a systematic analysis regarding the effect of changes in the macroeconomy and housing market on the credit risk of commercial banks in China is scarce, we use bank stress tests to analyse the credit risk in terms of the non-performing loans ratio of commercial banks in China; this is in response to changes in the macroeconomic factors and housing market. We address the role of the variation of the scale of shadow banking in China in terms of its contribution to the credit risk because of its uncertainty and close link with the commercial banks. Stress tests often focus on a single bank or financial institution yet we apply the same principles to examine the financial system as a whole in China, which would allow us to quantify the systemic risk in the entire Chinese financial system; and which variables, especially shadow banking contribute to the risks and by how much. This thesis contributes to the understanding of how China’s dual-channel housing finance system and shadow banking affect the evolution of house prices; and also, the main driving factors of the scale of China’s shadow banking and whether the housing market related loans and shadow banking pose risks to commercial banks. Possible research questions raised by the main findings of this thesis will enrich the debate on China’s housing market, shadow banking and regular banks, especially at a time when China is reforming its economic structure.

According to Kim and Nelson (1999) and McConnell and Perez-Quiros (2000), the timing of the Great Moderation occurred in U.S. at 1984Q1. Summers (2005) found out several reasons and different timings of the Great Moderation in the G-7 countries and Australia. During the past fifty years, there was a significantly sharp decline in the volatility of the real growth rate in Australia. Between 1968 and 1982, the standard deviation of the real growth rate was 1.416%¡Fhowever, between 1983 and 1996, the standard deviation of the real growth rate drastically reduced to 0.917%. Based on this obvious situation described above, we successively build up a Markov-Switching Model and Time-Varying Structural Autoregressive Model to investigate the structural break and the sources of the Great Moderation in Australia. The findings turn out that improved monetary policy and the decreased oil shock can account for the explanation of the moderation with the break date of 1984Q1.

This study investigates the long-term and short-term dynamic relationships among the variables of stock returns and institutional investors' buy-sell difference in Taiwan's stock market for the sample periods from Jan., 2000 through May, 2009. Some econometrical methodologies are used in this study, such as unit test, vector autoregressive model, cointegration test, vector error correction model, impulse response function. The major empirical results are shown as follows: 1. Cointegration test For the sample periods, one long-term equilibrium relationship is found from the Johansen's cointegration test, significantly with 5% confidence level between stock year returns and the buy-sell difference for the foreign investment institutions, the domestic investment institutions, and the dealers. The long-term equilibrium relationship is Ry=1.65*QFII+4.28*FUND+35.22*DLR-1142.6. 2. VECM estimation (1)With the vector error correction model (VECM) being applied to the sample periods, the findings indicate that the changes of stock returns are not influenced among the short-term dynamic relationships by the changes of institutional investors' buy-sell difference, but only affected by one-period-lag of itself. (2) Among the short-term dynamic relationships, the changes of foreign investment institutions' buy-sell difference are affected by one-period-lag of institutional investors that positively affected by one-period-lag of the dealers, and inversely affected by one-period-lag of itself and one-period-lag of the domestic investment institutions. However, it is positively affected by one-period-lag of long-term equilibrium, which indicates foreign investment institutions follow positive feedback trading strategies. (3)The changes of the domestic investment institutions' buy-sell difference are only affected by one-period-lag of itself among the short-term dynamic relationships. (4)The changes of the dealers' buy-sell difference are positively affected among the short-term dynamic relationships by one-period-lag of the foreign investment institutions. As for the long-term relationships, it is affected by one-period-lag of long-term equilibrium, which also indicates the dealers follow positive feedback trading strategies. (5)The foreign investment institutions and the dealers have the mutual feedback relationship.

Bakgrund: I november 2014 beslutade Riksbanken att ta steget mot en nollränta och i februari 2015 gick Riksbanken ut med ytterligare en sänkning till -0,10 procent. På så vis fick Sverige för första gången en negativ reporänta. Enligt makroekonomisk teori ska en sänkning av reporäntan stimulera konsumtion och investeringar i ekonomin. Huruvida reporäntan och dess räntesänkningar skapar förutsättningar för företag att investera är ett aktuellt och viktigt forskningsområde. Forskningen i ämnet är tunn på den svenska marknaden och således är forskningsbidraget från denna studie av betydelse.Syfte: Syftet med studien är att undersöka och analysera hur förändringar i reporänta, makro-ekonomiska faktorer samt finansiella indikatorer påverkar investeringar hos svenska företag.Genomförande: Studien bygger på en kvantitativ metod. En Vector Autoregressive model har skapats för att redogöra hur reporäntan, de makroekonomiska faktorerna och de finansiella indikatorerna påverkar företagens investeringar. För att möjliggöra en analys av dessa effekter har impulse response functions skattats i modellen. På så vis undersöks det hur en isolerad enhetsökning i de valda variablerna påverkar företagens investeringar över flera tidsperioder. För att genomföra en mer omfattande analys skattas tre modeller där den första tar hänsyn till både makroekonomiska faktorer och finansiella indikatorer. Den andra modellen exkluderar de finansiella indikatorerna och den tredje modellen speglar reporäntans utveckling i två olika tidsperioder.Resultat: Företagens investeringar påverkas av flertalet faktorer. En enhetsökning av utlåningsräntan, växelkursen och företagens inflationsförväntningar uppvisar ett signifikant negativt samband. En enhetsökning av BNP-tillväxten visar däremot ett signifikant positivt samband. Reporäntan visar ingen direkt effekt på investeringar i de första två modellerna. Däremot uppvisar reporäntan skillnader i den tredje modellen, där ett negativt samband förekommer i den första av de två observerade tidsperioderna. / Background: The central bank of Sweden decided in November 2014 to set the repo rate close to zero. Further they decided to lower the repo rate to -0,10 percent in February 2015. In regard to this, Sweden had a negative repo rate for the first time. According to macroeconomic theory a decrease in the repo rate is performed to stimulate an economy’s investments and consumptions. Whether or not a decrease in interest rates gives greater incentives for firms to invest is a topical subject and an important field of research. In addition to this, the existing research on the Swedish market is insufficient within this field, which gives us further motives to conduct this study.Aim: The purpose of this study is to examine and analyse how changes in the repo rate, macroeconomic factors and financial indicators affects investments of Swedish firms.Completion: The study is conducted with a quantitative approach. A Vector Autoregressive model is created in order to examine the impact of changes in the repo rate, the macroeconomic factors and the financial indicators on firms’ investments. Impulse response functions are estimated to allow a further analysis of these effects. Hence, it is conceivable to examine how one isolated unit-increase in a specific variable affects firms’ investment through several time periods. Furthermore, we estimate three models, one which includes both macroeconomic variables and financial indicators and another which excludes the financial indicators. The last model reflects the repo rate’s impact on investments in two separate time periods.Result: Investments of firms are affected by numerous of factors. One unit-increase of the lending rate, the exchange rate and firms’ expectations of inflation exhibit a negative relation to investments. Furthermore, one unit-increase in GDP-growth tends to increase investments. However, the repo rate has no impact on investments in the first two models. In spite of this, evidence from the third model indicates that the repo rate has a negative impact on investments during the first period.

This paper investigates the nexus between regional income levels and inequality. We present a novel small-scale inequality database for Austrian municipalities to address this question. Our dataset combines individual tax data of Austrian wage tax payer on regionally disaggregated scale with census and geographical information. This setting allows us to investigate regional spillover effects of average income and various measures of income inequality. Using this data set we find distinct regional clusters of both high average wages and high earnings inequality in Austria. Furthermore we use spatial econometric regressions to quantify the effects between income levels and a number of inequality measures such as the Gini and 90/10 quantile ratios. (authors' abstract) / Series: Department of Economics Working Paper Series

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

Periodically correlated time series generally exist in several fields including hydrology, climatology, economics and finance, and are commonly modelled using periodic autoregressive (PAR) model. For a time series with stochastic periodic trend, for which a unit root is expected, a periodically integrated autoregressive PIAR model with periodic and/or seasonal unit root has been shown to be a satisfactory model. The existing theory used the multivariate methodology to study PIAR models. However, this theory is convoluted, majority of it only developed for quarterly time series and its generalisation to time series with larger number of periods is quite cumbersome. This thesis studies the existing theory and highlights its restrictions and flaws. It provides a coherent presentation of the steps for analysing PAR and PIAR models for different number of periods. It presents the different unit roots representations and compares the performance of different unit root tests available in literature. The restrictions of existing studies gave us the impetus to develop a unified theory that gives a clear understanding of the integration and unit roots in the periodic models. This theory is based on the spectral information of the multi-companion matrix of the periodic models. It is more general than the existing theory, since it can be applied to any number of periods whereas the existing methods are developed for quarterly time series. Using the multi-companion method, we specify and estimate the periodic models without the need to extract complicated restrictions on the model parameters corresponding to the unit roots, as required by NLS method. The multi-companion estimation method performed well and its performance is equivalent to the NLS estimation method that has been used in the literature. Analysing integrated multivariate models is a problematic issue in time series. The multi-companion theory provides a more general approach than the error correction method that is commonly used to analyse such time series. A modified state state representation for the seasonal periodically integrated autoregressive (SPIAR) model with periodic and seasonal unit roots is presented. Also an alternative state space representations from which the state space representations of PAR, PIAR and the seasonal periodic autoregressive (SPAR) models can be directly obtained is proposed. The seasons of the parameters in these representations have been clearly specified, which guarantees correct estimated parameters. Kalman filter have been used to estimate the parameters of these models and better estimation results are obtained when the initial values were estimated rather than when they were given.

The aim of this diploma thesis was to get familiar with methods to generate surrogates and how to apply them on cardiovascular signals. The first part of this diploma thesis describes the basic theory of baroreflex function and methods to generate surrogate data. Surrogate data were generated from data, acquired from the database, by using three different methods. In the next part of this diploma thesis, coherence significance between blood pressure and heart intervals was calculated by using surrogates. In the end two hypotheses were defined and tested by which it was detected whether the orthostatic change of the measurement position has effect on the causal coherence change and baroreflex function.