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This paper discusses the factors that affect the share price index of Taiwan solar power industry, crude oil prices and the size of the solar manufacturers in Taiwan and Taiwan's market index into the consideration. In addition, considering whether the policies implemented by our government would change the solar industry in Taiwan¡¦s stocks structural .Using the correlation coefficient, the unit root test, Chow test, cointegration test , vector error correction model, impulse response and forecast error variance decomposition to explore their relationship respectively. The study period starts from January 3,2002 until December 30,2011, a total of 2450 daily data for empirical analysis. By Chow test , we find that there is no structural change of solar stock index after the implementation of the domestic policies. Three international crude oil prices and the total share capital of solar manufacturers in Taiwan and the Taiwan solar power industry stocks index has co-integration relationship, means the three international crude oil prices and solar companies total share capital of solar stock index has a long-run equilibrium relationship. By the error correction model of West Texas crude oil price of Brent crude oil prices, the total share capital of the solar companies in Taiwan and Taiwan solar stock index mutual interaction, and the relationship between changes in Taiwan's solar stock price index and Brent crude oil price, West Texas crude oil prices and the total manufacturers of solar energy manufacturers in Taiwan¡¦s share capital are positive, besides, I also found a positive relationship in the impulse response.

Abstract Japan is the second largest economic power in the world. It has a great deal of FDI outflows but few FDI inflows. Therefore, Japan is in the serious situation of ¡§FDI balance of payments deficit.¡¨ In terms of inward FDI stocks as a percentage of GDP and gross fixed capital formation, Japan is the lowest place of G-7. The purpose of this research is focusing on discussing the shortage of FDI inflows and causing factors which lower the desires of investments in Japan by using the simplest way which is based on the actual situation and the limit of the information in Japan. This paper takes the quarterly data of Japan from 1978 to 2005 and four variables (wage index, real exchange rate, trade and FDI inflows). In this research, the unit root test is used to check if the data have the stationarity or not, and then it uses vector autoregression model (VAR) to proceed impulse response function and forecast error variance decomposition. According to the result of these two approaches, we can figure out the influences of four variables for each other, and then find out the causing factors which lead Japan to have less FDI inflows. The calculation shows that the reason which leads Japanese wages to increase gradually results not only from real exchange rate, trade and FDI inflows, but also from Japanese labor system (lifetime employment system and payment according to working seniority) and the labor quantities. The causality runs from real exchange rate to trade is greater than vice versa. Trade has a positive impact from the real exchange rate which means that the depreciation can accelerate trade. However, the main factor of hindering FDI inflows is Japanese high wages rather than real exchange rate or trade. Therefore, in order to get rid of the depression which was caused by the bubble economy in 1990s, Japanese government not only opens up the restrictions in policy but also takes the control of the prime costs into the most important consideration.

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In this master thesis, the impact of forward-looking macroeconomic information under IFRS 9 is studied using fictional data from a Swedish mortgage loan portfolio. The study employs a time series analysis approach and employs vector autoregression models to model expected credit loss parameters with multiple incorporated macroeconomic parameters. The models are analyzed using impulse response functions to study the impact of macroeconomic shocks and the results show that the unemployment rate, USD/SEK exchange rate and 3-month interest rates have a significant impact on expected credit losses. / I detta examensarbete studeras effekterna av framåtblickande makroekonomisk information enligt IFRS 9 med fiktiv data baserad på en svensk bolåneportfölj. Studien använder sig av tidsserieanalys och vektorautoregressionsmodeller för att modellera förväntade kreditförlust-parametrar med flera inkorporerade makroekonomiska parametrar. Modellerna analyseras med hjälp av impulsresponsfunktioner för att studera effekterna av makroekonomiska chocker. Resultaten visar att arbetslöshet, USD/SEK växelkurs och 3-månaders räntor har en signifikant inverkan på förväntade kreditförluster.

Seminal papers of Solow (1957) and Swan (1956) stimulated debate among economists on the role of technical change in productivity improvements and for that matter economic growth. The consensus is that technological change accounts for a significant proportion of gross national product (GNP) growth in industrialised economies. In the case of Australia, the aggregate productivity performance was poor in the 1970s and 1980s, but picked up very strongly by the 1990s, and was above the OECD average growth level for the first time in its productivity growth history. However, this high productivity growth rate could not be sustained and Australia started to experience a slowdown in productivity growth since 2000. This study empirically measures the performance of productivity in Australia’s economy for the period 1950-2005, using an econometric approach. Time-series data are used to develop econometric models that capture the dynamic interactions between GDP, fixed capital, labour units, human capital, foreign direct investment (FDI) and information and communication technology (ICT). The Johansen (1988) cointegration techniques are used to establish a long-run steady-state relation between or among economic time series. The econometric analysis pays careful attention to the time-series properties of the data by conducting unit root and conintegration tests for the variables in the system. / This study finds that Australia experienced productivity growth in the 1950s, a slow down in the mid 1960s, a very strong productivity growth in the mid 1990s and another slowdown from 2000 onwards. The study finds evidence that human capital, FDI and ICT are very strong determinants of long-run GDP and productivity growth in Australia. The study finds that the three, four and the five factor models are likely to give better measures of productivity performance in Australia as these models recognise human capital, FDI and ICT and include them as separate factors in the production function, This study finds evidence that the previous studies on the Australia’s productivity puzzle have made a very significant omission by not considering human capital, FDI and ICT as additional exogenous variables and by excluding them from the production function for productivity analysis.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed.

It has previously been determined that using 3 or 4 points on a categorized response scale will fail to produce a continuous distribution of scores. However, there is no evidence, thus far, revealing the number of scale points that may indeed possess an approximate or sufficiently continuous distribution. This study provides the evidence to suggest the level of categorization in discrete scales that makes them directly comparable to continuous scales in terms of their measurement properties. To do this, we first introduced a novel procedure for simulating discretely scaled data that was both informed and validated through the principles of the Classical True Score Model. Second, we employed a measurement invariance (MI) approach to confirmatory factor analysis (CFA) in order to directly compare the measurement quality of continuously scaled factor models to that of discretely scaled models. The simulated design conditions of the study varied with respect to item-specific variance (low, moderate, high), random error variance (none, moderate, high), and discrete scale categorization (number of scale points ranged from 3 to 101). A population analogue approach was taken with respect to sample size (N = 10,000). We concluded that there are conditions under which response scales with 11 to 15 scale points can reproduce the measurement properties of a continuous scale. Using response scales with more than 15 points may be, for the most part, unnecessary. Scales having from 3 to 10 points introduce a significant level of measurement error, and caution should be taken when employing such scales. The implications of this research and future directions are discussed.

This dissertation consists of three essays wherein tools of financial econometrics are used to study the three aspects of farmland valuation puzzle: short-term boom-bust cycles, overpricing of farmland, and inconclusive effects of direct government payments. Essay I addresses the causes of unexplained short-term boom-bust cycles in farmland values in a dynamic land pricing model (DLPM). The analysis finds that gross return rate of farmland asset decreases as the farmland asset level increases, and that the diminishing return function of farmland asset contributes to the boom-bust cycles in farmland values. Furthermore, it is mathematically proved that land values are potentially unstable under diminishing return functions. We also find that intertemporal elasticity of substitution, risk aversion, and transaction costs are important determinants of farmland asset values. Essay II examines the apparent overpricing of farmland by decomposing the forecast error variance of farmland prices into forward looking and backward looking components. The analysis finds that in the short run, the forward looking Capital Asset Pricing Model (CAPM) portion of the forecast errors are significantly higher in a boom or bust stage than in a stable stage. This shows that the farmland market absorbs economic information in a discriminative manner according to the stability of the market, and the market (and actors therein) responds to new information gradually as suggested by the theory. This helps to explain the overpricing of farmland, but this explanation works primarily in the short run. Finally, essay III investigates the duel effects of direct government payments and climate change on farmland values. This study uses a smooth coefficient semi-parametric panel data model. The analysis finds that land valuation is affected by climate change and government payments, both through discounted revenues and through effects on the risk aversion of land owners. This essay shows that including heterogeneous risk aversion is an efficient way to mitigate the impacts of misspecifications in a DLPM, and that precipitation is a good explanatory variable. In particular, precipitation affects land values in a bimodal manner, indicating that farmland prices could have multiple peaks in precipitation due to adaption through crop selection and technology alternation.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Globalization provoked in financial markets by means stock exchanges an interchange among the markets over the world. The aim of this study was to examine the relationship of the ten major main economic index of the world represented in New York (DJIA, S&P500 e Nasdaq), Tokyo (NIKKEI 225), London (FSTE 100), São Paulo (IBOV), Shanghai (SSE180), Paris (CAC-40), Frankfurt (DAX-30) and Buenos Aires (Merval) and looking for its co-integration, to demonstrate the behavior of these indexes and the long run equilibrium, from January of 2010 to March of 2011. To investigate the equilibrium and the long rum behavior the error correction model was used jointly with co-integration test and impulse response based on Cholesky decomposition. The results of this study show that the index of stock markets has long term equilibrium, and American markets, Argentina and English showed a strong influence over other markets. With this research we can infer that a relationship exists between the stock markets under study, confirming that the economy in a country can influence the others. In this sense, the contribution of this study, given this range of discussions involving the interconnection of economies with respect to trades made on the stock exchanges, was to show the relationships and influences in the world. / A internacionalização somada à abertura dos mercados financeiros transformou as economias antes fechadas em economias abertas, provocou um intercâmbio entre as economias mundiais por meio das bolsas de valores. O objetivo deste estudo é examinar a relação entre os dez principais índices econômicos do mundo, sendo eles: Nova York (DJIA, S&P500 e Nasdaq), Tóquio (Nikkei 225), Londres (FSTE 100), São Paulo (IBOV), Shangai (SSE180), Paris (CAC), Frankfurt (DAX-30) e Bueno Aires (Merval), por meio da análise de co-integrações para demonstrar o comportamento desses índices e seus equilíbrios no período de janeiro de 2010 a março de 2011. Para investigar e verificar o comportamento em longo prazo, foi utilizado o modelo de correção de erros e teste de impulso-resposta baseado na decomposição de Cholesky. Os resultados deste estudo mostram que existe equilíbrio em longo prazo entre os índices do mercado de ações. Os mercados americano, argentino e inglês mostraram forte influência sobre os demais mercados. Com esta pesquisa, verifica-se que existe uma relação entre os mercados de ações estudados, confirmando que a economia de um país influencia as demais. A contribuição deste estudo é verificar a assertiva das discussões atuais sobre a dependência das economias mundiais com as negociações por meio da bolsa de valores.