資產報酬的波動程度隱含風險與不確定性，不同的投資者存在不同之風險偏好與風險承受能力，因此瞭解報酬波動之特性有其必要性；又鑑於過去不動產市場對於商用與住宅不動產兩次市場之相關研究較欠缺。因此本研究擬分別探討商用與住宅不動產市場報酬波動特性與差異，並檢視其風險與報酬間之關係。此外，總體經濟環境之變動會影響不動產市場供需關係，進而影響其價格與報酬之波動，因此本研究最後再進一步討論影響其市場報酬之總體經濟因素。 為捕捉不動產市場報酬之波動特性，本研究擬透過GARCH模型分別檢驗商用與住宅不動產市場報酬波動特性與差異；進而應用GARCH-M模型，探討商用與住宅不動產市場報酬與風險之關係；最後透過落遲分配模型實證比較分析顯著影響商用與住宅不動產市場報酬之總體經濟因素。樣本取自台北地區，資料期間為1997年2月至2009年3月之月資料。 實證結果顯示，商用不動產市場中投資人較容易透過自身過去的報酬波動推測未來的波動，反觀住宅不動產市場部分，投資人則傾向注意整體市場消息的散佈，因為其較容易受到外在因素影響而導致報酬波動；由GARCH-M模型實證結果顯示，住宅與商用不動產市場報酬與風險間均呈現顯著正相關，顯示其市場波動風險增加時期，會提供更高之報酬以均衡投資者所面對之較高市場波動風險；由落遲分配模型實證結果顯示，商用與住宅不動產市場報酬確實和總經變數之間有著程度不同的關聯性，所有當期總經變數與不動產報酬間均不存在顯著影響關係，顯示各總經變數對不動產報酬的影響存在時間落差。此外，總經變數對商用報酬的影響程度相對大於對住宅報酬的影響，且總體經濟環境變動對於商用不動產市場報酬之衝擊相對較為迅速。 / This research plans to study the relative volatility characteristic of commercial and residential property returns. In addition, the changing real estate environment can be linked to the macro economy, so we further discusses the relationship between property returns and the macro economy. In order to catch the volatility characteristic of real estate returns, we use GARCH model to examine the volatile behavior of real estate returns of commercial and residential property in the Taipei area during the period of February 1997 to March 2009, and because risk is time-varying in the market, we continue to employ GARCH-M model to observe whether can explain the change in expected returns of commercial and residential property. Furthermore, we use distributed-lag model to explore the relationship between macroeconomic factors and real estate returns. The major findings of this article can be summarized as follows. First, it is easier for investors to infer the future fluctuation through oneself returns in the past in the commercial real estate market, but part on the residential real estate market, the volatility of residential property returns is influenced by external factor more easily. Second, our empirical applications in both commercial and residential real estate markets show that the risk is positively correlated with both property returns and high risk can bring high return. Third, there are different relations of intensity between real estate returns and macroeconomic factors and the impact of macroeconomic factors on real estate returns exist time-lag. In addition, macroeconomic factors’ impact on commercial returns is relatively great, and the environmental change takes place to the impact of the commercial property returns comparatively fast.

The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.

Government fiscal and monetary policy has long been based on the theory that was neither proven nor refuted since its origination. The original form of the Phillips curve has undergone significant modifications but its relevance remains questionable. This thesis examines the correlation between inflation and unemployment observed in the Czech Republic and Germany over the last twenty years. The validity of the theory is tested by advanced methods of time series analysis in the R environment. All the variables are gradually tested which results in the assessment of the correlation between the time series. The outcome of the testing is presented for both countries and a comparison at international level is drawn. Is is discovered that both of the countries have dependencies in their data. Czech republic has significant dependency in both ways, for Germany is the dependency significantly weaker and only in one way.

Doctor of Philosophy / Department of Economics / Wayne Nafziger / This dissertation constructs three empirical essays. The first essay illustrates the causality on the relationship between output (GDP) growth and exports. By using the Modified Wald (MWald) test we observe unidirectional causality from exports to GDP. More specifically, for the robustness we use a Vector Error Correction Model (VECM) model and the Generalized Impulse Response Function Analysis (GIRA). The VECM and the GIRA yield bidirectional causality between exports and GDP, which weakly supports the unidirectional result of the to MWald test. Meanwhile, we confirm that there is structure break by using the structural break test. These results are plausible and consistent with the expectations of our study for the Export Led Growth Hypothesis (ELGH). However, compared with previous studies on the ELGH for Korea, our results are different. Other studies show a bidirectional causality relationship but this study only has unidirectional causality. These differences may be caused from different observation data, various variables, and use of different econometric methodologies. Also, model selection and omitting variables can also significantly change the results of causality testing. The second essay investigates a degree of competition between Korea's and China's exports in the U.S. market by using the substitute elasticity on a simple demand model. The market share of Korean exports has been decreasing while that of China's has been increasing. The results of this study are as follows. First, we find that Korea has a dominant market share of only goods group code 27 in commodity groups over that of China, otherwise having China's dominant market shares over those of Korea for other export sections by using historical trade data. Second, most estimates of substitute elasticity between both countries' exports in the U.S. market are small (inelastic). However, 61 (apparel articles and accessories, knit or crochet), 62 (apparel articles and accessories, not knit etc) and 85 (electric machinery etc, sound equipments, TV equipment, parts) commodity groups' substitute elasticities are large (elastic) and are competitive in the U.S. market compared with those of China. A small value of the elasticity of substitution may be due to an identification problem for a simple standard model as well as measurement errors in prices as a unit value in this study. So, in order to avoid problems such as these, we may need to use appropriate instrumental or proxy variables in the simple standard model, which highly correlate with the independent (unit price) variables and are uncorrelated with measurement error terms. In practice, it is not easy to find good instrumental variables. The final essay evaluates the roles of price and income as important factors that affect Korea's exports by using the most recent monthly data. By using the Autoregressive Distributed Lag (ARDL) bounds testing approach we find the long-run relationship of variables and estimate the long-run price and income elasticities. However, the estimates of these long-run elasticities are statistically insignificant. This may be due to some misspecifications or measurement errors in our model. Meanwhile, due to the existence of the long-run relationship between variables, we construct the Error Correction Model (ECM) in order to observe the short-run dynamics of the elasticities. Specifically, we add a dummy variable into our export demand model to achieve more efficient estimations since the dummy variable reflects a shock in Korea's export; Korea's economic crisis in 1997. In contrast to the long-run elasticity, we find that the short-run elasticities' estimates are more statistically significant. When we use the structure break test to check the structural stability of Korea's export demand, we find that there is no structural break point of 1997. Therefore, a shock of Korea's economic crisis in 1997 might not significantly affect Korea's export demand in a given sample. However, the Information Technology (IT) bubble of the world economy in 2001 and the entry of Korea into the OECD had triggered an increase in Korea's export demand due to existing structural break points of both events. In addition, we find that income elasticities are larger than price elasticities in the short run. This implies that income has more of an impact than that of price for the export demand model in the short run. This also implies that the change of Korea's exports in the short run is more sensitive to changes in foreign income (industrial production) compared with that of price (exchange rate). An interesting result, thus, is that Korea's exports in the short run may have higher export performance on income than that of price (exchange rate). This might be a consequence of the dependence of an increase in foreign income in recent years. In recent years, developing countries have greatly increased their economic growth compared with that of developed countries and Korea's exports have increased into these developing countries. Thus, we confirm that an increase in Korea's exports is mainly affected by income compared with price, specifically in the short run by using recent data.

Thesis (M. Commerce (Economics)) -- University of Limpopo, 2016 / The purpose of this paper is to ascertain the impact of the real effective exchange rate on South Africa‟s trade balance and whether the J-curve phenomenon and the Marshal-Lerner condition are satisfied in the economy. Using data spanning the period 1980Q1 – 2014Q4, the Autoregressive Distributed Lag (ARDL) bounds test as well as the Johansen cointegration test were employed to test for the long run cointegrating relationship between the variables. The ARDL approach was employed to estimate both the long run and short run models as well as to ascertain whether the Marshal – Learner condition as well as the J-curve phenomenon are satisfied in the RSA economy. The results from the cointegration tests show that there is a stable long run equilibrium relationship between the trade balance, real effective exchange rate, domestic GDP, money supply, terms of trade and foreign reserves. The results from the Autoregressive Distributed Lag long run model show that a depreciation of the ZAR improves the trade balance, thus confirming the MarshalLerner condition. The results further reveal that domestic GDP and money supply both have a significant negative impact on the trade balance in the long run with the terms of trade reported positive as well. Foreign reserves were not found to significantly affect the trade balance in the long run. In the short run, the ARDL error correction model shows that a ZAR depreciation leads to a deterioration of the trade balance, thus confirming the J-curve effect for the RSA economy. The terms of trade effect was reported positive in the short run, thus confirming the Harberger-LaursenMetzler effect (HLME) in the process. Money supply, domestic GDP and foreign reserves are also found to have a significant negative impact on the trade balance in the short run. Finally, the error correction model reveals that about 26% of the disequilibrium in the trade balance model is corrected in each quarter.

El presente trabajo de investigación tiene como objetivo evaluar la fuente de energía (renovable y no renovable) que fomente en mayor medida el crecimiento económico en el Perú. Para ello, se observó la relación entre el crecimiento económico y las diferentes fuentes de energía en el corto plazo y el largo plazo. Además, se utilizó el método autorregresivo con retardos distribuidos (ARDL) para confirmar la relación a largo plazo de las series. El modelo ARDL confirmó la cointegración entre las variables y con ello, la relación a largo y corto plazo. Los hallazgos que arrojó la estimación afirman que el consumo de energía renovable se relaciona positivamente con el PBI en el corto y largo plazo. Por lo tanto, se concluye que la economía peruana debería invertir aún más en la exploración y explotación de recursos de energía renovable. / The objective of this research work is to evaluate the source of energy (renewable and non-renewable) that promotes economic growth in Peru to a greater extent. For this, the relationship between economic growth and different energy sources in the short and long term was observed. In addition, the autoregressive distributed lag method (ARDL) was used to confirm the long-term relationship of the series. The ARDL model confirmed the cointegration between the variables and with it, the long- and short-term relationship. The findings that the estimation yielded affirm that the consumption of renewable energy is positively related to the GDP in the short and long term. Therefore, it is concluded that the Peruvian economy should invest even more in the exploration and exploitation of renewable energy resources. / Trabajo de investigación

Thesis (M.Com. (Economics)) -- University of Limpopo, 2022 / The purpose of this study was to investigate growth through innovation and productivity in the South African economy. The study employed the Autoregressive Distributed Lag (ARDL) approach to analyse the annual time series data from the period 1994 to 2018. The data of the study is quantitative and was collected from the South African Reserve Bank and the World Bank. Due to a decline in investment in innovation in South Africa as compared to Brazil, Russia, India and China, the study recommends increased investment in innovation, which may yield positive results on economic growth given the Fourth Industrial Revolution (4IR) presence. The results of the study indicate that there is a long-run relationship between the variables furthermore, in the short-run research and development (R&D), several patents and manufacturing: Labour productivity has a positive and is statistically significant on GDP. However, labour productivity in the non-agricultural sector is positive but statistically insignificant on GDP. Moreover, the findings, in the long run, reveal that R&D, number of patents, and manufacturing: labour productivity is positive and statistically significant on the economic growth in South Africa while labour productivity in the non-agricultural sector has a negative impact on economic growth. This study recommends that policymakers should aim at increasing government-funded R&D, education and human capital to induce productivity and eventually drive up economic growth in South Africa.

The thesis examined the comprehensive causal relationship between the banking sector, stock market development and economic growth in a multi-variate framework using Zimbabwean time series data from 1988 to 2015. Three banking sector development proxies (total financial sector credit, banking credit to private sector and broad money M3) and three stock market development proxies (stock market capitalization, value traded and turnover ratio) were employed to estimate both long and short run relationships between banking sector, stock market and economic growth in Zimbabwe. The study employs the vector error correction model (VECM) as the main estimation technique and the autoregressive distributed lag (ARDL) approach as a robustness testing technique. Results showed that in Zimbabwe a significant causal relationship from banking sector and stock market development to economic growth exists in the long run without any feedback effects. In the short run, however, a negative yet statistically significant causal relationship runs from economic growth to banking sector and stock market development in Zimbabwe. The study further concludes that there is a unidirectional causal relationship running from stock market development to banking sector development in Zimbabwe in both short and long run periods. Nonetheless this relationship between banking sector and stock markets has been found to be more significant in the short run than in the long run. The thesis adopts the complementary view and recommends for the spontaneity implementation of monetary policies as the economy grows. Monetary authorities should thus formulate policies to promote both banks and stock markets with corresponding growth in Zimbabwe’s economy. / Business Management / M. Com. (Business Management)