This study initially presents historical trends in both the capitalized value and market value of farmland in the eight states comprising the Corn Belt and Lake States production regions as defined by the USDA. An econometric analysis of annual real cash rents per acre prior to determining the capitalized value of farmland in the eight states is then conducted. Two distributed lag models were hypothesized. The comparison of regression results of these two distributed lag models indicates that current year real cash rent can be best explained by current year real net farm income, lagged real net farm income over a period of years, and real cash rent in the previous year. A spreadsheet simulation model is used to project capitalized farmland values in each state as well as regional averages over the 2012-2015 period. These projections reflect alternative assumptions regarding future trends in real net farm income at the state level as well as the rate on 10-year constant maturity U.S. government bonds to assess the potential sensitivity of capitalized farmland values under adverse economic conditions. The projected trends in capitalized farmland values under two alternative stress scenarios reflecting higher interest rates levels and lower net farm income levels indicates that capitalized farmland values are particularly sensitive to interest rate fluctuations since cash rent expectations of landlords are based on current and lagged historical profit performance.

Pollution studies have sought to understand the relationships between adverse health effects and harmful exposures. Many environmental health studies are predicated on the idea that each exposure has both acute and long term health effects that need to be accurately mapped. Considerable work has been done linking air pollution to deleterious health outcomes but the underlying biological pathways and contributing sources remain difficult to identify. There are many statistical issues that arise in the exploration of these longitudinal study designs such as understanding pathways of effects, addressing missing data, and assessing the health effects of multipollutant mixtures. To this end this dissertation aims to address the afore mentioned statistical issues. Our first contribution investigates the mechanistic pathways between air pollutants and measures of cardiac electrical instability. The methods from chapter 1 propose a path analysis that would allow for the estimation of health effects according to multiple paths using structural equation models. Our second contribution recognizes that panel studies suffer from attrition over time and the loss of data can affect the analysis. Methods from Chapter 2 extend current regression calibration approaches by imputing missing data through the use of moving averages and assumed correlation structures. Our last contribution explores the use of factor analysis and two-stage hierarchical regression which are two commonly used approaches in the analysis of multipollutant mixtures. The methods from Chapter 3 attempt to compare the performance of these two existing methodologies for estimating health effects from multipollutant sources.

The repo rate, which is the key interest rate, set by the central banks has been declining for many years and hitting zero in Sweden in late 2014. We analyse the effectiveness on the economy from a change in the repo rate, comparing two time periods with high and low repo rate environments. We use quarterly data on GDP and its components, between 1994 and 2019. For analysing the effectiveness, we use multiple Auto Regressive Distributed Lag (ARDL) modelling to compute a total of 12 models. In our findings, we saw that the effectiveness of a change in repo rate has been increased in the low repo rate environment, making it harder to increase the rate without harming the economy but also increasing the effect of a decrease in the repo rate. Also, we found that the investment component of GDP may exhibit extra high effectiveness in the low repo rate environment. This method of analysing the repo rates impact on the economy could be used for decision makers regarding monetary policies.

This thesis employs the Autoregressive Distributed Lag model (ARDL), Toda-Yamamoto causality analysis, and ordinary least square (OLS for robust estimation) techniques to empirically investigate the impact of economic growth and energy consumption on the environment in Nigeria from 1980 to 2020. The results of cointegration demonstrate a long-term link between the model's input variables. The outcome of the first objective of the study shows that trade and economic development in Nigeria worsen the state of the environment. Environmental quality is accelerated by financial development; nevertheless, FDI is proven to be insignificant in predicting environmental quality. The result demonstrates that FDI and energy use both have the potential to significantly speed up the rate of environmental degradation. Nevertheless, trade has a negligible impact on the environment in the country, and financial development slows down environmental deterioration. The study also finds that the combination between energy and economic development improves Nigeria's environmental quality. The outcome of the fourth objective shows that economic expansion and energy consumption have a favorable impact on the environment. Additionally, environmental degradation, energy use, and economic growth are all causally related. Moreover, the outcome of the robust estimation reveals a positive and significant relationship between economic growth and energy consumption in the environment. Therefore, the study suggests economic policies with environmental control measures. This could be through an emphasis on the use of other alternatives of low-emission energy, that will mitigate the level of C02 and enhance energy utilization for a better environment in the nation.

In this study, the effects of CBRT monetary policy decisions on the consumer, automobile, housing and commercial loans of the banks during the period from the early of 2004 to the middle of 2011 are examined. In order to perform this study, it is benefited from weekly weighted average loan interest rate data of the banks, which is the data having the highest frequency that could be obtained from the electronic data distribution system of CBRT. Monetary policy instruments of Central Bank may change in the course of time or monetary policy could be executed by more than one instrument. Therefore, as the political interest rate would be insufficient in the calculation of the effect of monetary policy on loan interest rates of the banks, Government Dept Securities&rsquo / premiums are used instead of the political interest rates in this study to make it reflect the policies of central bank more clearly as a whole. Among the Government Dept Securities that have different maturity structure, benchmark bonds that are adapted to the expected political interest rate changes and that react to the unexpected interest rate changes at the high rate (reaction coefficient 0.983) are used. In order to weight the cointegration relation between interest rates, unrestricted error correction model is established and it is determined by Bound Test that there is a long-term relation between each interest rate and interest rate of benchmark bond. After a cointegration relation is determined among the serials, autoregressive distributed lag model is used to determine the level of transitivity and it is determined that monetary policy decisions affect the banking interest rate at 77% level and by 13 weeks delay on average.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-06-20T13:47:47Z No. of bitstreams: 1 jeanviniciusmarçal.pdf: 2941702 bytes, checksum: 46f4a5b14de034715ce1e2488e4bd957 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-06-29T12:39:39Z (GMT) No. of bitstreams: 1 jeanviniciusmarçal.pdf: 2941702 bytes, checksum: 46f4a5b14de034715ce1e2488e4bd957 (MD5) / Made available in DSpace on 2017-06-29T12:39:39Z (GMT). No. of bitstreams: 1 jeanviniciusmarçal.pdf: 2941702 bytes, checksum: 46f4a5b14de034715ce1e2488e4bd957 (MD5) Previous issue date: 2017-02-16 / Esta dissertação objetivou analisar o mecanismo de transmissão da política monetária para a taxa de juros de varejo na economia brasileira em uma abordagem não linear. O período principal de análise foi de março de 2011 a março de 2016. A estratégia empírica consistiu no emprego da abordagem de política monetária para o repasse e do uso do modelo de cointegração não linear NARDL. Os principais resultados encontrados são que para as taxas de empréstimos analisadas encontrou-se evidência da assimetria de curto e longo prazo no repasse da taxa SELIC. Conclui-se ainda que a transmissão da taxa de juros no Brasil é caracterizada por apresentar o predomínio do sobre repasse. Por fim, ao comparar o período principal com um período anterior, delimitado de janeiro de 2000 a dezembro de 2012, verificou-se a mudança no sinal da assimetria, passando de negativa para positiva no período atual. / This dissertation aims to analyze interest rate pass-through mechanism from SELIC to retail interest rate in the Brazilian economy in a nonlinear framework. The main review period was from March 2011 to March 2016. The empirical strategy consists in the use of monetary policy approach to interest rate pass-through and use of nonlinear cointegration model NARDL. The main results are that exist evidence of short as well as long-term asymmetry in the interest rate pass-through. We can also conclude that the interest rate pass-through is characterized by the predominance of the more complete pass-through. Finally, when comparing the main period with an earlier period, delimited from January 2000 to December 2012, there was a change in the sign of asymmetry, from negative to positive in the current period.

外人直接投資（foreign direct investment, FDI）在經濟理論中是相當熱門的議題，它代表了讓地主國（host country）國資本累積、技術進步在短時間內快速增加的可能，因此許多國家往往會採取某些吸引外資的政策，再搭配國內制度或是貿易政策的改變，以追求經濟成長。然而，外來直接投資對地主國可能產生的所得重分配的影響，本文即是對此做一深入探討，並以台灣製造業資料來研究外來直接投資是否會擴大工資不均等的情形。 研究期間從1981～2004年共24年，依產業特性將製造業分為十大類，分別採取兩種不同的迴歸模型，包括自我迴歸落遲分配模型（auto regressive distributed lag model, ARDL model）與縱橫資料（panel data）迴歸模型等。實證模型上由生產理論出發，選擇作為解釋工資不均等的變數包括外人直接投資比例、出口比例、進口比例及產出成長率等四個變數。由實證結果得到以下結論： （1）就個別產業來看，FDI對台灣製造業工資不均等的影響並不一致，反應出產業特性不同，FDI所扮演的角色也不盡相同。其中FDI會惡化皮革與毛皮製造業的工資不均等情形，減輕橡膠及塑膠製品製造業與非金屬製品製造業的工資不均等情形，對其他製造業則是無明顯影響。 （2）就整體製造業的情形來看， FDI對工資的不均等的淨效果為正，但效果不大；出口、產出成長率有輕微使工資不均等擴大的情形，而進口則是可輕微縮減工資不均等的狀況。 （3）若是將十大製造業依產品特性區分為「民生」、「化學」、「機械」電子等三大工業，則可以發現FDI對民生工業有明顯擴大工資不均等的情形，在其他兩大工業則是無顯著影響。

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.