Firstly rather accurate approximations to the p value functions of the common Standard CUSUM test and the OLS-based CUSUM test for structural change are derived. Secondly alternative boundaries for both tests are suggested and their properties are examined by simulation of expected p values. It turns out that the power of the OLS-based CUSUM test for early and late structural changes can be improved, whereas this weakness of the Standard CUSUM test cannot be repaired by the new boundaries. / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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There has been significant interest in the empirical performance of the Purchasing Power Parity (PPP) hypothesis. Initial studies were, in general, unfavorable for PPP. Recent research has found that trend-break unit root test derived form linear models do not support the hypothesis of long-run PPP for real exchange rates. In this paper, we propose unit root tests that use STR models and minimum LM unit root tests that endogenously determine structural breaks to investigate long-run PPP in real exchange rates for Asia Pacific countries.

Master of Science / Department of Agricultural Economics / Glynn T. Tonsor / The past several years has marked one of the most heightened periods of fed cattle basis volatility since the installment of live cattle futures contracts. Understanding basis, the difference between local cash price and the futures contract price, is imperative when making marketing and procurement decisions. In the face of increased volatility, the ability to produce accurate basis expectations is no simple task. The purpose of these analyses was to develop econometric models to determine the greatest influencers of fed cattle basis, to test the presence of structural changes in the determinants of fed cattle basis, and to compare out-of-sample forecasting performance. This study analyzed in-sample econometric models using monthly data from January 2003 through September 2016, then compared the results of the competing models. Using the same time period, we then identified the presence of structural breaks in the data. Furthermore, this study analyzed the out-of-sample forecasting performance for January 2012 through September 2016. The out-of-sample results were then compared to in-sample estimations and historical average basis models. The in-sample estimations indicated the important factors that influence fed cattle basis. The results indicate that there are multiple structural breaks present in the determinants of fed cattle basis examined during this study. We can robustly conclude that there was a market structural break present in the fourth quarter of 2013 and within the 2005-2006 time period. The results indicate that the out-of-sample regression estimations were outperformed by historical average models and did not improve our ability to accurately forecast basis. Overall, a 3 or 4 year historical average model should be preferred over econometric estimations when forecasting fed cattle basis.

The revaluation of the yuan in July 2005 was described by the Chinese central bank as a change in the currency regime, rather than merely a changed level of the exchange rate. The reform was said to involve a shift away from the fixed exchange rate, a gradual movement towards greater flexibility, and a peg to a basket of currencies. This paper closely examines the post-July Chinese currency regime utilising contemporary ideas in the econometrics of structural change. We find that the yuan has remained pegged to the US dollar, rather than to a basket, and has extremely limited currency flexibility. We find no evidence of structural change in the post-July period, which suggests that there has been no evolution towards greater flexibility. We show a monitoring procedure which will detect future evolution of the currency regime. / Series: Research Report Series / Department of Statistics and Mathematics

This dissertation consists of two chapters. The first chapter addresses the role of trade barriers in explaining differences in the relative prices of tradables across countries. The second chapter assesses the quantitative importance of changes in comparative advantage in explaining the changes in the compositions of exports and output in South Korea during its growth miracle. In the first chapter I quantitatively address the role of trade barriers in explaining the cross-country distribution of the price of nontradables relative to tradables. Relative prices of nontradables are higher in rich countries than in poor countries. The standard explanation for this is due to Balassa (1964) and Samuelson (1964), where, in each country, the relative price of nontradables is equal to the inverse of relative productivity, and relative productivity is higher in poor countries. I construct a multi-country model of trade in which countries face asymmetric trade barriers. There are many tradable goods and trade barriers determine the cross-country pattern of specialization across tradable goods. The realized pattern of specialization determines measured productivity in the tradables sector, which determines relative prices. Existing trade barriers account for half of the difference in relative prices between rich and poor countries. In the second chapter, I explore how the evolution of comparative advantage can explain the changes in the compositions of exports and output that occurred in South Korea during its growth miracle. From 1960 to 1995 manufacture's share in both exports and output increased. I embed a dynamic, multi-country model of trade into a three-sector model of structural change where agriculture, manufactures, and services are complementary in both consumption and production. I measure productivity growth, in each sector for each country, using a growth accounting procedure. I feed the productivity growth rates into the model and find that the increase in manufacture's share in exports and output are explained by a shift in comparative advantage. The model also matches other aspects of the compositions: the declines in both agriculture's and service's share in exports, and the decline in agriculture's share in output. Finally, the model tracks the composition of output for other countries.

Doctor of Philosophy (PhD) / The purpose of this research is to help understand the key relationships in an evolving economic structure that are driving resource use and greenhouse gas emissions in Australia. The approach involves looking at the factors and relationships that underpin economic growth in Australia. This research seeks to understand the changes in these factors by taking a historical perspective to the determinants of environmental impact through an investigation of structural changes over a period of 30 years. A detailed model is developed using the macro-economic tool of input-output analysis. This model makes it possible to investigate inter-relationships and intra-relationships between sectors of the environment, the economy and the population at disparate scales.

Like many North American agricultural regions, Saskatchewan has experienced significant fundamental structural changes in farming. Structural change encompasses evolution in distribution of farm sizes, land tenure and financial characteristics, as well as variations in demographic and production characteristics. These issues are often a source of discontent among farm populations as it implies these populations are forced to adapt in a number of potentially unpleasant ways. These changes have profound and sometimes poorly understood effects on the rural economy for example, structural change affects rural population and therefore demand for rural infrastructure. <p>Traditional agricultural farm level analysis is often conducted using a representative farm or group, but this framework cannot capture the growing heterogeneity of modern farm operators or the current operating environment in agricultural regions. Farm profiles vary by demographic characteristics, such as age and education, and resource endowments. Agent-based simulation captures this heterogeneity through a farm by farm analysis, where after initialization, the regional economy evolves over time.<p>A synthetic population is created based on survey data and the land characteristics based on the actual land data in CAR 7B of Saskatchewan. A number of different price and yield time paths were created using a bootstrap procedure on historical data and the model evolved to potential agriculture structures that may occur in the model region, 30 years in the future.<p>Structural change occurs endogenously as farms interact in land markets, and make decisions on land use. Agents compete for available land in a purchase and lease market with land selling to the highest bidder. The dynamic nature of agent-based models allows individual farms to adjust land use in response to changing economic conditions and individual preferences. How individuals organize their resources will be critical to farm survival and growth.<p>The results indicate that many of the trends are the same under the different price and yield time paths, however the rate of change is significantly impacted by the price and yield time path that occurs. The model predicted the trend to fewer and larger farms will continue into the future. The forecasted distribution of smaller farms will decline and proportion of large farms will increase, while mid sized farms will remain relatively unchanged. The proportion of mixed farms, land use, and total livestock numbers depend significantly on the price and yield time path. The actual structure that will occur will be the result of the actual individual price and yield time path that occurs.

Like many North American agricultural regions, Saskatchewan has experienced significant fundamental structural changes in farming. Structural change encompasses evolution in distribution of farm sizes, land tenure and financial characteristics, as well as variations in demographic and production characteristics. These issues are often a source of discontent among farm populations as it implies these populations are forced to adapt in a number of potentially unpleasant ways. These changes have profound and sometimes poorly understood effects on the rural economy for example, structural change affects rural population and therefore demand for rural infrastructure. <p>Traditional agricultural farm level analysis is often conducted using a representative farm or group, but this framework cannot capture the growing heterogeneity of modern farm operators or the current operating environment in agricultural regions. Farm profiles vary by demographic characteristics, such as age and education, and resource endowments. Agent-based simulation captures this heterogeneity through a farm by farm analysis, where after initialization, the regional economy evolves over time.<p>A synthetic population is created based on survey data and the land characteristics based on the actual land data in CAR 7B of Saskatchewan. A number of different price and yield time paths were created using a bootstrap procedure on historical data and the model evolved to potential agriculture structures that may occur in the model region, 30 years in the future.<p>Structural change occurs endogenously as farms interact in land markets, and make decisions on land use. Agents compete for available land in a purchase and lease market with land selling to the highest bidder. The dynamic nature of agent-based models allows individual farms to adjust land use in response to changing economic conditions and individual preferences. How individuals organize their resources will be critical to farm survival and growth.<p>The results indicate that many of the trends are the same under the different price and yield time paths, however the rate of change is significantly impacted by the price and yield time path that occurs. The model predicted the trend to fewer and larger farms will continue into the future. The forecasted distribution of smaller farms will decline and proportion of large farms will increase, while mid sized farms will remain relatively unchanged. The proportion of mixed farms, land use, and total livestock numbers depend significantly on the price and yield time path. The actual structure that will occur will be the result of the actual individual price and yield time path that occurs.

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