本論文集收錄了三篇有關開放經濟宏觀經濟學的文章。 / 第一篇文章研究了中國從1978年到2010年實際經濟週期。本文首先詳細記錄了中國實際經濟週期三十多年來經驗特征, 我們發現中國的實際經濟週期表現出不同于其他新興市場國家和發達國家的獨特的實際經濟週期經驗特征。再則，我們通過建立實際經濟週期模型和貝葉斯估計方法來檢驗現有新興市場實際經濟週期理論能夠在多大程度上解釋中國實際經濟週期。在我們的估計結果中，我們發現一個包含持久性生產力衝擊的基準模型不能很好的解釋中國實際經濟週期。而在基準模型的基礎上添加了國際金融摩擦的擴展模型（我們稱之為金融摩擦模型）能夠較好的解釋中國實際經濟週期。國際金融摩擦替代了持久性生產力衝擊的作用并優化了模型擬合。 / 第二篇文章研究了發展中國家廣泛使用的財政性油價穩定政策的福利影響。一些評論認為作為發展中國家的主要貿易對象的發達國家，特別是美國，能夠從發展中國家的油價穩定政策中獲利。我們的文章研究了這個論題，我們建立了一個具有美元非對稱性定價特征的兩國家模型。我們發現發展中國家的最優油價補貼率以及它的全球福利影響關鍵性的取決於是否貨幣政策能夠有效的應對油價衝擊。當貨幣政策能夠完全有效並且能夠央行使用最優貨幣政策時， 發展中國家則不需要財政性的油價穩定政策。然而當貨幣政策不能夠完全有效時，即使能夠使用最優貨幣政策，發展中國家還是需要油價補貼來穩定油價。而對美國來說，由於存在非對稱性的美元定價，美國反而受到福利損失。 / 第三篇文章研究了進口中間產品價格衝擊的福利影響和傳遞機制。隨著垂直貿易的快速發展，世界中間產品價格的波動成為了小型開放經濟體國家的主要不確定性衝擊之一。我們建立並且估計了一個兩部門的價格粘性的模型來解釋中間產品價格衝擊如何通過垂直貿易途徑對小型開放經濟體產生影響。我們發現其影響關鍵性的取決于垂直貿易結構和匯率制度。再次，其影響也顯著取決于國際金融市場准入的程度。 / This thesis consists of three essays on Open-Economy Macroeconomics. / The first essay studies real business cycle in Chinese economy. During the past three decades, Chinas economy experienced sizable economic fluctuations along with rapid economic growth. However, the research on Chinese real business cycle is limited. In this paper, we document some stylized facts of Chinese real business cycle from 1978 to 2010. We find that Chinese real business cycle exhibits a mixed pattern that is not consistent with those of developed economies or emerging market economies. Moreover, we investigate to what extent the existing theories of emerging market real business cycle can explain Chinese data using Bayesian estimation of small open economy real business cycle models. Our results show that a benchmark model with permanent pro-ductivity shocks cannot account for stylized facts of Chinese real business cycle very well. Instead, a Financial-Friction model that augments the benchmark with inter-national financial friction significantly improves the model fitness. And international financial friction dominates the role of permanent productivity shocks. / The second essay studies oil price stabilization polices that are adopted extensively in developing countries. Some argue that developed economies, especially the US, may gain from these policies through trade. This paper studies this issue in a two-country model with dollar currency pricing. We find that the optimal level of oil price stabilization chosen by developing countries and its implications for global welfare depend critically on whether monetary policy can eectively respond to oil shocks. In an environment without monetary shocks, when optimal monetary policies are considered, there is no role for oil price stabilization in developing countries. However, to make the oil price stabilization policy redundant, optimal monetary policy is not necessary. Some non-optimal endogenous monetary policies satisfying certain conditions can also make the developing countries choose zero oil price stabilization. The results change when there are monetary shocks. Even with optimal monetary policies, the developing countries will choose a positive level of oil price stabilization. However, due to dollar currency pricing, the US actually loses from the stabilization policy. Our results are well supported by the quantitative analysis in a full-fledged dynamic stochastic general equilibrium model. / The third essay studies the welfare implication and transmission mechanism of imported intermediate goods price shock. With the rapid growth of vertical trade in small open economies, the world price fluctuation of intermediate goods has increasingly become one of major uncertainties faced by these economies. This paper develops and estimates a two-sector sticky-price model to show how intermediate goods price shock affects small open economies through vertical trade. We find that the effects depend critically on the structure of vertical trade and exchange rate policy regime. Furthermore, the quantitative eects of intermediate goods price shock also change significantly with the degree of financial integration. / 1. Real business cycle in Chinese economy -- 2. Oil price stabilization and global welfare -- 3. The effects of intermediate good price shocks on small open economy. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wu, Zhouheng. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references. / Abstracts also in Chinese.

Wang, Boqun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 35-37). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgment --- p.ii / Contents --- p.iii / List of Tables and Figures --- p.iv / Chapter 1. --- Introduction --- p.v / Chapter 2. --- Literature Review --- p.1 / Chapter 2.1. --- Interpretation of the Output Moderation --- p.3 / Chapter 3 . --- Reduction of Output Volatility in China --- p.6 / Chapter 3.1. --- Data Description --- p.8 / Chapter 3.2. --- Basic Statistical Analysis --- p.8 / Chapter 3.3 --- Decomposition of the Reduction in Volatility --- p.13 / Chapter 3.4. --- Compositional Change --- p.13 / Chapter 4. --- Output Volatility Drop from Central-planning to Economic transition…… --- p.15 / Chapter 5. --- Output Moderation during the Reform Period --- p.19 / Chapter 5.1. --- Conceptual Framework --- p.19 / Chapter 5.2. --- General Determinants --- p.19 / Chapter 5.2.1. --- China-specific Determinants --- p.22 / Chapter 5.3. --- Panel Regression --- p.23 / Chapter 5.3.1. --- Without Share --- p.25 / Chapter 5.3.2. --- With Share --- p.29 / Chapter 5.3.3. --- Interpretation of the Regression Result --- p.33 / Chapter 6. --- Conclusion --- p.33 / References --- p.35 / Figures and Tables --- p.38

Abstract: Taiwan has limited resources graphically, so 97% of primary energy source is dependent on import. Industrial sectors are the main sources to Taiwanese economic development since the 1970s, and the oil is the base of the industries, therefore, the fluctuation of world oil price will lead to the fluctuation of domestic business cycles. Besides, Taiwanese economy has highly depended on international trade; therefore the international business cycles also have influence on the domestic business cycles. Furthermore, the international trade accounts for substantial percentage of balance of international payment. Thus, the change in the international trade will also have impact on Taiwanese economy. This paper investigates the influence of international business cycles to the Taiwanese economy. Using a structural vector-autoregressive model (SVAR model) of a small open economy (OE), our SVAR model includes industry product index (IP) of three regions (Asia, Europe, and North America), world oil price, the Taiwanese industry product index and the Taiwanese trade balance. We try to understand how these factors and their variance decompositions explain Taiwanese business cycles. We chose two periods to do the analysis¡G1974:01-1984:01 and 1985:01-2002:04. To summarize, Taiwanese business cycles were much more impacted by the factors from itself. Besides, we can also say that the impact is neither from nominal nor from real variables. Domestic shocks will be more important in explaining Taiwanese economy. Taiwan has limited resource and depends on import; however, the government will control the oil price. Therefore, we conclude that the world oil price does not have huge impact on Taiwanese economy during our studying period. Asian shocks maybe have more influence than other regions on Taiwanese economy gradually during our studying period.

published_or_final_version / Applied Statistics / Master / Master of Social Sciences

Thesis (Ph. D.)--University of New Mexico, 2005. / Includes bibliographical references (leaves 113-118).

In 2008, the global economy went into recession. Millions of jobs were lost, confidence in the financial markets fell and billions of dollars were lost by investors. Prior to the onset of the recession, the major economies of the world (USA, and Western Europe) had experienced a period of economic boom and expansion. Austrian Business Cycle Theory proposes that the roots of the current financial crisis and recessions in general, are found the actions of central banks through credit expansion and manipulation of interest rates. Central banks manipulate interest rates causing them to fall below the natural level, leading to credit expansion and malinvestments. Austrian Business Cycle Theory is based in capital theory. Capital theory incorporates the elements of time and money and allows the setting of a microeconomic foundation. The theory recognises that investment is not an aggregate (as do Keynesians and Monetarists). Opposition to empirical testing by Austrian economists has meant that few statistical analyses of Austrian Business Cycle Theory have been carried out. The apprehension toward empirical testing of Austrian Business Cycle Theory stems from some Austrian economists who argue that human behaviour cannot be captured in statistical terms. Recently, some Austrian economists have begun to do empirical research Austrian Business Cycle Theory and the thesis adds to that growing field. The thesis tests empirically for ABCT in South Africa by using Vector Error Correction Model and Granger causality techniques and the results are as follows: The Vector Error Correction Model shows that any disequilibrium adjustment in the structural equations influences correction mostly through changes in Manufacturing. The disequilibrium adjustment process for Investment is also found to have statistical significance. The results propose that Investment in South Africa is not inert. The Granger causality tests show that credit expansion causes interest rates to be artificially lowered leading to mal-investments. The main policy recommendation is that business cycles can be prevented by not manipulating interest rates and by not increasing credit availability.

Thesis (MBA)--Stellenbosch University, 2000. / ENGLISH ABSTRACT: The assumption underlying this study is that the regime of the economy imparts certain characteristics to the business cycle indicators and that by using a discriminant analysis it would be possible to gain information from the various indicators as to the state of activity in the economy. A discriminant analysis was developed on an Excel spreadsheet. The Schwartz Information Criterion, SIC, was calculated for the models. This value compares how closely the model follows the true data generating process. The discriminant analysis was performed using all the variables or indicators applicable to the model in question. Using a linear programming algorithm the variables were removed from the model in order to maximise the SIC value for the model. The result was a variable set that maximised the information about the regime of the economy available from the various economic indicators. The models' performance was evaluated for post sample performance in a test data set. Five models were developed. They were: • the coincident logistic model; • the one period ahead logistic CLI (composite leading indicator) model; • the one period ahead logistic component model; • the three period ahead logistic CLI model; and • the three period ahead logistic component model. All the models produced meaningful results in the estimation data set for the United States economy. In the test data set only the coincident logistic model was found to give a clear signal of the regime switch. All models applied to the US data showed activity around all the regime switches. Two of the models did not produce useful results when applied to South African economic data. For this reason the one and two period ahead logistic component models were not used. The remaining three models gave clear signals of regime switches for all regime switches in the estimation and the test data set. The best overall model as far as SIC value was the one period ahead logistic CLI model applied to the South African data. The highest SIC for a model applied to the United States data is the logistic coincident model. The models were also evaluated on the number of wrong classifications. The best model in this regard is the coincident logistic model and one period ahead logistic CLI model applied to the United States data. The most accurate model for the South African data was the one-month ahead logistic CLI model in the estimation data set and the logistic coincident model in the test data set. The models were more decisive in the South African data than in the United States data set having a much lower region of uncertainty. Taking into consideration the greater decisiveness in conjunction with accuracy the models performed better with the South African data. The discriminant analysis generates a probability of expansion, which is used in conjunction with a classification rule based on observed frequencies in the estimation data set. A plot of the probability of expansion calculated by the models versus the true data generating process reveals that the models provide meaningful information as to the regime of the economy. The models tend to lag the true data generating process but do show activity around the regime switches. The models when applied to the United States data show good correlation with the true data generating process over the estimation data set but not as good over the test data set. The models perform better when applied to South African data when evaluated graphically. The models when applied to the South African data give good clear signals over all regime switches in all data sets. Indications of regime switches in the estimation data set were clearer than in the test data set. The use of a discriminant analysis for regime classification has been proven to be effective. This method should be used in conjunction with other methods to evaluate business cycle regimes. Useful information is extracted as regards the state of the economy from the various economic indicators. For this reason discriminant analysis of business cycles can be used as an additional tool for the evaluation of business cycle regimes. / AFRIKAANSE OPSOMMING: Die onderliggende aanname van hierdie studie is dat die ekonomiese stelsel sekere eienskappe aan die sakesiklus verleen, en dat 'n diskriminant ontleding dit moontlik maak om inligting te verkry uit die verskeie aanwysers oor die stand van ekonomiese aktiwiteite. 'n Diskriminant ontleding is op 'n Excel-sigblad ontwerp. Die Schwartz Informasie Kriterium (SIK) is vir die modelle bereken. Hierdie waarde dui aan hoe getrou die model die ware datagenereringsproses volg. Die diskriminant ontleding is gedoen deur gebruik te maak van al die veranderlikes of aanwysers wat van toepassing is op die betrokke model. Die veranderlikes is uit die model verwyder deur die gebruik van 'n lineêre programmerings algoritme, ten einde die SIK-waarde van die model te maksimaliseer. Die resultaat was 'n stel veranderlikes wat inligting via die verskeie ekonomiese aanwysers oor die beskikbare ekonomiese stelsel maksimaliseer het. Die model is vir buite-steekproef prestasie in 'n toetsdatastel evalueer. Die volgende vyf modelle is ontwikkel: • samevallende logistiese model • een periode vooruit logistiese saamgestelde leidende aanwysers (SLA)- model • een periode vooruit logistiese komponentmodel • drie periode vooruit logistiese SLA-model • drie periode vooruit logistiese komponentmodel. Al die modelle het betekenisvolle resultate in die steekproefdata vir die ekonomie van die VSA gelewer. In die toetsdatastel het slegs die samevallende logistiese model 'n duidelike aanduiding van regime-verandering gegee. Alle modelle wat op die VSA data toegepas is, het aktiwiteite rondom al die regime-veranderings aangetoon. Twee van die modelle wat op Suid-Afrikaanse data toegepas is, het nie bruikbare resultate opgelewer nie, en om hierdie rede is die een en twee periodes vooruit logistiese komponentmodelle nie gebruik nie. Die oorblywende drie modelle het duidelike aanduidings van regime-veranderings vir alle regime-veranderings aangetoon in die steekproefdata en die toetsdatastel. Die beste oorkoepelende model in terme van SIK-waarde was die een periode vooruit logistiese SLA-model wat op Suid-Afrikaanse data toegepas is. Die grootste SIK-waarde vir 'n model wat op VSA-data toegepas is, is vir die samevallende logistiese model. Modelle is ook evalueer in terme van die foutiewe klassifikasies. Die beste model in hierdie verband is die samevallende logistiese model en die een periode vooruit logistiese SLA-model wat op VSA-data toegepas is. Die mees akkurate model vir Suid-Afrikaanse data was die een maand vooruit logistiese SLA-model in die steekproef datastel en die samevallende logistiese model in die toetsdatastel. Die modelle was meer beslissend in die Suid-Afrikaanse data as in die VSA-datastel, omdat die Suid-Afrikaanse data 'n baie kleiner onsekerheidsgebied openbaar het. Gegewe die groter beslistheid tesame met akkuraatheid, het die modelle beter presteer met Suid-Afrikaanse data. Die diskriminant ontleding skep 'n opswaaiwaarskynlikheid, wat saam met 'n klassifikasiereël, gebaseer op die waargenome frekwensies in die steekproefdata, gebruik word. 'n Stip van die opswaaiwaarskynlikhede, bereken volgens die modelle versus die ware datagenereringsproses, dui daarop dat die modelle betekenisvolle inligting oor die ekonomiese stelsel bied. Die modelle neig om die ware datagenereringsproses te volg, maar toon tog beweging rondom regime-veranderings. Die modelle het goeie korrelasie met die ware datagenereringsproses oor die steekproefdatastel getoon op die VSA-data, maar nie juis goeie korrelasie oor die toetsdatastel nie. Die modelle presteer beter wanneer dit op Suid-Afrikaanse data toegepas word, en gee goeie, duidelike tekens oor alle regime-veranderings in alle datastelle. Aanduidings van regime-veranderings in die steekproefdatastel was duideliker as in die toetsdatastel. 'n Diskriminant ontleding vir stelselklassifikasie het effektief geblyk te wees. Hierdie metode behoort saam met ander metodes gebruik te word om sakesiklusstelsels te evalueer. Nuttige inligting word uit die verskillende ekonomiese aanwysers verkry oor die stand van die ekonomie. Juis om hierdie rede kan 'n diskriminant ontleding van sakesiklusse as bykomende instrument gebruik word om sakesiklusse te evalueer.

M.Com. (Economics) / Please refer to full text to view abstract

M.Comm. / There are many different opinions among economists around the validity and existence of the business cycle. It varies from the total nullification of the existence of a business cycle to the founding of a working definition in this regard. One of the characteristics of the South African economy and similar capitalist systems is an unstable business environment. Periods of economic growth are followed by periods of economic recession when employment, production, prices, profits and general economic welfare are in decline. These phases are known as the business cycle. Economists differ from the early days with relation to the factors that led to changes in total economic activity. Classical economists focussed on the supply side factors as the main cause of the business cycle. John Keynes criticized the early models and presented a model in which change in output is largely dependent on changes in aggregate demand. The latest theory is known as the Real Business Cycle and includes both the supply and demand side factors. The emphasis is, however, on the supply side and argues that changes in the aggregate supply are the main determining factor in economic contraction or expansion. Between 1946 and 1996, 14 complete cycles occurred in the South African economy. The total cycle comprises of the first upward phase, the second upward phase, the first downward phase and the second downward phase. Specific indicators are present during each phase. Car sales are determined by demand. The demand include consumer preference, income, the price of competing or similar goods, expectations of the consumer, availability of credit, consumer confidence as well as the price of the product. The single most important influence on car sales is political stability, economic growth and interest rates. These factors determine the extent of consumer confidence. The occurrence of business cycles in the South African economy and the cyclical tendency of car sales are largely in tandem. Of the 14 upward and onward phases since 1960, 12 phases show positive correlations. This means that car sales represent an adequate barometer with regard to the state of economic activity as a whole in the country. It is also an effective barometer regarding expected future developments on the economic front. The state of the business cycle can be of tremendous value regarding planning in the motor manufacturing industry. The specific phase in which the business cycle is, will provide a sound indication of what kind of success might be anticipated with regard to future sales.

Capital structure is the varying levels of use of debt and equity to finance a firms operations. Firms have an overall leverage level, consisting of a long term and short term level. The economy has different phases over the business cycle ranging from boom cycles where businesses prosper to recessions when they may have difficulties. This cycle can be identified utilising economic indicators, the South African Reserve Bank has classified time periods into growth and recessionary periods, this business cycle phase allocation was utilised for this study to mark recessions. Specific industries from the JSE were selected in an attempt to answer the study’s hypotheses of industry capital structure heterogeneity and that recessions affect capital structure. A sample period from 1995 until 2012 was utilised, containing 5 recessionary years. A stable industry, farming and fisheries, a highly variable industry, the heavy construction industry and a new age industry, the computer services industry, were selected. The results of the study suggested that capital structure varies across industries as evidenced by the mean differences of leverage for total debt, long term debt and short term debt being statistically significant. The computer services industry utilised the least debt over all. The industries all showed a preference for the use of short term debt. Panel data analysis following both Fixed Effect Methodology and Random Effect Methodology was conducted to analyse the firm-specific factors which affect capital structure as well as the use of a dummy variable to capture the effects of recessions. The firm-specific effects under study included asset tangibility, tax, profitability, age and size. South African firms follow a pecking order as shown by the negative relationship observed between profitability and long and short term debt ratios. Existence of weak evidence in this study shows that recessions affect the capital structure of some of the industries studied. The computer services industry is clearly affected, while farming and fisheries industry has very weak evidence that long term debt may be affected, while there is no evidence to show that the heavy construction industry is affected at all. The study concluded with accepting the hypothesis that there are capital structure variations across industries, which are listed on the JSE in boom and bust periods.