The exchange rate target zone has been paid much attention in the early 1990 initially by Krugman (1991).It expressed when exchange rate surpasses the band of exchange rate that implicitly or explicitly determined by the central bank, the central Bank will intervene the foreign exchange by buying or selling foreign exchange to ensure the exchange rate staying inside the band, otherwise, the exchange rate will be allowed to fluctuate inside the band freely.According to Krugman (1991), when economic system faces random disturbances, the exchange rate target zone regime is helpful to narrow down the exchange rate volatility contrast to that in the floating exchange rate regime. That is, the exchange rate target zone has more essential stability,which is called ``honeymoon effect". In recent decade, Krugman's exchange rate target zone model has been tested empirically.In this thesis, the smooth transition autoregression with target zone (STARTZ) proposed originally by Lundbergh and Ter"{a}svirta (2006) and logistic smooth transition regression with two thresholds (LSTR2) are used to make comparisons for in-sample fitness and out-of-sample forcastability.Furthermore, we also test two important assumptions of the exchange rate target zone model: the credibility assumption and marginal interventions. The data are constructed with 755 daily spot exchange rates, denominated in Eurpean Currency Unit (ECU), from January 14, 1987 to December 29, 1989, in German, France, and Italy.We split the sample into in-sample (570 observations), and out-of-sample (185 observations), and make use of STARTZ-GARCH and LSTR2-STGARCH to fit the in-sample regimes, and apply Rapach and Wohard (2006)'s Bootstapping to generate the out-of-sample forecasts. Finally,we make use of Diebold and Mariano (1995)'s predictive accuracy tests to compare the out-of-sample forecastability between STARTZ and LSTR2 models.According to the empirical results, we can find that LSTR2 model has not bad performance in fitting the in-sample and forecasting the out-of-sample data compared to STARTZ model.

There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model.  The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation. / <p>Diss. Stockholm : Handelshögskolan, 2004</p>

The existing literature on current account¡¦s analysis of influencing factors-related issues had had extensive research achievements, but they mostly stressed the discussion of influencing factors from outside the economic body. To better understand and improve government¡¦s ability of controlling variations in current account balance in order to suit the macro economic situation through the use of nimble interest and exchange rate policies, we employ the analytical tool to examine factors that influence Taiwan's excess saving during the period from 1987Q3 to 2009Q1. We modify the current account share of GDP regression by including interest rate, exchange rate and quarterly rate of inflation to reflect the effect of current account dynamics. Empirical evidences indicate that the coefficients of the long-run relationship are significantly crucial. We apply both the linear error-correction model (ECM) and nonlinear smooth transition regression model to investigate the dynamics of current account vis-a-vis interest rate, exchange rate and the quarterly rate of inflation. and find supportings to the appropriateness of nonlinear smooth transition regression model. Furthermore, exchange rate was found having positive impact on current account balances. That is, a depreciation in the exchange rate would improve the current account and an appreciation, on the other hand, will worsen the current account. But the quarterly rate of inflation has a significantly negative effect, with an increase in the quarterly rate of inflation leading to a decrease in current account balances. Finally, from the long run cointegrating relationship, current account balances raises while the interest rate is decreasing, indicating there might exist income effect when interest rates fall. Because a fall in income reduces consumption, and increases saving, in turn, causes current account balances to rise, and vice versa. The empirical results shows all coefficients¡¦ sign can not only explain and interpret real economic phenomena, but also are consistent with theoretical expectations.

財政透明度為建立一個良好政府治理的基礎。近年來隨著中國大陸高速的經濟發展，中央政府相關單位亦注意到財政公開的重要性。然而，目前中國財政資訊仍處於不透明的狀態。另一方面，中國大陸貪腐現象無所不在，內部腐敗行為更是日益猖獗，因此如何打擊貪腐、提升中國地方政府的財政透明度，就成為迫在眉睫的問題。 因此，本文的研究目的主要探討中國貪腐程度對地方政府財政透明度的影響：第一，瞭解當今社會對於財政透明度的提倡與國際規範；第二，考量貪汙與財政透明度之間可能存在非線性關係，建構一個追蹤平滑轉換迴歸模型（Panel Smooth Transition Regression Model，PSTR），瞭解中國大陸財政資訊的公開情形是否因各地區貪腐程度的不同而有所差異；最後，對於中國大陸嚴重的貪腐與財政透明度的關聯做深入探討，以期能為中國大陸財政不透明與不重視情況提出政策建議。

[pt] Esta dissertação tem por objetivo comparar o desempenho de modelos não lineares de previsão de retornos em 10 ativos do mercado acionário brasileiro. Entre os modelos escolhidos, pode-se citar o STAR-Tree, que combina conceitos da metodologia STAR (Smooth Transition AutoRegression) e do algoritmo CART (Classification And Regression Trees), tendo como resultado final uma regressão com transição suave entre múltiplos regimes. A especificação do modelo é feita através de testes de hipótese do tipo Multiplicador de Lagrange que indicam o nó a ser dividido e a variável explicativa correspondente. A estimação dos parâmetros é feita pelo método de Mínimos Quadrados Não Lineares para determinar o valor dos parâmetros lineares e não lineares. Redes Neurais, modelos ARMAX (estes lineares) e ainda o método Naive também foram incluídos na análise. Os resultados das previsões foram avaliados a partir de medidas estatísticas e financeiras e se basearam em um negociador automático que informa o instante correto de assumir uma posição comprada ou vendida em cada ativo. Os melhores desempenhos foram alcançados pelas Redes Neurais, pelos modelos ARMAX e pela forma de previsão ARC (Adaptative Regime Combination) derivada da metodologia STAR-Tree, sendo ambos ainda superiores ao retorno das ações durante o período de teste / [en] The goal of this dissertation is to compare the performance of non linear models to forecast return on 10 equities in the Brazilian Stock Market. Among the chosen ones, it can be cited the STAR-Tree, which matches concepts from the STAR (Smooth Transition AutoRegression) methodology and the CART (Classification And Regression Trees) algorithm, having as the resultant structure a regression with smooth transition among multiple regimes. The model specification is done by Lagrange Multiplier hypothesis tests that indicate the node to be splitted and the corresponding explanatory variable. The parameter estimation is done by the Non Linear Least Squares method that determine the linear and non linear parameters. Neural Netwoks, ARMAX models (these ones linear) and the Naive method were also included in the analysis. The forecasting results were calculated using statistical and financial measures and were based on an automatic negociator that signaled the right instant to take a short or a long position in each stock. The best results were reached by the Neural Networks, ARMAX models and ARC (Adaptative Regime Combination ) forecasting method derived from STAR-Tree, with all of them performing better then the equity return during the test period.

[pt] O objetivo principal desta tese introduzir um modelo estruturado por árvores que combina aspectos de duas metodologias: CART (Classification and Regression Tree) e STR (Smooth Transition Regression). O modelo aqui denominado STR-Tree. A idéia especificar um modelo não-linear paramétrico através da estrutura de uma árvore de decisão binária. O modelo resultante pode ser analisado como uma regressão com transição suave entre múltiplos regimes. As decisões sobre as divisões dos nós são inteiramente baseadas em testes do tipo Multiplicadores de Lagrange. Uma especificação alternativa baseada em validação cruzada também utilizada. Um experimento de Monte Carlo utilizado para avaliar o desempenho da metodologia proposta comparando-a com outras técnicas comumente utilizadas. Como resultado verifica-se que o modelo STR- Tree supera o tradicional CART quando seleciona a arquitetura de árvores simuladas. Além do mais, utilizar testes do tipo Multiplicadores de Lagrange gera resultados melhores do que procedimentos de validação cruzada. Quando foram utilizadas bases de dados reais, o modelo STR-Tree demonstrou habilidade preditiva superior ao CART. Através de uma aplicação, extende-se a metodologia para a análise de séries temporais. Neste caso, o modelo denominado STAR- Tree, sendo obtido através de uma árvore de decisão binária que ajusta modelos autoregressivos de primeira ordem nos regimes. A série de retornos da taxa de câmbio Euro/Dólar foi modelada e a capacidade preditiva e o desempenho financeiro do modelo foi comparado com metodologias padrões como previsões ingênuas e modelos ARMA. Como resultado obtido um modelo parcimonioso que apresenta desempenho estatístico equivalente às estratégias convencionais, porém obtendo resultados financeiros superiores. / [en] He main goal of this Thesis is to introduce a tree- structured model that combines aspects from two methodologies: CART (Classification and Regression Trees) and STR (Smooth Transition Regression). The model is called STR-Tree, The idea is to specify a nonlinear parametric model through the structure of a binary decision tree. The resulting modelo can be analyzed as a smooth transition regression model with multiple regimes. The decisions for splitting the nodes of the tree are entirely based on Lagrange Multipliers tests. An alternative specification that uses cross- validation is also tried. A Monte Carlo Experiment is used to evaluate the performance of the proposed methodology and to compare with other techniques that are commonly used. The results showed that the STRTree model outperformed the traditional CART when specifying the architecture of a simulated tree. Moreover, the use of Lagrange Multipliers tests gave better results than a cross-validation procedure. After applying the model to real datasets, it could be seen that STR-Tree showed superior predictive ability when compared to CART. The idea was extended to time series analysis through an application. In this situation, we call the model as STAR- Tree which is obtained through a binary decision tree that fits first-order autoregressive models for different regimes. The model was fitted to the returns of Euro/Dolar exchange rate time series and then evaluated statistically and financially. Comparing with the naive approach and ARMA methodology, the STAR-Tree was parsimonious and presented statistical performance equivalent to others. The financial results were better than the others.

La question de la transmission internationale des cycles a reçu une attention considérable en raison de l’intensification de la globalisation économique et financière. La problématique générale de cette thèse s’inscrit dans le prolongement de la littérature consacrée à cette question. Plus précisément, elle focalise son attention sur l’analyse de la transmission du cycle des Etats-Unis sur les pays émergents. Elle comporte trois chapitres. Le premier, en se basant sur une nouvelle approche économétrique en termes de modèle Global VAR, s’attache à étudier l’effet des chocs provenant des Etats-Unis sur les pays émergents. Il confirme l’idée que les Etats-Unis jouent un rôle important dans la transmission des cycles économiques étant donné leur poids dans l’économie mondiale. Le second chapitre propose d’étudier la transmission financière des Etats-Unis en s’intéressant à la crise des subprimes sur ces pays. L’estimation du modèle switch à probabilité variée développée indique que la persistance des stress financiers, le durcissement des conditions du crédit et l’augmentation du risque de non-solvabilité bancaire ont été les causes fondamentales de la transmission financière. La volatilité de l’indice boursier américain a été le facteur clé de la contamination avec tous les pays étudiés. Le troisième chapitre est consacré à étudier si les pays émergents sont en mesure d’adopter des politiques budgétaires contracycliques pouvant atténuer les chocs provenant de l’extérieur. En utilisant le modèle à seuil avec transition lisse en panel (le modèle PSTR), ce chapitre confirme que la politique budgétaire dans les pays émergents est procyclique en période de ralentissement économique et aussi lorsque la dette publique dépasse le seuil critique. De ce fait, une solide position budgétaire est fondamentale pour assurer la stabilité macroéconomique. / The issue of international transmission cycles has considerably received attention due to the increasing economic and financial globalization. Our thesis is in line with the literature dedicated to this question. More specifically, we focusour attention on the analysis of the transmission cycle of the United States to emerging countries. It consists of three chapters. The first one, based on a new econometric approach in terms of Global VAR model, aims to study the effect of shocks from the U.S. to emerging countries. The main resultconfirms the idea that the United States plays an important role in the transmission of economic cycles given their weight in the world economy. The second chapter proposes to study the financial transmission of the United States by focusing on the subprime crisis on these countries. The estimation of time varyingtransitionprobability (TVTP) Markov switchingmodel indicates that the persistence of financial stress, the tightening of the conditions of the credit and the increase of the risk of Banking solvency constitute the major determinants of the financial transmission. The US stock market volatility is the key factor transmission channel for all the studied countries. The third chapter is devoted to investigate whether emerging countries are able to adopt countercyclical fiscal policies to mitigate the impact from outside. Using the threshold model with smooth transition panel ( the PSTR model ), this chapter confirms that fiscal policy in emerging countries is procyclicalin the slowdown periodand also when public debt exceeds the critical threshold. Therefore, a strong fiscal position is fundamental to ensure macroeconomic stability.