[pt] Desde o século XVI, já existia a idéia de que o poder de compra deveria influenciar no valor de cada moeda. A fim de se entender as relações entre câmbio e inflação, modelos autoregressivos lineares sempre apresentaram dificuldades para superar o passeio aleatório. Possíveis fricções em operações cambiais podem dificultar a arbitragem próxima do câmbio de equilíbrio considerado pelos agentes financeiros. À medida em que se distancia do valor considerado justo, a convergência se torna mais intensa, pois os custos já não seriam uma parcela tão relevante para o lucro potencial da operação. No modelo não-linear proposto, há dois regimes diferentes: um próximo do equilíbrio (comportamento de passeio aleatório) e um comportamento longe dele ocorrendo simultaneamente, mas com pesos variáveis. A depender do nível do câmbio em relação ao equilíbrio, um regime ganha mais peso e outro perde relevância. Essa tese tem o objetivo de avaliar o caráter preditivo do movimento cambiais. O modelo não-linear ESTAR é usado para montar cestas de moedas a serem compradas e vendidas e o retorno advindo de oscilações cambiais é computado. Por fim, incorporamos os efeitos de juros ao modelo para montar portfólios de moedas a fim de simular o retorno de um investimento usando essa estratégia. Para as cestas de moedas, o modelo gerou bons retornos e baixos riscos, tanto em termos de desvio padrão quanto em termos de drawdown. Tal característica foi observada no modelo in-sample e no out-of-sample o que indica um forte caráter preditivo. Levando em conta o efeito dos juros, os portfólios com menos moedas apresentaram retornos positivos, porém essa vantagem é perdida ao se aumentar a quantidade de moedas. / [en] Since the sixteenth century, there was already the idea that purchasing power should influence the value of each currency. In order to understand the relationship between exchange rate and inflation, linear autoregressive models always presented difficulties to beat the random walk. Possible frictions in foreign exchange operations may hinder arbitrage close to the equilibrium exchange rate considered by financial agents. As the exchange rate distances itself from the value considered fair, the convergence becomes more intense, because the costs would no longer be so relevant to the potential profit of the operation. In the proposed nonlinear model, there are two different regimes: one near equilibrium (random walk behavior) and one behavior away from it occurring simultaneously, but with variable weights. For different levels of the exchange rate relative to the equilibrium, one regime gains more weight and the other loses relevance. This thesis aims to evaluate the predictive nature of the exchange rate movement. The nonlinear model ESTAR is used to create baskets of currencies to be bought and sold and the aggregate return based on exchange rate movements is computed. Finally, we consider the interest rate effects on the model to set up currencies portfolios in order to simulate the return on an investment using this strategy. For the baskets of currencies, the model generated good returns and low risks, based on both standard deviation and drawdown. This characteristic was observed in the in-sample model and in the out-of-sample model, which indicates a strong predictive power. Considering the interest effect, portfolios with fewer currencies showed positive returns, but this advantage is lost by increasing the number of currencies.

This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in these area. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An Ftypetest for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail,and thereafter illustrated within two corresponding macroeconomic data sets.

This thesis consists of four manuscripts in the area of nonlinear time series econometrics on topics of testing, modeling and forecasting nonlinear common features. The aim of this thesis is to develop new econometric contributions for hypothesis testing and forecasting in thesearea. Both stationary and nonstationary time series are concerned. A definition of common features is proposed in an appropriate way to each class. Based on the definition, a vector nonlinear time series model with common features is set up for testing for common features. The proposed models are available for forecasting as well after being well specified. The first paper addresses a testing procedure on nonstationary time series. A class of nonlinear cointegration, smooth-transition (ST) cointegration, is examined. The ST cointegration nests the previously developed linear and threshold cointegration. An F-type test for examining the ST cointegration is derived when stationary transition variables are imposed rather than nonstationary variables. Later ones drive the test standard, while the former ones make the test nonstandard. This has important implications for empirical work. It is crucial to distinguish between the cases with stationary and nonstationary transition variables so that the correct test can be used. The second and the fourth papers develop testing approaches for stationary time series. In particular, the vector ST autoregressive (VSTAR) model is extended to allow for common nonlinear features (CNFs). These two papers propose a modeling procedure and derive tests for the presence of CNFs. Including model specification using the testing contributions above, the third paper considers forecasting with vector nonlinear time series models and extends the procedures available for univariate nonlinear models. The VSTAR model with CNFs and the ST cointegration model in the previous papers are exemplified in detail, and thereafter illustrated within two corresponding macroeconomic data sets.

A literatura é extensa no que se refere a estimações de modelos de oferta e demanda para exportações, mas poucos consideram que a resposta em exportações a variações na taxa de câmbio possa ser lenta e assimétrica. Dixit (1989) afirma que a firma que deseja passar a atuar no mercado externo ou deixar tal mercado deve incorrer em custos irrecuperáveis. Além disso, políticas de wait and see fazem com que estas não mudem seu estado (atuantes ou não) imediatamente quando variações significativas na taxa de câmbio acontecem. Tais fatores criam o fenômeno da hysteresis econômica, caracterizado pela forte não-linearidade de uma variável, gerando assimetria dependendo do estado e da magnitude do choque em tal. Assim, propõe-se neste trabalho uma forma alternativa de se captar tal efeito, via modelo de cointegração com transição suavizada, desenvolvido em Saikkonen e Choi (2004). Os resultados encontrados apontam para a evidência do efeito histerético, apresentando tal modelagem não-linear para quatro dos dezesseis setores industriais estudados do Brasil. / The literature is large on what refers to estimation of export supply and demand models, but just a few consider that the response on exports to variations on the exchange rate can be slow and asymmetric. Dixit (1989) says that the firm who wishes to operate on the foreign market or leave it must incur on sunk costs. Besides, wait-and-see policies makes the firm to remain it state unaltered (operating or not) immediately when significant variations on the exchange rate happens. This factor creates the phenomena called economic hysteresis, representing a strong non-linearity of a variable, generating asymmetries depending on the state and magnitude of the shock on the variable. Thus, its proposed on this work an alternative form to capture this effect, by smooth transition cointegration model, developed on Saikkonen and Choi (2004). The results indicate to the evidence of the hysteretic effect, presenting non-linear modeling for four of sixteen industrial sectors studied of Brazil.

Housing prices are of crucial importance in financial stability management. The severe financial crises that originated in the housing market in the US and subsequently spread throughout the world highlighted the crucial role that the housing market plays in preserving financial stability. After the severe housing market crash, many financial institutions in the US suffered from high default rates, severe liquidity shortages, and even bankruptcy. Against this background, researchers have sought to use econometric models to capture and forecast prices of homes. Available empirical research indicates that nonlinear models may be suitable for modelling price cycles. Accordingly, this thesis focuses primarily on using nonlinear models to empirically investigate cyclical patterns in housing prices. More specifically, the content of this thesis can be summarised in three essays which complement the existing literature on price modelling by using nonlinear models. The first essay contributes to the literature by testing the ability of regime switching models to capture and forecast house prices. The second essay examines the impact of banking factors on house price fluctuations. To account for house price characteristics, the regime switching model and generalised autoregressive conditionally heteroscedastic (GARCH) in-mean model have been used. The final essay investigates the effect of structural breaks on the unit root test and shows that a time-varying GARCH in-mean model can be used to estimate the housing price cycle in the UK.

The thesis contains the four chapters: Testing parameter constancy in unit root autoregressive models against continuous change; Dickey-Fuller type of tests against nonlinear dynamic models; Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed; Testing unit roots in nonlinear dynamic heterogeneous panels. In Chapter  1 we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots . The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory. In Chapter 2 we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power. In Chapter 3 we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test. In Chapter 4 we present a unit root test against a non-linear dynamic heterogeneous panel with each country modelled as an LSTAR model. All parameters are viewed as country specific. We allow for serially correlated residuals over time and heterogeneous variance among countries. The test is derived under three special cases: (i) the number of countries and observations over time are fixed, (ii) observations over time are fixed and the number of countries tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of countries. Small sample properties of the test  show modest size distortions and satisfactory power being superior to the Im, Pesaran and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for non-linearities under the alternative hypothesis. / Diss. Stockholm : Handelshögskolan, 2004

This dissertation includes two chapters in the field of international finances about foreign exchange rate predictability and testing purchase power parity. In each chapter, we build the theory, methodology, and the empirical results to present the paper¡¦s construction. The first chapter, we studies whether the pure price inflation rate which is extracted from stock return can help us to test the relative of purchasing power parity in where Asian countries include Malaysia, Korea, Taiwan, Thailand, Hong Kong, and Singapore against the United States. The paper of Chowdhry et al. (2005) argue that relative PPP may not hold for the official price inflation rates which is constructed from consumer price indices, since relative price changes and other frictions cause price to be sticky. Thus, they use the Fisher equation and Fama-French three factors elaborately to build up a model on the nominal return of real risk-free asset to extract the pure price inflation rates. Their argument is supported in the case of Japan, Germany, the United States, and the United Kingdom. We are interested in the case of some Asian countries. So, this chapter, we extend the model and methodology of Chowdhry et al. (2005) to test the relative PPP for Asian countries. If our empirical evidence is firmly supported, it will be a strongly reconfirmed the elaborated idea of Chowdhry et al. (2005). In our study, the PPP rule is not supported for Asian countries since joint null hypothesis of a=0 and b=1 are rejected at all horizons except Taiwan at monthly horizon. The testing results by constrained seeming unrelated regression (SUR) and system equation in pooled data are similar to the tests of country-by-country. Therefore, we apply the methods of panel unite root from Im et al. (2003), Maddala and Wu (1999), and Pesaran (2007) to test the PPP doctrine, and it is strongly supported PPP for Asian countries. The second chapter, we extract the estimated data of pure price inflation by Chowdhry et al. (2005), and use the data to build up a nonlinear STR (smooth transition autoregressive) model by Granger and Teräsvirta (1993), then compare the performance of linear or nonlinear model of exchange rate predictability with random walk model in the United States, the United Kingdom, Japan, and Germany. This study has presented evidences that the extracted inflation rates offer a good predictability on the prediction of exchange rate for the United Kingdom and Germany. Those extracted data in which are calculated from the industry portfolio returns of stock market. The issue of series correlation in regression error does matter the estimated coefficients £]k, thus we estimate the simulation of Gaussian bootstrap distribution for testing variables with Newey West standard deviation in regression estimate. The empirical evidences show that the PPP doctrine affects the predictability performance of exchange rate change by the extracted inflation rates.

¡@¡@The purpose of this research is to employ the STAR model in discussing and analyzing the relationship between stock index and macroeconomic variables in Taiwan, Japan and Korea. ¡@¡@Monthly stock market index data is analyzed over the period January 1990 to December 2000, with the sample period from January 2001 to April 2005 being used in an out-of -sample forecasting exercise. The macroeconomic variables considered in this paper include money supply, consumer price index, industrial production index, interest rate and exchange rate. ¡@¡@The empirical results of Taiwan, Japan and Korea show that LSTAR & ESTAR model improve both the in-sample fit and out-of-sample forecast of the data over both the linear model alternative.

The purpose of this thesis is to explore the possibilities and advantages of describing macroeconomic relationships with a certain well-defined class of parametric nonlinear econometric models, called smooth transition regressions (STR). An STR model is a flexible nonlinear specification with a continuum of regimes. It is locally linear transitions from one extreme regime to another are determined by a function of a continuous variable, the transition variable.The thesis consists of four essays and the macroeconomic relationships that are considered are: Consumption, Money Demand and the Phillips Curve. The essays of this dissertation emphasise the importance of allowing for a flexible functional form when dealing with macroeconomic relationships. / Diss. Stockholm : Handelshögsk.