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Diss. Stockholm : Handelshögsk.

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.

During the last few years nonlinear models have been a very active area of econometric research: new models have been introduced and existing ones generalized. To a large extent, these developments have concerned models in which the conditional moments are regime-dependent. In such models, the different regimes are usually linear and the change between them is governed by an observable or unobservable variable. These specifications can be useful in situations in which it is suspected that the behaviour of the dependent variable may vary between regimes. A classical example can be found the business cycle literature where it is argued that contractions in the economy are not only more violent but also short-lived than expansions. Unemployment, which tends to rise faster during recessions than decline during booms, constitutes another example. Two of the most popular regime-dependent models are the smooth transition and the threshold model. In both models cases the transition variable is observable but the specification of the way in which the model changes from one regime to the other is different. Particularly, in the smooth transition model the change is a continuous whereas in the threshold model it is abrupt. One of the factors that has influenced the development of nonlinear models are improvements in computer technology. They have not only permitted an introduction of more complex models but have also allowed the use of computer-intensive methods in hypothesis testing. This is particularly important in nonlinear models because there these methods have proved to be practical in testing statistical hypothesis such as linearity and parameter constancy. In general, these testing situation are not trivial and their solution often requires computer-intensive methods. In particular, bootstrapping and Monte Carlo testing are now commonly used. In this thesis the smooth transition model is used in different ways. In the first chapter, a vector smooth transition model is used as a device for deriving a test for parameter constancy in stationary vector autoregressive models. In the second chapter we introduce a panel model whose parameters can change in a smooth fashion between regimes as a function of an exogenous variable. The method is used to investigate whether financial constraints affect firms' \ investment decisions. The third chapter is concern with linearity testing in smooth transition models. New tests are introduced and Monte Carlo testing techniques are shown to be useful in achieving control over the size of the test. Finally, the last chapter is devoted to the Smooth Permanent Surge model. This is a nonlinear moving average model in which a shock can have transitory or permanent effects depending on its sign and magnitude. Test for linearity and random walk hypothesis are introduced. / Diss. Stockholm : Handelshögsk., 2004

<p>In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.</p>

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>

In this paper, we study the industrial production index of Sweden from Jan, 2000 to latest Feb, 2010. We find out there is a structural break at time point Dec, 2007, when the global financial crisis burst out first in U.S then spread to Europe. To model the industrial production index, one of the business cycle indicators which may behave nonlinear feature suggests utilizing a smooth transition autoregressive (STAR) model. Following the procedures given by Teräsvirta (1994), we carry out the linearity test against the STAR model, determine the delay parameter and choose between the LSTAR model and the ESTAR model. The results from the estimated model suggest the STAR model is better performing than the linear autoregressive model.

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.

With gradually increasing interdependence of international political and economic environments, part of Asian countries' financial markets reform adopted progressive policies towards liberalization and internationalization. Therefore, the integration of international financial markets has attracted a bunch of scholars to investigate related topics of international stock market. Granger and (1993) documented that most of the economic variables have nonlinear characters. Chelley-Steeley (2004) uses smooth transition regression model to explore the financial market integration of regional and global markets among emerging and developed countries. Smooth transition regression model considered the possibility of nonlinear changes in regression parameters. This paper applies the smooth transition regression model to reinvestigate Chelley-Steeley¡¦s (2004) study of nonlinear relationship of stock markets among some East Asian countries and the United States, Japan and Germany. The main difference of our model and Chelley-Steeley¡¦ model is that we relax his constant market index correlation between two countries by allowing the autoregressive process on market index correlation. Empirical evidences of linear model, original non-linear model and our non-linear extension model show that our non-linear extension model outperformedthe other two models in terms of goodness of fit.

Abstract Divident discount model found further expected dividend discounting to some fix period. The dividends are determined from the the core of company and relates retain earning. In Taiwan stock market, divedneds are not paid per season. So, I adept earning per share to proxy variable and employ market value weight to conduct dividends for Taiwan stock idnex. The next step, investgate the relationship between price index and diviednds using the econometric model was created by Kapetanios et al. (2006). Consequencely, the relationship are fitted discribtion by ESTR cointegration rather than linear cointegration.