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 October 2000 the Initiative for the Integration of Regional Infrastructure in South America (IIRSA) was launched. The purpose of the IIRSA is to improve integration of the South American countries and intraregional trade between them. One of the ultimate goals is to promote sustainable growth. The purpose of this paper is to find out if a better quantity and quality of transport infrastructure increases intraregional trade in South America. It is found that the quantity of transport infrastructure increases intraregional trade. On the other hand, there is no evidence for the quality of transport infrastructure increasing intraregional trade in South America. Furthermore, this paper investigates whether economic growth can be obtained through more trade. In other words, this paper examines if trade causes growth. The results do not confirm the trade-growth causality for all countries. The difference between the existence of a trade-growth causal relationship or not could be explained by the core commodities that the different South American countries export.

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.

The dependence on oil has increased in many nations as a result of increasing industrialization and oil has been the factor of many crises as well as many wars. This paper examines how the price of oil affects the unemployment in Sweden. The case of Sweden is interesting since its politics are very different compared to other industrialized countries when it comes to unemployment and benefits. Our main objective is to see whether a change in the oil price will cause a change in unemployment at a later stage. We perform linear regression analysis relating current changes in the variables and Granger causality tests to conclude if there exists a direct relationship. The result we received from our linear regression test on current changes and our Granger causality test showed a relationship between the price of oil and unemployment in Sweden. In the linear regression relating current changes in these variables, a positive relationship was indicated. Due to the fact that some of the coefficient estimates are positive and some are negative in the Granger causality regressions, we can not conclude whether an increase in the price of oil will cause a positive or negative effect on unemployment.

Purpose – The aim of this study is to explore how outsourcing affects developing countries. The effects are examined for Ghana and Vietnam, which have recently become attractive outsourcing locations. They are compared with China and India, both well known for their outsourcing sectors and their attractiveness as outsourcing locations.   Design/methodology/approach – In this research paper an exploratory method is applied. During the examination economic data provided by supranational organizations is used to measure the effects of outsourcing. Data is collected to match the requirements of the applied triangular model for measuring. Background for the data collection is the triangular model by Granger. Key figures for observation are Gross Domestic Product (GDP), Foreign Direct Investment (FDI), exports, and their correlations. Validity and reliability is ensured through cross examination of the model.   Findings – The effects of outsourcing vary a lot. All key figures rose in general during the observed 30 years, from 1981 till 2010. The correlations show that there are eventual relations of the figures, although direct relations each by each year are not found. The most remarkable finding is that FDI might indeed push the exports. Export rates are rising in the years after the investment is done. The relation of FDI and GDP show that there can be interrelations as well, but if the GDP is increased in higher rates than the FDI is done. An overall result of the examination is that Vietnam seems to rely heavily on outsourcing as they export almost ¾ of the fabrications whereas it is assumed that Ghana tries more on development and improvement of the whole economy.   Originality/value – This research paper looks at the often discussed phenomenon outsourcing by focussing on its economic effects by focussing on the effects for the developing countries Vietnam and Ghana. It provides the reader with new aspects to be considered in the surrounding of outsourcing. Further investigations are necessary to explore if the found can be generalised.

This thesis examines the linkages between macroeconomic variables and the number of domestic Mergers &amp; Acquisitions (M&amp;A) in Sweden during 1998-2011 (in terms of changes). This study treats stationary times series data, from which multiple regression models are assembled. These models include gross domestic product, OMX Stockholm price index, lending rate, money supply, debt rate, consumer confidence, the unemployment rate and capacity utilization as explanatory variables. Aggregate number of M&amp;As is set to the dependent variable. The outcome was that gross domestic product, money supply, unemployment rate and stock prices can help explain fluctuations in M&amp;A activity during different time frames. However, the majority of the explanation for fluctuations in M&amp;A activity lies within factors beyond ourestimation model. Through a Granger-causality test, we establish if the significant variables can help to predict M&amp;A activity and vice versa. During different time periods gross domestic product and unemployment helps in predicting M&amp;A activity. M&amp;A activity also improves the prediction of gross domestic product in some time periods.

The purpose of this paper is to investigate if real stock returns are related to real GDP growth for the case of Sweden between 1980 and 2008. By using correlation tests, the paper presents evidence that there is almost no correlation between current real stock returns and current real GDP growth. On the other hand, Granger causality tests show that stock returns are related to future production growth for the period 1980-2008. Stock returns therefore indicate real economic activity in the next quarter. Between 1980 and 1992, there is no evidence of Granger causality from stock returns to GDP growth. However, stock returns Granger-cause production growth between 1993 and 2008, which suggests that the market has become better at predicting future economic activity. The paper also documents that GDP growth does not indicate future stock returns.

The aim of this thesis is to examine performance and relationship between the US and Asian REITs indices. We find two-year (2005/3/10~2007/3/12) return of T-REITs is 15.87%, which is much lower than the return of US, Japan and Singapore. However, T-REITs has the lowest risk in selected sample countries because the lowest VaRs is found. We estimate one-day horizon holding periods VaRs and find T-REITs¡¦ performance is better than other country by the Sharpe Ratio of VaRs. The Granger causality approach indicates some lead-lag relationships between these REITs. The NAREIT EQUITY index is leading the Hong-Kong and Singapore REITs indices; Singapore REITs index is leading the J-REITs index; J-REITs index is leading the NAREIT EQUITY index. However, Causality tests show no significant lead-lag relationships between Taiwan REITs market and other REITs markets.

<p>In October 2000 the Initiative for the Integration of Regional Infrastructure in South America (IIRSA) was launched. The purpose of the IIRSA is to improve integration of the South American countries and intraregional trade between them. One of the ultimate goals is to promote sustainable growth. The purpose of this paper is to find out if a better quantity and quality of transport infrastructure increases intraregional trade in South America. It is found that the quantity of transport infrastructure increases intraregional trade. On the other hand, there is no evidence for the quality of transport infrastructure increasing intraregional trade in South America. Furthermore, this paper investigates whether economic growth can be obtained through more trade. In other words, this paper examines if trade causes growth. The results do not confirm the trade-growth causality for all countries. The difference between the existence of a trade-growth causal relationship or not could be explained by the core commodities that the different South American countries export.</p>

<p>The purpose of this paper is to investigate if real stock returns are related to real GDP growth for the case of Sweden between 1980 and 2008. By using correlation tests, the paper presents evidence that there is almost no correlation between current real stock returns and current real GDP growth. On the other hand, Granger causality tests show that stock returns are related to future production growth for the period 1980-2008. Stock returns therefore indicate real economic activity in the next quarter. Between 1980 and 1992, there is no evidence of Granger causality from stock returns to GDP growth. However, stock returns Granger-cause production growth between 1993 and 2008, which suggests that the market has become better at predicting future economic activity. The paper also documents that GDP growth does not indicate future stock returns.</p>