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The purpose of this research is to investigate the power of various often-used cointegration tests against the fractionally cointegrated alternaive from Monte Carlo simulation. According to the simulation results, the Saikkonen¡¦s Infinite VAR model is most adequate cointegration test.

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In the past few decades the theory of cointegration has been widely used in the empirical analysis of economic data. The reason is that, it captures the economic notion of a long-run economic relation. One of the problems experienced when applying cointegrated techniques to econometric modelling is the determination of lag lengths for the modelled variables. Applied studies have resulted in contradictory choices for lag length selection. This study reviews and compares some of the well-known information criteria using simulation techniques for bivariate models.

This thesis, which consists of four essays, focus on seasonal and periodic cointegration models. These models are tools to describe changing seasonality.Essay 1 "Forecasting performance of seasonal cointegration models", with Johan Lyhagen. Forecasts from two different seasonal cointegration specifications are compared in an empirical forecasting example and in a Monte Carlo study. One of the two specifications include a certain parameter restriction at the annual frequency, wheras the other specification is more general. In the empirical forecasting example we also include a standard cointegration model based on first differences and seasonal dummies and analyze the effects of restricting or not restricting seasonal dummies in the seasonal cointegration models. While the Monte Carlo results favor the general specification, and definitely so if larger sample sizes are considered, we do not find such clear cut evidence in the empirical example.Essay 2 "On forecasting cointegrated seasonal time series", with Philip Hans Franses. In this essay we analyze periodic and seasonal cointegration models for bivariate quarterly observed time series in an empirical forecasting study. We include both single equation and multiple equations methods for those two classes of models. A VAR model in first differences, with and without cointegration restrictions, and a VAR model in annual differences are also included in the analysis, where they serve as benchmark models. Our empirical results indicate that the VAR model in first differences without cointegration is best if one-step ahead forecasts are considered. For longer forecast horizons however, the VAR model in annual differences is better. When comparing periodic versus seasonal cointegration models, we find that the seasonal cointegration models tend to yield better forecasts. Essay 3 "Size and power of the likelihood ratio test for seasonal cointegration in small samples: A Monte Carlo study", This essay investigates the small sample size and power properties of the likelihood ratio test in the seasonal error correction model. Two specifications of the model at the annual frequency are analyzed. One is more restricted (RS), designed for the particular case of 'synchronous cointegration', whereas the other specification is general (GS). The results indicate that RS has poor size properties in cases where non-synchronous cointegration clearly should play a role. There is a risk of finding 'evidence' of too many cointegrating vectors at the annual frequency when using RS. On the other hand, if the restriction is almost satisfied, the general specification looses power at least for small sample sizes, while tests in RS have good properties. Essay 4 "On seasonal error correction when the processes include different numbers of unit roots", with Johan Lyhagen. We propose a seasonal error correction model (SECM) for quarterly data which includes variables with different numbers of unit roots and thus needs to be transformed in different ways in order to yield stationarity. A Monte Carlo simulation is carried out to investigate the consequences of specifying a SECM with all variables in annual diffrerences in this situation. The SECM in annual differences is compared to the correctly specified model. Pre-testing for unit roots using two different approaches, and where the models are specified according to the unit root test results, is also considered. The results indicate that, in practice, a cointegration model where all variables are transformed with the annual difference filter is more robust than one obtained by pre-testing for a smaller number of unit roots. / Diss. Stockholm : Handelshögsk., 2001 [4], iv s., s. 1-23: sammanfattning, s. 25-110, [5] s.: 4 uppsatser

"It is the part of a wise man to keep himself today for tomorrow, and not venture all his eggs in one basket."                     - Don Quixote (Part I, Book III, Chapter 9) by Miguel de Cervantes Saavedra [1547-1616]     This research aimed to investigate whether it is possible for investors to diversify their investment and reduce the risk of investment by investing in the selected European countries.  Stock market cointegration and international diversification is a widely accepted topic among the scholars and academics in recent years.  This current study is motivated from the significant amount of interesting studies in this field. A combination of not perfectly positively correlated instruments gives the investor an opportunity to gain from portfolio diversification.  Similarly, Investors can attain diversification benefit if one country’s stock market is not cointegrated with other country’s stock market.  Six European countries and a time frame of ten years (January, 2001 to December, 2010) have been taken into consideration for the purpose of this research.  The countries are UK, Denmark, Germany, Spain, Poland, and Czech Republic.  The time period of the study is divided into two sub period to observe the recent crisis effect on these selected countries. A quantitative approach is adopted in the research.  We used an econometric model for this research which is Johansen and Juselius multivariate cointegration approach.  The evidence from the study suggest that although cointegration exists among the selected countries in some extent, investors can still get some diversification opportunity by investing in the emerging countries (Czech Republic and Poland).  This study is unique in the sense that in our research, we wanted to fill the research gap by combining new and old EU member countries with the latest time period of study and also considered the recent crisis effect.   This study has a number of implications on portfolio managers, policy makers, and academic scholars.

This dissertation includes two different empirical models about the economic growth and globalization in developed and developing countries from 1970 to 2008. First, we apply the quantile cointegration model provided by Xiao (2009) to examine the non-linear relationship between economic growth and globalization. Our empirical findings provide not only strong evidence that the cointegrating coefficients follow the time-varying process, but also the viewpoint of a long-run approach that overall globalization and their three dimensions act as engines of economic growth. Second, we adopt an advanced panel cointegration method which incorporates multiple structural breaks to examine the growth-globalization relationship. Differing from the weak outcomes of the traditional panel ointegration test without breaks, our findings provide strong evidence that overall globalization and its social dimension are cointegrated with RGDP both in developed and developing samples, and most of the structural break points are discovered in several main events. In addition, in evaluating whether or not the structural breaks affect the RGDP through globalization, we discover that the globalization have a directly positive impact on RGDP but indirectly exhibit negative (positive) impacts on real output via the channel of globalization in developed (developing) samples. Also, Different countries/groups reflect the different outcomes from the common shock of break event under the process of globalization. For the entire performance, the overall globalization brings the most positive effect on the real output in developed samples, and the social globalization is the main factor of promoting the economic development in developing samples.

In the system of Taiwan, if the demand function is given, then the Central Bank can improve economic growth and steady price by controlling the money supply. In fact, true money demand is unknown, so focal point of my paper is to estimate trading money demand function of Taiwan. First, I get that real income, real M1B, and nominal rate are integrated of order 1 processes by using Augmented Dickey-Fuller test (ADF test) , Phillips-Perron test (PP test) , and Ng-Perron (NP test) . In the conventional model of Engle and Granger (1987) , I use Johansen¡¦s (1988, 1991) maximun likelihood method to estimate co-integrating vector. The result is the same with Ching-Nun Lee (1996) . In the conventional model of Engle and Granger, a linear combination of individually I(1) series becomes I(0). Series have cointegration, but their linear combination is not I(0). Therefore the conventional model of Engle and Granger does not encompass all non-stational economic models. Harris, McCabe, and Leybourne (2002) provided the stochastic cointegration. The stochastic cointegration allows that a linear combination of individually I(1) series is not I(0). Therefore, my paper also uses stochastic cointegration to test trading money demand of Taiwan. The result is real M1B, real income, and one month rate have stochastic cointegration.