This thesis assesses the usefulness of confidence indicators for short term forecasting of the economic activity in the Czech Republic. The predictive power of both the business confidence indicator and the customer confidence indicator is examined using two empirical approaches. First we predict the likelihood of economic downturn defined as a discrete event using logit models, later we estimate GDP growth out of sample forecasts in the framework of vector autoregression models. The results obtained from the downturn probability models confirm the ability of confidence indicators (especially the business confidence indicator) to estimate the current economic situation and to anticipate economic downturn one quarter ahead. Results from the out-of-sample GDP growth value forecasting are ambiguous. Nevertheless the customer confidence indicator significantly improved original forecasts based on a model with standard macroeconomic variables and therefore we conclude in favour of its predictive power. This result was indirectly confirmed by OECD as the Czech customer confidence indicator has been included as a new component in the OECD domestic composite leading indicator since April 2012.

This thesis investigates the role of the exchange rate and interest rate channels in the monetary transmission mechanism in Ukraine. The responses on the domes- tic as well as Russian economy shocks are estimated using the Vector Autoregression Model with block-exogeneity restriction. Monetary transmission did not prove to be strongly effective via neither of the estimated channels, although the exchange rate channel demonstrates the results which are more in line with the economic theory. In addition, the exchange rate channel shows the higher and more significant pass through. Further, we estimate the importance of the shocks of both home and for- eign economies for the domestic variables deviations using variance decomposition technique. The relevance of the Russian shocks in fluctuations of home variables is found out. The current estimation of the transmission mechanism is relevant due to the planned inflation targeting regime implementation in Ukraine which requires understanding of that processes in the economy. 1

Dynamische Zusammenhänge zwischen den internationalen Kapitelmärkten sind seit Anfang 90-er Jahre erforscht worden. Die meisten dieser Untersuchungen betrafen dieUSA und die anderen entwickelnden Märkte. Es gibt nur wenige Untersuchungen zu diesem Thema in den sich entwickelnden Märkten. Mit Hilfe von vektorautoregressiven(VAR) Modellen überprüft diese Arbeit den dynamischen Zusammenhang zwischen den Börsen der Region Pazifisches Becken vor und nach der Asiatischen Krise 1997.Unsere Studie zeigt, dass alle Börsen in der Region Asien-Pazifik mit den anderen Börsen statistisch zusammenhängen, mit Ausnahme von China. Nach der Asiatischen Krise 1997 wurden die Märkte noch stärker integriert. Die Asiatische Krise hatte einen weltweiten Einfluß auf alle Börsen der Region Pazifisches Becken: Die Verbindungen zwischen den Börsen nach der Krise sind stärker als vor der Krise. Die Märkte, die zueinander geographisch und ökonomisch nahe liegen, haben deutlich stärkere Wechselbeziehungen.Das Ergebnis zeigt, dass der USA-Markt nicht der einzige dominierende Markt in der Region ist. Die Studie stellt fest, dass die anderen entwickelten Märkte wie z.B.Neuseeland, Australien, Hongkong und Singapur, weitere vorherrschende Märkte neben USA sind. Ein Schock in einem Markt wird schnell zu den anderen Märkten übertragen. Schocks in den sich entwickelnden Märkten werden zu anderen Märkten schnell übertragen, aber ohne einen solchen großen Einfluß wie die aus den entwickelten Märkten. / International capital markets linkages have been studied since the early 90-es. Most of these studies have focused on the US and other developed markets. There are only few researches on this topic in the emerging markets. This paper examines the dynamic linkages between Pacific-Basin stock markets before and after the Asian Crisis 1997, using the vector autoregressive (VAR) approach. Our study shows that all Asia-Pacific stock markets are integrated with each other,except China. Following Asian Crisis 1997, the markets became more integrated. The Asian crisis had a global effect on all stock markets in Pacific-Basin region. The linkages between stock markets after the crisis are stronger than those before the crisis. Markets that are geographically and economically closer to each other tend to have a stronger correlation The result shows that the US market is not the only dominant market in the region. The study notes that the other developed markets such as New Zealand, Australia, Hongkong and Singapore are further comparatively dominant markets besides US market. A shock in one market is rapidly transmitted to other markets. Shocks in the emerging markets are also swiftly passed to other markets, but without having such a big effect compared to those in the developed markets.

In the last few decades, vector autoregressive (VAR) models have gained tremendous popularity as an all-purpose tool in econometrics and other disciplines. Some of their most prominent uses are for forecasting, causality tests, tests of economic theories, hypothesis-seeking, data characterisation, innovation accounting, policy analysis, and cointegration analysis. Their popularity appears to be attributable to their flexibility relative to other models rather than to their virtues per se. In addition, analysts often use VAR models as benchmark models. VAR modeling has not gone uncriticised, though. A list of relevant arguments against VAR modelling can be found in Section 2.3 of this thesis. There is one additional problem which is rarely mentioned though, namely the often heavily biased estimates in VAR models. Although methods to reduce this bias have been available for quite some time, it has probably not been done before, at least not in any systematic way. The present thesis attempts to systematically examine the performance of bias-reduced VAR estimates, using two existing and one newly derived approximation to the bias. The thesis is orginanised as follows. After a short introductory chapter, a brief history of VAR modelling can be found in Chapter 2 together with a review of different representations and a compilation of criticisms against VAR models. Chapter 3 reports the results of very extensive Monte Carlo experiments serving dual purposes: Firstly, the simulations will reveal whether or not bias really poses a serious problem, because if it turns out that biases appear only by exception or are mainly insignificant, there would be little need to reduce the bias. Secondly, the same data as in Chapter 3 will be used in Chapter 4 to evaluate the bias approximations, allowing for direct comparison between bias-reduced and original estimates. Though Monte Carlo methods have been (rightfully) criticised for being too specific to allow for any generalisation, there seems to be no good alternative to analyse small-sample properties of complicated estimators such as these. Chapter 4 is in a sense the core of the thesis, containing evaluations of three bias approximations. The performance of the bias approximations is evaluated chiefly using single regression equations and 3D surfaces. The only truly new research result in this thesis can also be found in Chapter 4; a second-order approximation to the bias of the parameter matrix in a VAR(p) model. Its performance is compared with the performance of two existing first-order approximations, and all three are used to construct bias-reduced estimators, which are then evaluated. Chapter 5 holds an application of US money supply and inflation in order to find out whether the results in Chapter 4 can have any real impacts. Unfortunately though, bias reduction appears not to make any difference in this particular case. Chapter 6 concludes. / Diss. Stockholm : Handelshögsk.

The purpose of this thesis is to identify a general model to forecast GDP growth for the Baltic States, Estonia, Latvia and Lithuania. If the model provides reliable results for these states, then the model should be able to forecast GDP growth for other countries of interest. Forecasts are made by using a reduced vector autoregressive (VAR) model. The VAR models make use of past values of Gross Domestic Product-Inflation-Unemployment as explanatory variables. The performed forecasts have provided good results for horizons up to t+8. The forecasts for 2009 (t+12) are in line with those of several other actors. It is reasonable to assume that some of the forecasts for t+16 have reliable results. The Lithuanian forecast show a fall in GDP with 12.51 per cent in 2009 and a GDP growth of 4.23 per cent in 2010. The forecast for Estonia show that the GDP will decrease with 1.49 per cent in 2009 and 12.72 per cent in 2010. Finally the forecast for Latvia show a fall in GDP of 3.1 per cent in 2009 and 18 per cent in 2010. From the findings it is possible to conclude that the model provided reliable estimates of future levels of GDP for the Baltic States and the benchmark countries. This indicates that the model should be applicable on other countries of interest.

In this study, time series analysis and factor analysis of multivariate analysis were applied to surface water quality monitoring data sets. In order to gain a better understanding on water pollution situations and sources of different river basin, we have done some statistical analysis according to the water quality monitoring data from the database of Environmental Protection Administration Executive Yuan in the years from 2004 to 2008. The statistical analysis is based on the aforementioned contents to find the common latent factors of the water pollution and try to explain the main sources of water pollution of river basins. Specifically the water quality monitoring data of the Tamsui River and the Kao-Ping River basin are analyzed. In seriously polluted areas, from the time series results they reveal that the water quality monitoring data has significant serial correlations. Therefore we firstly use vector autoregression (VAR) model to eliminate the dynamic dependencies of data, and later perform the factor analysis for the residuals. With this analysis procedure, the resulting models and factor variables from the factor analysis, sufficiently explained water quality variables with the potential relationship between environmental pollution in the region.

The Great Moderation means the reduction in the volatility of aggregate economic activity and here we use GDP growth rate to stand for economic activity. In this paper, we apply a Markov switching model to estimate the timing of the Great Moderation in France. Subsequently, by using a Time-varying structural vector autoregression model to determine which are the main variables that cause the reduction of French GDP growth rate and to see the relationship of these variables we choose.

Abstract Japan is the second largest economic power in the world. It has a great deal of FDI outflows but few FDI inflows. Therefore, Japan is in the serious situation of ¡§FDI balance of payments deficit.¡¨ In terms of inward FDI stocks as a percentage of GDP and gross fixed capital formation, Japan is the lowest place of G-7. The purpose of this research is focusing on discussing the shortage of FDI inflows and causing factors which lower the desires of investments in Japan by using the simplest way which is based on the actual situation and the limit of the information in Japan. This paper takes the quarterly data of Japan from 1978 to 2005 and four variables (wage index, real exchange rate, trade and FDI inflows). In this research, the unit root test is used to check if the data have the stationarity or not, and then it uses vector autoregression model (VAR) to proceed impulse response function and forecast error variance decomposition. According to the result of these two approaches, we can figure out the influences of four variables for each other, and then find out the causing factors which lead Japan to have less FDI inflows. The calculation shows that the reason which leads Japanese wages to increase gradually results not only from real exchange rate, trade and FDI inflows, but also from Japanese labor system (lifetime employment system and payment according to working seniority) and the labor quantities. The causality runs from real exchange rate to trade is greater than vice versa. Trade has a positive impact from the real exchange rate which means that the depreciation can accelerate trade. However, the main factor of hindering FDI inflows is Japanese high wages rather than real exchange rate or trade. Therefore, in order to get rid of the depression which was caused by the bubble economy in 1990s, Japanese government not only opens up the restrictions in policy but also takes the control of the prime costs into the most important consideration.

The purpose of this thesis is to identify the factors playing a key role in the determination of the Turkish stock market liquidity in aggregate terms in a time series context and discuss the joint dynamics of the market-wide liquidity with its selected determinants and the trade volume. The main determinants tested are the level of return, the return volatility and the monetary stance of the Central Bank of the Republic of Turkey. The expected positive relationship between the liquidity and the return is confirmed, while the negative effect of the volatility on liquidity appears one-week later. The behavior of various liquidity variables are also examined around the macroeconomic data announcement dates, during the 2008 financial crisis, and after the tick size change in the Istanbul Stock Exchange (ISE). The time series dynamics between the trade volume, return, volatility and the liquidity are put forward within the Vector Autoregression analysis framework. The GARCH modeling of the return series, which is an input to the liquidity model estimations, is a byproduct of this thesis. It is observed that the return series exhibits volatility clustering, persistence, leverage effects and mean reversion. In addition, while the level of the ISE market return decreased, the volatility of the return increased during the 2008 crisis. Accordingly, EGARCH model assuming normally distributed error terms and allowing a shift in the variance during the crisis period is chosen as the best model.

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