Testing Structural Changes Using Ratio Type Statistics Barbora Peštová Charles University in Prague, Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics, Czech Republic Abstract of the doctoral thesis We deal with sequences of observations that are naturally ordered in time and assume various underlying stochastic models. These models are parametric and some of the parameters are possibly subject to change at some unknown time point. The main goal of this thesis is to test whether such an unknown change has occurred or not. The core of the change point methods presented here is in ratio type statistics based on maxima of cumulative sums. Firstly, an overview of thesis' starting points is given. Then we focus on methods for detecting a gradual change in mean. Consequently, procedures for detection of an abrupt change in mean are generalized by considering a score function. We explore the possibility of applying the bootstrap methods for obtaining critical values, while disturbances of the change point model are considered as weakly dependent. Procedures for detection of changes in parameters of linear regression models are shown as well and a permutation version of the test is derived. Then, a related problem of testing a change in autoregression parameter is studied....

The contribution of this thesis is to build a small macro-econometric model of the Namibian economy, which demonstrates that there is significant statistical support for the hypothesis that there is a contemporaneous relationship between real wage, productivity, unemployment and interest rates in Namibia. This phenomenon has not yet been exploited using macro-econometric modelling, and thus, represents a significant contribution to modelling literature in Namibia. The determination of the sources of unemployment also receives special attention given that high unemployment is a chronic problem in Namibia. All models specified and estimated in the study use the SVAR methodology for the period 1980 to 2013. The study develops a small macro-econometric model using three modular experiments, which include, a basic model, models that separately append demand and exchange rate channels variables to the basic model, and the specification of a small macro-econometric model. The ultimate aim is to find out if monetary policy plays a role in influencing labour market and nominal variables. The hypothesis that the basic real wage, productivity, unemployment rate and interest rate system can be estimated simultaneously is validated. Further, demand and exchange rate channels variables are found to have important additional information, which explains the monetary transmission process, and that shocks to labour market variables affect monetary policy in Namibia. The results also show that the demand channel (import prices and bank credit to the private sector) and the exchange rate channel (nominal exchange rate) variables have important additional information, which affects monetary transmission process in Namibia, which justifies their inclusion in the small macro-econometric model. In addition, shocks to the import price and exchange rate in the macro-econometric model significantly affect labour market variables. However, shocks to bank credit only partially perform as expected, implying that its results need to be considered cautiously. The study further finds that tight monetary policy shocks significantly affect real and nominal variables in Namibia. The results also show that shocks to all variables in the unemployment model significantly affect unemployment, suggesting that the hysteresis assumption is corroborated. This implies that long run aggregate demand is non-neutral in Namibia. / Economics / D. Litt. et Phil. (Economics)

This thesis is composed of three chapters that propose some novel approaches on tail risk for financial market and forecasting in finance and macroeconomics. The first part of this dissertation focuses on financial market correlations and introduces a simple measure of tail correlation, TailCoR, while the second contribution addresses the issue of identification of non- normal structural shocks in Vector Autoregression which is common on finance. The third part belongs to the vast literature on predictions of economic growth; the problem is tackled using a Bayesian Dynamic Factor model to predict Norwegian GDP.Chapter I: TailCoRThe first chapter introduces a simple measure of tail correlation, TailCoR, which disentangles linear and non linear correlation. The aim is to capture all features of financial market co- movement when extreme events (i.e. financial crises) occur. Indeed, tail correlations may arise because asset prices are either linearly correlated (i.e. the Pearson correlations are different from zero) or non-linearly correlated, meaning that asset prices are dependent at the tail of the distribution.Since it is based on quantiles, TailCoR has three main advantages: i) it is not based on asymptotic arguments, ii) it is very general as it applies with no specific distributional assumption, and iii) it is simple to use. We show that TailCoR also disentangles easily between linear and non-linear correlations. The measure has been successfully tested on simulated data. Several extensions, useful for practitioners, are presented like downside and upside tail correlations.In our empirical analysis, we apply this measure to eight major US banks for the period 2003-2012. For comparison purposes, we compute the upper and lower exceedance correlations and the parametric and non-parametric tail dependence coefficients. On the overall sample, results show that both the linear and non-linear contributions are relevant. The results suggest that co-movement increases during the financial crisis because of both the linear and non- linear correlations. Furthermore, the increase of TailCoR at the end of 2012 is mostly driven by the non-linearity, reflecting the risks of tail events and their spillovers associated with the European sovereign debt crisis. Chapter II: On the identification of non-normal shocks in structural VARThe second chapter deals with the structural interpretation of the VAR using the statistical properties of the innovation terms. In general, financial markets are characterized by non- normal shocks. Under non-Gaussianity, we introduce a methodology based on the reduction of tail dependency to identify the non-normal structural shocks.Borrowing from statistics, the methodology can be summarized in two main steps: i) decor- relate the estimated residuals and ii) the uncorrelated residuals are rotated in order to get a vector of independent shocks using a tail dependency matrix. We do not label the shocks a priori, but post-estimate on the basis of economic judgement.Furthermore, we show how our approach allows to identify all the shocks using a Monte Carlo study. In some cases, the method can turn out to be more significant when the amount of tail events are relevant. Therefore, the frequency of the series and the degree of non-normality are relevant to achieve accurate identification.Finally, we apply our method to two different VAR, all estimated on US data: i) a monthly trivariate model which studies the effects of oil market shocks, and finally ii) a VAR that focuses on the interaction between monetary policy and the stock market. In the first case, we validate the results obtained in the economic literature. In the second case, we cannot confirm the validity of an identification scheme based on combination of short and long run restrictions which is used in part of the empirical literature.Chapter III :Nowcasting NorwayThe third chapter consists in predictions of Norwegian Mainland GDP. Policy institutions have to decide to set their policies without knowledge of the current economic conditions. We estimate a Bayesian dynamic factor model (BDFM) on a panel of macroeconomic variables (all followed by market operators) from 1990 until 2011.First, the BDFM is an extension to the Bayesian framework of the dynamic factor model (DFM). The difference is that, compared with a DFM, there is more dynamics in the BDFM introduced in order to accommodate the dynamic heterogeneity of different variables. How- ever, in order to introduce more dynamics, the BDFM requires to estimate a large number of parameters, which can easily lead to volatile predictions due to estimation uncertainty. This is why the model is estimated with Bayesian methods, which, by shrinking the factor model toward a simple naive prior model, are able to limit estimation uncertainty.The second aspect is the use of a small dataset. A common feature of the literature on DFM is the use of large datasets. However, there is a literature that has shown how, for the purpose of forecasting, DFMs can be estimated on a small number of appropriately selected variables.Finally, through a pseudo real-time exercise, we show that the BDFM performs well both in terms of point forecast, and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg Survey, and that it outperforms the predictions published by the Norges Bank in its monetary policy report. / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished

The diploma thesis comprises of a general approach used to predict the time series, their categorization, basic characteristics and basic statistical methods for their prediction. Neural networks are also mentioned and their categorization with regards to the suitability for prediction of time series. A program for the prediction of the progress of multiple time series in stock market is designed and implemented, and it's based on a model of flexible neuron tree, whose structure is optimized using immune programming and parameters using a modified version of simulated annealing or particle swarm optimization. Firstly, the program is tested on its ability to predict simple time series and then on its ability to predict multiple time series.

The financial crisis of 2007/8 has triggered a profound debate about public budget finance sustainability, ever-increasing government expenditures and the efficiency of fiscal policy measures. Given this context, the following dissertation provides four contributions that analyze the long-run growth of government spending throughout economic development, discuss potential effects of fiscal policy measures on output, and provide new insights into the assessment of debt sustainability for a variety of industrialized countries. Since the breakout of the European debt crisis in 2009/2010, there has been a revival of interest in the long-term growth of government expenditures. In this context, the relationship between the size of the public sector and economic growth - often referred to as Wagner's law - has been in the focus of numerous studies, especially with regard to public policy and fiscal sustainability. Using historical data from the mid-19th century, the first chapter analyzes the validity of Wagner's law for five industrialized European countries and links the discussion to different stages of economic development. In line with Wagner's hypothesis, our findings show that the relationship between public spending and economic growth has weakened at an advanced stage of development. Furthermore, all countries under review support the notion that Wagner's law may have lost its economic relevance in recent decades. As a consequence of the 2007/8 financial crisis, there has been an increasing theoretical and empirical debate about the impact of fiscal policy measures on output. Accordingly, the Structural Vector Autoregression (SVAR) approach to estimating the fiscal multipliers developed by Blanchard and Perotti (2002) has been applied widely in the literature in recent years. In the second chapter, we point out that the fiscal multipliers derived from this approach include the predicted future path of the policy instruments as well as their dynamic interaction. We analyze a data set from the US and document that these interactions are economically and statistically significant. In a counterfactual simulation, we report fiscal multipliers that abstract from these dynamic responses. Furthermore, we use our estimates to analyze the recent fiscal stimulus of the American Recovery and Reinvestment Act (ARRA). The third chapter contributes to the existing empirical literature on fiscal multipliers by applying a five-variable SVAR approach to a uniform data set for Belgium, France, Germany, and the United Kingdom. Besides studying the effects of expenditure and tax increases on output, we additionally analyze their dynamic effects on inflation and interest rates as well as the dynamic interaction of both policy instruments. By conducting counterfactual simulations, which abstract from the dynamic response of key macroeconomic variables to the initial fiscal shocks, we study the importance of these channels for the transmission of fiscal policy on output. Overall, the results demonstrate that the effects of fiscal shocks are limited and rather different across countries. Further, it is shown that the inflation and interest rate channel are insignificant for the transmission of fiscal policy. In the field of public finances, governmental budgetary policies are among the most controversial and disputed areas of political and scientific controversy. The sustainability of public debt is often analyzed by testing stationarity conditions of government's budget deficits. The fourth chapter shows that this test can be implemented more effectively by means of an asymmetric unit root test. We argue that this approach increases the power of the test and reduces the likelihood of drawing false inferences. We illustrate this in an application to 14 countries of the European Monetary Union as well as in a Monte Carlo simulation. Distinguishing between positive and negative changes in deficits, we find consistency with the intertemporal budget constraint for more countries, i.e. lower persistence of positive changes in some countries, compared to the earlier literature.

Corruption is a social evil that resonates far and deep in societies, eroding trust in governance, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. It is a multidimensional and complex societal malady that occurs in various forms and contexts. As such, any effort to combat corruption must be accompanied by a thorough examination of the attributes that might play a key role in exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that influence corruption, using machine learning techniques, neural network analysis, and time series causal relationship analysis and aggregated data from 113 countries from 2007 to 2017. The results suggest that improvements in technological readiness, human development index, and e-governance index have the most profound impacts on corruption reduction. This dissertation discusses corruption at each phase of infrastructure systems development and engineering ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical efficiency measurement methods to measure infrastructure inefficiencies, and to rank infrastructure administrative jurisdictions at the state level. An efficiency frontier is developed using optimization and the highest performing jurisdictions are identified. The dissertation’s framework could serve as a starting point for governmental and non-governmental oversight agencies to study forms and contexts of corruption and inefficiencies, and to propose influential methods for reducing the instances. Moreover, the framework can help oversight agencies to promote the overall accountability of infrastructure agencies by establishing a clearer connection between infrastructure investment and performance, and by carrying out comparative assessments of infrastructure performance across the jurisdictions under their oversight or supervision.

台灣不動產投資信託（T-REITs）自2005年發行至今已逾六年，然其市場表現仍不如發行之初所預期。過去國內已有許多研究針對T-REITs市場發展進行探討，然而目前就T-REITs與直接不動產投資市場價格表現間之相關研究尚付之闕如。有鑑於此，本研究藉由共整合與Granger因果關係檢定，檢視REITs與直接不動產市場間之關聯性，了解台灣與美國之REITs市場表現差異及其影響因素，進而作為改進T-REITs運作機制或架構之參考依據。 實證結果發現，美國之REITs與直接不動產市場之間存在共整合關係。此結果表示，長期而言，這兩者可能具有相似之風險分散效益。此外，透過Granger因果關係檢定發現REITs領先於直接不動產，乃因前者市場較具效率。另一方面，台灣之REITs與直接不動產市場之間則不具有共整合以及領先或落後關係，然直接不動產當期價格仍會受到本身與REITs之前期價格影響。 本研究進一步分析台、美兩國實證結果之差異原因如下：資料的樣本期間、REITs市場規模、存在於T-REITs市場之集中性風險以及潛在的代理問題。其中，針對T-REITs潛在代理問題，本研究藉由分析股票與T-REIT報酬率之波動性，發現T-REIT之不動產管理機構若與母集團相關者，則其市場表現較差。因此，我們得出T-REITs市場發展主要是受限於代理問題之結論。本研究成果不僅有助於改善T-REITs市場效率，亦可提供學術與實務之參考。 / The mechanism of Real Estate Investment Trusts in Taiwan (or T-REITs) was launched in 2005, however, T-REITs market did not perform as expected. What caused the limited development of T-REITs market? Current literature on the performance between T-REITs and direct real estate investment is limited. Through the cointegration and Granger causality tests, the purpose of this study is hence to explore the short-term and long-term dynamics between REITs and direct real estate markets in the U.S. and Taiwan, respectively. This study presents evidence of the cointegration relationship between REITs and direct real estate in the U.S. It implies that the diversification properties of these two assets are likely to be similar over the long horizon. According to the Granger causality test, REITs leads direct real estate due to the market information efficiency. These findings are consistent with those of previous studies. On the other hand, we find no cointegration and lead-lag relation between T-REITs and commercial real estate. Moreover, the current commercial transaction price is affected by both its and T-REIT previous price. By comparing the difference between the results of these two countries, there are several possible explanations for the different results between the U.S. and Taiwan, including difference in sample period, market capitalization, concentrated risk, and most importantly, the potential agency problem existing in T-REITs market. Finally, the underperformance of parent-related management T-REIT is verified through the volatilities of stock and T-REIT returns. Therefore, we conclude that the limited development of T-REITs is caused by the agency problem in REITs market. Results of this study may provide T-REITs market for improving its efficiency, as well as for the reference for both academics and real practices.