To resolve the inflation caused by overheated economy in 1992, China executed Macroscopic Control Policy to stabilize the fluctuation of price standard in 1993. It seemed to achieve the effort of controlling inflation. However, because of longtime Macroscopic Control Policy after Asian Financial Crisis, it resulted in negative impacts. CPI in China has been minus quantity for 39 months from October in 1997 to December in 2000. And CPI turned plus into minus from April in 1998 to January in 2000. And CPI turned plus into minus from April in 1998 to January in 2000. The growth rate of RPI is -2.6% and that of CPI is -0.8% in 1998. It declined to -3.0%(RPI) and -1.4%(CPI) in 1999. The growth rate of GDP has fallen down since 1992. The main purpose of this paper is to explore the reason of the deflation late in 1990 in China. I want to find out why deflation was happened in china? What is the main cause of deflation in china? What are the impacts and shocks to china economic growth by these causes? How are the impacts and shocks to china economic growth by these causes? The whole supply and demand and money contraction resulted in the downfall of GDP and CPI. To prevent the phenomena of overheated economy since 1993, most of investment moved away China because of Macroscopic Control Policy. Under this kind of situation, we couldn¡¦t say that the investments were excess. Therefore, the main reason isn¡¦t prices dropping caused by too much supply. China continued Deflation Policy after Asian Financial Crisis in 1997, so the speed of economy development decreased slowly. It also reduced the whole consumption, public spending, investment export, and money supply and demand. The effect of negative development resulted in the deflation of economy development.

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.

Gene expression is tightly regulated by complex transcriptional regulatory mechanisms to achieve specific expression patterns, which are essential to facilitate important biological processes such as embryonic development. Dysregulation of gene expression can lead to diseases such as cancers. A better understanding of the transcriptional regulation will therefore not only advance the understanding of fundamental biological processes, but also provide mechanistic insights into diseases. The earlier versions of high-throughput expression profiling techniques were limited to measuring average gene expression across large pools of cells. In contrast, recent technological improvements have made it possible to perform expression profiling in single cells. Single-cell expression profiling is able to capture heterogeneity among single cells, which is not possible in conventional bulk expression profiling. In my PhD, I focus on developing new algorithms, as well as benchmarking and utilising existing algorithms to study the transcriptomes of various biological systems using single-cell expression data. I have developed two different single-cell specific network inference algorithms, BTR and SPVAR, which are based on two different formalisms, Boolean and autoregression frameworks respectively. BTR was shown to be useful for improving existing Boolean models with single-cell expression data, while SPVAR was shown to be a conservative predictor of gene interactions using pseudotime-ordered single-cell expression data. In addition, I have obtained novel biological insights by analysing single-cell RNAseq data from the epiblast stem cells reprogramming and the leukaemia systems. Three different driver genes, namely Esrrb, Klf2 and GY118F, were shown to drive reprogramming of epiblast stem cells via different reprogramming routes. As for the leukaemia system, FLT3-ITD and IDH1-R132H mutations were shown to interact with each other and potentially predispose some cells for developing acute myeloid leukaemia.

The causal relationship between financial development and economic growth is a controversial issue. For developing countries, empirical studies have provided mixed result. This study seeks to empirically explore the relationship and the causal link between financial development and economic growth in two sub-Saharan African countries between 1970 and 2006. The empirical investigation is carried out using time methods and the five most commonly used indicators of financial development in the literature. However, the causal relationship was carried out using two different methods which are the autoregressive distributed lag bounds testing (ARDL) and the vector error correction model (VECM). Using this above methodology the study first found that in both countries there is a positive and long-term relationship between all the indicators of financial development and economic growth which was proxied by the real per capita GDP. With respect to the causality test, the two methods used provide mixed results especially in South Africa. In Cameroon the study found that financial development causes economic growth using the two methods, whereas in South Africa economic growth causes financial development when the VECM method is used, while there is an independence relationship between the two variables in South Africa when using ARDL. / Economics / M.Comm. (Economics)

The objective of the present study is to forecast annual rainfall taking into account the periodicities and structure of the stochastic component. This study has six Chapters. Chapter 1 presents introduction to the problem and objectives of the study. Chapter 2 consists of review of literature. Chapter 3 deals with the model formulation and development. Chapter 4 gives an account of the application of the model. Chapter 5 presents results and discussions. Chapter 6 gives the conclusions drawn from the study. In this thesis the following model formulations are presented in order to achieve the objective. Fourier analysis model is used to identify periodicities that are present in the rainfall series.1 These periodic components are used to obtain discrotized ranges which is an essential input for the Fourier series model. Auto power regression model is developed for estimation of rainfall and hence to compute the first order residuals errlt The parameters of the model are estimated using genetic algorithm. The auto power regression model is of the form, ( Refer the PDF File for Formula) where αi and βi are parameters and M indicates modular value. Fourier series model is formulated and solved through genetic algorithm to estimate the parameters amplitude R, phase Φ and periodic frequency wj for the residual series errlt. The ranges for the parameters R, Φ and wj were obtained from Fourier analysis model. errl't= /µerrlt+ Σj Rcos(wjt+ Φ) Further, an integrated auto power regression and Fourier series model developed (with parameters of the model being known from the above analysis) to estimate new rainfall series Zesťt=Zµ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) and the second order residuals, err2t is computed using, err2t = (zt –Zesťt) Thus, the periodicities are removed in the errlt series and the second order residuals err'2f obtained represents the stochastic component of the actual rainfall series. Auto regressive model is formulated to study the structure of the stochastic component err2t. The auto regressive model of order two AR(2) is found to fit well. The parameters of the AR(2) model were estimated using method of least squares. An exponential weighting function is developed to compute the weight considering weight as a function of AR{2) parameters. The product of weight and Gaussian white noise N(0, óerr2) is termed as weighted stochastic component. Also, drought analysis is performed considering annual (January to December) and summer monsoon (June to September) rainfall totals, to determine average drought interval (idrt) which is used in assigning signs to the random component of the forecasting model. In the final form of the forecasting model. Zest”t = Z µ Σ t αi(ZMi-t ) βi+µerrl+ Σj Rcos(wjt+ Φ) ± WT(Φ1, Φ2)N(0, óerr2) The weighted stochastic component is added or subtracted considering two criteria. Criterion I is used for all rainfall series except all-India series for which criterion II is used. The criteria also consider average drought interval Further, it can be seen that a ± sign is introduced to add or subtract the weighted stochastic component, albeit the stochastic component itself can either be positive or negative. The introduction of ± sign on the already signed value (instead of absolute value) is found to improve the forecast in the sense of obtaining more number of point rainfall estimates within 20 percent error. Incorporating significant periodicities, and weighted stochastic component along with average drought interval into the forecasting formulation is the main feature of the model. Thus, in the process of rainfall prediction, the genetic algorithm is used as an efficient tool in estimating optimal parameters of the auto power regression and the Fourier series models, without the use of an expensive nonlinear least square algorithm. The model application is demonstrated considering different annual rainfall series relating to IMD-Regions (RI...R5), all-india (AI), IMD-Subdivisions (S1...S29), Zones (Z1...Z10) and all-Karnataka (AK). The results of the proposed model are encouraging in providing improved forecasts. The model considers periodicity, average critical drought frequency and weighted stochastic component in forecasting the rainfall series. The model performed well in achieving success-rate of 70 percent with percentage error less than 20 percent in 4 out of 5 IMD Regions (R2 to R5), all-India, 17 out of 29 IMD Subdivisions (S1 to S5, S7 to S9, S18, S19, S21, S24 to S29) and all-Karnataka rainfall series. The model performance for Zones was not that-satisfactory as only 2 out of 10 Zones [Z1 and Z2) met the criterion. In a separate study, an effort was made to forecast annual rainfall using IMSL subroutine SPWF -which estimates Wiener forecast parameters. Monthly data is considered for the study. The Wiener parameters obtained were used to estimate monthly rainfall. The annual estimates obtained by simple aggregation of the monthly estimates compared extremely well with the actual annual rainfall values. A success rate of more than 80 percent with percentage error less than 10 percent is achieved in 4 out of 5 IMD Regions (R2 to R5), all-India, 18 out of 29 IMD Subdivisions (S1 to S8, S14, S18, S19, S22 to S24, S26 to S29) and all-Karnataka rainfall series. Whereas a success rate of 80 percent within 20 percent error is achieved in 4 out of 5 IMD Regions (except R1), all-India, 25 outof 29 IMD Subdivisions (except S10, S11, S12 and S17), all- Karnataka and 8 out of 10 Zones (except Z6 and Z8)(Please refer PDF File for Formulas)

The causal relationship between financial development and economic growth is a controversial issue. For developing countries, empirical studies have provided mixed result. This study seeks to empirically explore the relationship and the causal link between financial development and economic growth in two sub-Saharan African countries between 1970 and 2006. The empirical investigation is carried out using time methods and the five most commonly used indicators of financial development in the literature. However, the causal relationship was carried out using two different methods which are the autoregressive distributed lag bounds testing (ARDL) and the vector error correction model (VECM). Using this above methodology the study first found that in both countries there is a positive and long-term relationship between all the indicators of financial development and economic growth which was proxied by the real per capita GDP. With respect to the causality test, the two methods used provide mixed results especially in South Africa. In Cameroon the study found that financial development causes economic growth using the two methods, whereas in South Africa economic growth causes financial development when the VECM method is used, while there is an independence relationship between the two variables in South Africa when using ARDL. / Economics / M.Comm. (Economics)

The focus of the dissertation is based on the previous research of a relatively new field of environmental governance posed by climate change policy. The implementation of the climate change policy in Europe is then a subject to the discussion over the consistency of that policy with one of the fundamental goals of European integration. That goal is building an internal market as well as ensuring fair competition in such a market. The interaction of these two areas is a key objective of this research work. Given the complexity of the topic, the dissertation, for sake of clarity, is defined more narrowly. On one hand, the thesis provides with an analysis of European climate policy and its main instruments for regulating carbon emissions in the European economy - the European Union emission trading system (EU ETS). On the other hand, the thesis also provides with a description of the European competition policy. The reason is, as mentioned above, that the competition policy is a fundamental policy that guarantees the consistency of the implementation of environmental policies with the building of the internal market. The author analysed in the thesis basic measures implemented within the framework of those with the aim to prove out whether both policies are in mutual accord and whether they do function under the real terms. Based on the results achieved, the author can make following conclusions: The EU ETS mechanism decided for the European Union proved out to be a cost-efficient choice of emission reduction, despite of some temporary weakness that it has. Also, it can be concluded, that the allocation method is the ultimate criterion that determines both the efficiency of the climate action in Europe as well as its compliance with the competition policy. Stemming from that conclusion, there has been some strong evidence given that grandfathering has not been always in line with the state aid rules existing now in the environment protection. Last but not least, the optional use of the Article 10c of the Directive 2003/87/EC seems to be, from what one can say now, fully in line with the state aid rules valid in the European Union. However, further research in this field might be of very use in the future.

MSc (Statistics) / Department of Statistics / Over the years researchers have been modeling inflation rate in Ghana using linear models such as Autoregressive Integrated Moving Average (ARIMA), Autoregressive Moving Average (ARMA) and Moving Average (MA). Empirical research however, has shown that financial data, such as inflation rate, does not follow linear patterns. This study seeks to model and forecast inflation in Ghana using nonlinear models and to establish the existence of nonlinear patterns in the monthly rates of inflation between the period January 1981 to August 2016 as obtained from Ghana Statistical Service. Nonlinearity tests were conducted using Keenan and Tsay tests, and based on the results, we rejected the null hypothesis of linearity of monthly rates of inflation. The Augmented Dickey-Fuller (ADF) was performed to test for the presence of stationarity. The test rejected the null Hypothesis of unit root at 5% significant level, and hence we can conclude that the rate of inflation was stationary over the period under consideration. The data were transformed by taking the logarithms to follow nornal distribution, which is a desirable characteristic feature in most time series. Monthly rates of inflation were modeled using threshold models and their fitness and forecasting performance were compared with Autoregressive (AR ) models. Two Threshold models: Self-Exciting Threshold Autoregressive (SETAR) and Logistic Smooth Threshold Autoregressive (LSTAR) models, and two linear models: AR(1) and AR(2), were employed and fitted to the data. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were used to assess each of the fitted models such that the model with the minimum value of AIC and BIC, was judged the best model. Additionally, the fitted models were compared according to their forecasting performance using a criterion called mean absolute percentage error (MAPE). The model with the minimum MAPE emerged as the best forecast model and then the model was used to forecast monthly inflation rates for the year 2017. The rationale for choosing this type of model is contingent on the behaviour of the time-series data. Also with the history of inflation modeling and forecasting, nonlinear models have proven to perform better than linear models. The study found that the SETAR and LSTAR models fit the data best. The simple AR models however, out-performed the nonlinear models in terms of forecasting. Lastly, looking at the upward trend of the out-sample forecasts, it can be predicted that Ghana would experience double digit inflation in 2017. This would have several impacts on many aspects of the economy and could erode the economic gains i made in the year 2016. Our study has important policy implications for the Central Bank of Ghana which can use the data to put in place coherent monetary and fiscal policies that would put the anticipated increase in inflation under control.

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