Mainland China and Taiwan successfully joined WTO to be active members in December 2001 and January 2002. They relax economic and trade restrictions from both sides and make the relevant regulations and mechanisms more transparent year by year. Since May 2005, Mainland China implemented preferential policies, such as zero tariff, clearing customs and opening ' the fast roadway ' to many kinds of Taiwan¡¦s fruits, etc.That makes the proportion of Taiwan¡¦s fruits sold in Mainland China improve year by year, and the marketing stronghold expand constantly, too. According to the past documents and experiences, Taiwan fruit is relatively suitable for taking the gift box and up-market route, and aims at the demand of the high consumption group. Because most people in Mainland China consume the goods for freshness or the demands of giving a present, they would not purchase it in usual time. Therefore, we should avoid depending too much on some agricultural products and overly concentrate on the specific country. What we need to do is carrying on the global market survey, opening up the new service stronghold actively in order to improve the competitiveness of agricultural products in our country. In addition, the consumption habits between the South and North of Mainland China are different. We should make variable marketing strategies for different consumption ethnicities and match their demands and preferences. We set up an empirical model by using Time Series Analysis and actual data. The research includes the weight and amount sold to Mainland China, policy of zero tariff, old farmers¡¦ subsidies, areas of fruit trees, transportation and storage, Taiwan¡¦s inflation rate and processed goods of farm production. The main conclusion shows that the effect of zero tariff policy is not good enough to Taiwan¡¦s export sales. The possible reason may be that the cross-strait system is not sound. Finally, we compare the changes in production of Taiwan fruits between Years 2004-2008, and choose three levels of Taiwan fruits. The First one is new stars, like betel nuts, mango, orange, grapefruits and shakya. The second one is keeping it¡¦s level, like jujube, pineapple, guava, papaya, starfruit and wax apple. The third one is lack of competition, like coconut, peach, banana, orange, lemon, plum, persimmon, loquat, etc. The possible reason may be come from outside pressure or not suitable to plant in Taiwan.

<p><strong>Abstract</strong></p><p>The main objective of our paper is to investigate whether expansion in exports can lead to improve economic growth of Sub-Saharan African countries for the period 1970-2006. Four macro economic indicators (real GDP, Trade balance, Government expenditure and</p><p>Investment) are used in our model to carry out our analysis concerning Sub Saharan African countries.</p><p>Time series techniques such as unit root test (Augmented Dickey Fuller test) and co integration test (Johansen’s procedure) are used to find out whether there is a long run relationship between economic growth and trade balance.</p><p>The results of the unit root test indicate that all series are stationary after first difference, with I (1). Johansen’s co integration test showed that co integration (long run relationship) exists between GDP and Trade balance, as we got significant eigenvalues and found co integration between all of the four variables which shows that they are co integrated with each other and indicates a long run relationship.</p><p>Our results indicate that for the time period of 1970 to 2006, Sub Saharan African countries experienced a simultaneous increase in economic growth and trade balance as well as in investment and Govt expenditure.</p><p> </p><p><strong>Key words</strong>: exports, economic growth, unit root, co integration, Sub-Saharan Africa</p>

<p>Trade openness, market size, transparency, ease of doing business, location advantagesand low levels of corruption and country risk are the main determinants that attractForeign Direct Investment into a host country. FDI inflows in México have increasedremarkably since 1994 when the North America Free Trade Agreement (NAFTA) cameinto effect. Using multiple regression analysis in order to measure the impact of FDI onGDP; the Empirical results showed that a one percent increase in FDI leads on average toan increase of 0.08 percent in GDP which clearly reflects a positive but neither animportant nor a substantial impact of FDI on economic growth in México as it would beexpected. Time series data analysis for the period 1980-2007 has been tested for UnitRoot by applying the Dickey-Fuller (DF) test. Each time series after the first differencebecomes stationary and therefore it might be a causal relationship among the variables.However, FDI will not have a real impact on the society unless there is an effective stockof Human Capital capable of learning and absorbing the know-how to work successfullywith the technology that Multinational Corporations bring into the host country with theirinvestment. The challenge for the Mexican Government is to create structural reformssuch as the deregulation of energy and oil sector for private investment that will lead toconstantly higher flows of FDI. In the medium term this will then be reflected in thesociety in terms of poverty reduction and development of its population.</p>

This paper examines whether the CPI and real GDP for the US exhibit nonlinear reversion to trend as recently concluded by Beechey and Österholm [Beechey, M. and Österholm, P., 2008. Revisiting the uncertain unit root in GDP and CPI: testing for nonlinear trend reversion. Economics Letters 100, 221-223]. The wild bootstrap is used to correct for non-normality and heteroscedasticity in a nonlinear unit root test. The use of 'wild bootstrapped' critical values affects test conclusions in some cases. Results also are sensitive to the sample period examined.

Magister Commercii - MCom / The following research paper is based on employment intensity, arguing the notion that an increase in economic growth alone does not necessary increase the rate of employment in South Africa. In fact, other additional macroeconomic factors determine changes in the rate of employment, along with economic growth. This research measured the employment numbers in each South African sector with reference to sector-specific gross value added, to determine the level of elasticity of employment in each sector. This was done by extracting quarterly data in-between the year 1995 to 2019. For each sector, a unit root test was estimated, an ARDL bound test for cointegration, an error correction model. A stability and diagnostic test were conducted to test the fluidity of each regression model. The coefficient of each sector modelled indicated no correlation between employment and economic growth. In “all sectors”, the results of GVA were not influential enough to implement positive change in the levels of employment, thus, leading to jobless growth.

This thesis is comprised of five papers that all relate to bootstrap methodology in analysis of non-stationary time series. The first paper starts with the fact that the Dickey-Fuller unit root test using asymptotic critical value has bad small sample performance. The small sample correction proposed by Johansen (2004) and bootstrap are two effective methods to improve the performance of the test. In this paper we compare these two methods as well as analyse the effect of bias-adjusting through a simulation study. We consider AR(1) and AR(2) models and both size and power properties are investigated. The second paper studies the asymptotic refinement of the bootstrap cointegration rank test. We expand the test statistic of a simplified VECM model and a Monte Carlo simulation was carried out to verify that the bootstrap test gives asymptotic refinement. The third paper focuses on the number of bootstrap replicates in bootstrap Dickey-Fuller unit root test. Through a simulation study, we find that a small number of bootstrap replicates are sufficient for a precise size, but, with too small number of replicates, we will lose power when the null hypothesis is not true. The fourth and last paper of the thesis concerns unit root test in panel setting focusing on the test proposed by Palm, Smeekes and Urbain (2011). In the fourth paper, we study the robustness of the PSU test with comparison with two representative tests from the second generation panel unit root tests. In the last paper, we generalise the PSU test to the model with deterministic terms. Two different methods are proposed to deal with the deterministic terms, and the asymptotic validity of the bootstrap procedure is theoretically checked. The small sample properties are studied by simulations and the paper is concluded by an empirical example. / <p>Ogiltigt ISBN: 978-91-554-9069-0</p>

Periodically correlated time series generally exist in several fields including hydrology, climatology, economics and finance, and are commonly modelled using periodic autoregressive (PAR) model. For a time series with stochastic periodic trend, for which a unit root is expected, a periodically integrated autoregressive PIAR model with periodic and/or seasonal unit root has been shown to be a satisfactory model. The existing theory used the multivariate methodology to study PIAR models. However, this theory is convoluted, majority of it only developed for quarterly time series and its generalisation to time series with larger number of periods is quite cumbersome. This thesis studies the existing theory and highlights its restrictions and flaws. It provides a coherent presentation of the steps for analysing PAR and PIAR models for different number of periods. It presents the different unit roots representations and compares the performance of different unit root tests available in literature. The restrictions of existing studies gave us the impetus to develop a unified theory that gives a clear understanding of the integration and unit roots in the periodic models. This theory is based on the spectral information of the multi-companion matrix of the periodic models. It is more general than the existing theory, since it can be applied to any number of periods whereas the existing methods are developed for quarterly time series. Using the multi-companion method, we specify and estimate the periodic models without the need to extract complicated restrictions on the model parameters corresponding to the unit roots, as required by NLS method. The multi-companion estimation method performed well and its performance is equivalent to the NLS estimation method that has been used in the literature. Analysing integrated multivariate models is a problematic issue in time series. The multi-companion theory provides a more general approach than the error correction method that is commonly used to analyse such time series. A modified state state representation for the seasonal periodically integrated autoregressive (SPIAR) model with periodic and seasonal unit roots is presented. Also an alternative state space representations from which the state space representations of PAR, PIAR and the seasonal periodic autoregressive (SPAR) models can be directly obtained is proposed. The seasons of the parameters in these representations have been clearly specified, which guarantees correct estimated parameters. Kalman filter have been used to estimate the parameters of these models and better estimation results are obtained when the initial values were estimated rather than when they were given.

This study examines the dynamic interaction between housing prices, stock prices and the repo rate in the Stockholm region by using the Johansen tests for cointegration. Several studies have been done on this topic, but the results are mixed across the world, and not many have been done in Scandinavia. This study contributes to the literature by examining eleven years of monthly data for the housing prices in the Stockholm region. We find evidence of a long-run relationship between housing prices, stock prices and the interest rate. There is a negative relationship between housing prices and the interest rate as well as between stock prices and the interest rate, but a positive relationship between housing prices and stock prices.  However, the results are somewhat sensitive to model specification and therefore further studies on the topic are encouraged.

This study tests for the market efficiency of the Nairobi Securities Exchange (NSE) after the year 2000 to determine the effect of technological advancements on market efficiency. Data that is used is the NSE 20 share index over the period 2001 to 2015; and the NSE All Share Index (NSE ASI) from its initiation during 2008 to 2015. We cannot accept the Efficient Market Hypothesis (EMH) for the NSE using the serial correlation test, the unit root tests and the runs test. However, we can accept the EMH for the more robust variance ratio test. Overall, the results of the market efficiency are mixed. The most significant finding is that the efficiency of the NSE has increased since the year 2000 which suggests that advancements in technology have contributed to the increase in the market efficiency of the NSE. / Business Management / M. Com. (Business Management)

Dynamic panel data models are widely used by econometricians to study over time the economics of, for example, people, firms, regions, or countries, by pooling information over the cross-section. Though much of the panel research concerns inference in stationary models, macroeconomic data such as GDP, prices, and interest rates are typically trending over time and require in one way or another a nonstationary analysis. In time series analysis it is well-established how autoregressive unit roots give rise to stochastic trends, implying that random shocks to a dynamic process are persistent rather than transitory. Because the implications of, say, government policy actions are fundamentally different if shocks to the economy are lasting than if they are temporary, there are now a vast number of univariate time series unit root tests available. Similarly, panel unit root tests have been designed to test for the presence of stochastic trends within a panel data set and to what degree they are shared by the panel individuals. Today, growing data certainly offer new possibilities for panel data analysis, but also pose new problems concerning double-indexed limit theory, unobserved heterogeneity, and cross-sectional dependencies. For example, economic shocks, such as technological innovations, are many times global and make national aggregates cross-country dependent and related in international business cycles. Imposing a strong cross-sectional dependence, panel unit root tests often assume that the unobserved panel errors follow a dynamic factor model. The errors will then contain one part which is shared by the panel individuals, a common component, and one part which is individual-specific, an idiosyncratic component. This is appealing from the perspective of economic theory, because unobserved heterogeneity may be driven by global common shocks, which are well captured by dynamic factor models. Yet, only a handful of tests have been derived to test for unit roots in the common and in the idiosyncratic components separately. More importantly, likelihood-based methods, which are commonly used in classical factor analysis, have been ruled out for large dynamic factor models due to the considerable number of parameters. This thesis consists of four papers where we consider the exact factor model, in which the idiosyncratic components are mutually independent, and so any cross-sectional dependence is through the common factors only. Within this framework we derive some likelihood-based tests for common and idiosyncratic unit roots. In doing so we address an important issue for dynamic factor models, because likelihood-based tests, such as the Wald test, the likelihood ratio test, and the Lagrange multiplier test, are well-known to be asymptotically most powerful against local alternatives. Our approach is specific-to-general, meaning that we start with restrictions on the parameter space that allow us to use explicit maximum likelihood estimators. We then proceed with relaxing some of the assumptions, and consider a more general framework requiring numerical maximum likelihood estimation. By simulation we compare size and power of our tests with some established panel unit root tests. The simulations suggest that the likelihood-based tests are locally powerful and in some cases more robust in terms of size. / Solving Macroeconomic Problems Using Non-Stationary Panel Data