China has undertaken a series of comprehensive economic and banking reform programs over the past three decades. As part of the WTO agreement, the domestic financial sector is fully open to foreign investors from WTO member countries in 2006. To answer the challenges, the policy makers and management of SOCB have been introducing two major steps to improve the Competitiveness of the commercial banks: transfer the bad debts to asset management companies and inject foreign exchange reserves to capital. However, the qualitative study shows that the general performance of the state-owned commercial banks is unstable during this period. It is high time that the consequences and efficiency of the reform were examined on an objective basis. This research offers a careful and rigorous examination of the condition and determinants of banking efficiency and competitiveness in China, with the focus on the state-owned commercial banks. The key contribution of this study is to develop a comprehensive empirical framework to measure and explain the performance of the state-owned commercial banks during the crucial transitional period from 1998 to 2003. This research examines the banking market conditions on the basis of a synthesis of the traditional Structure-Conduct-Performance paradigm and other alternative hypotheses. The thesis reveals that the state-owned commercial banks still dominate in both retail and business banking markets. The interest earnings remain the dominant source of commercial revenues. Due to the special relationship with government and their operational characters in the financial market, the state-owned commercial banks are not sensitive to monetary policy adjustments. The competition from other type of commercial banks has been strengthening, but the impact is rather limited. The main contribution of this study to the empirical literature on the Chinese banking market is the employment of the Data Envelopment Analysis to measure the efficiency of the state-owned commercial banks at provincial level, followed by a panel econometric investigation into the differences in banking efficiency across the stat-owned commercial banking groups as well as individual provinces. The results show that the level of banking efficiency was generally very low and there was a significant extent of input surplus among the provincial branches. The source of inefficiency is different among individual banking groups. The econometric study reveals that the SOCBs benefit from the concentrated market structure and strong complementary relationship with their traditional business areas. The empirical results have also shed light on further policy measures to enhance banking competition and performance in China.

From the idea of efficiency raised by Koopmans in 1951, and the panel data first introduced into the efficiency analysis by Pitt and Lee (1981) and Schmidt and Sickles (1984), the techniques of stochastic frontier analysis are fast developed and the applications of stochastic frontier are widely used in different areas, such as education, industry and hospital. But most researchers focus on only one aspect, either the development of new models or empirical applications. This thesis attempts to fill the gap to get a general idea of the properties of different panel data stochastic frontier models, on both statistical aspects and economic aspects, by the comparison of different models applied to different production applications. The thesis is also attempt to shed light on whether particular panel data stochastic frontier models are better suited to different data sets. The models selected capture the simplest situation, with no heterogeneity or heteroscedasticity, and complicated ones, with exogenous variables included in the models. Not only the classical models, such as the Pitt and Lee (1981) and Battese and Coelli (1992.1995), but also the new developed models, such as the latent class model and fixed management model are detected in the thesis. On the economic aspect, the data selected captures both microeconomic and macroeconomic, with the application to the World GDP and the Italian manufacturing industry. The results show that: the panel data stochastic frontier models perform better on the microeconomic level than on the macroeconomic level; the classical models perform better than the new developed ones; some panel data stochastic frontier models make ideal assumptions but the requirements to the dataset are hard to achieve; that the influence from the exogenous variables is quite strong.

This study examines the determinants of economic growth at sub-national level in South Africa, and investigates cross-locality medium-term (five-year) growth rate differentials between 354 magisterial districts. The period in question is 1998 to 2002. A dynamic panel data regression model is used that includes measures of geography (distance and natural resources) as well as recent estimates of physical and human capital. It is found that the significant determinants of local economic growth are distance from internal markets, human capital, export propensity, and the capital stock of municipalities (reflecting institutional quality and governance on local government level). Distance from international harbours, as a measure of transport costs, and urban agglomeration (or density) affects growth indirectly through its significant effect on the ability of a region to export. Overall, these results indicate that geography is important for economic growth, independent of its effects in institutions. Bearing in mind the medium-term focus of the work, no evidence of absolute convergence could be found over a five-year period, rather the tentative evidence suggests slow beta convergence. / Thesis (Ph.D. (Economics))--North-West University, Potchefstroom Campus, 2005.

Climate change is being observed through increased average temperatures world-wide, as well as through increased frequency of extreme events, such as floods and droughts. As climate is an uncontrollable yet essential input in the agriculture industry, the impact of climate change may have on crop production in Saskatchewan is of importance. The main objective of this study is to investigate how farmers adapt to climate change by switching their crop mix, and how this crop mix may change under future climate change scenarios. A fractional multinomial logit (FMNL) model was used to assess how total area of cropland has changed over a thirty year time period. The panel data included variables to represent the land characteristics of Saskatchewan (i.e. the three major soil zones - Black, Dark Brown and Brown), climatic variables to represent average monthly temperature and precipitation, and price and policy variables in order to assess how average seeded area of each crop group changed. With these results, a simple simulation model was developed to evaluate how the area of each crop group in a base year comparison (2000) would change under future climate scenarios for each soil zone. The results from the FMNL model indicate that crop allocation depends largely on the price of other crop groups and temperatures in the spring (April) and summer (July). Climate plays and important role in the major crop groups, such as wheat, canola and pulses. Cool, dry springs are the ideal conditions when choosing nearly all crops, while hot, wet summers increase the choice to leave land to summerfallow. Policy and the different soil zones also play a significant role in area allocation decisions. Changes in policies such as the removal of the Crow’s Nest Pass Agreement, and the removal of oats from the Canadian Wheat Board (CWB) marketing, had a negative impact on the choice to grow wheat, as expected. The different soil zones in Saskatchewan played an important role in area allocation for a majority of the crops, having a negative effect on the choice of wheat over every other crop group except pulses and summerfallow. Three climate change scenarios were simulated for each soil zone and compared to a base area (year 2000 area seeded) of crop groups. The findings from the projected changes in climate indicate that the area allocated to wheat will continue to decrease into the future, following current trends. The average projected decline in wheat area from the base years by 2099 ranges between 3.5% to 4.6% in the Black soil zone, between 2.7% and 2.9% in the Dark Brown and 2.7% to 4% in the brown soil zone, depending on climate change scenario. Interestingly, the area left to summerfallow is projected to increase over the future climate change scenarios. The choice of wheat is preferred over pulses, feed and forages, while the choice of specialty oilseeds (flaxseed, mustard seed and canary seed) are projected to become preferred over wheat in the future. The major conclusion from this research are: (i) following current trends, the area devoted to spring wheat and durum wheat would continue to decline into the future; (ii) Area devoted to wheat remains a preferred choice over pulses, feed and forages while specialty oilseeds represent a viable alternative choice to wheat and (iii) most significantly, summerfallow area would increase. This is in contrast to the current trend of declining summerfallow area as a result of tighter crop rotations. This finding was observed throughout all three soil zones as well as for all three climate change projection periods. This will have major implications on individual farmers as well as the economy in Saskatchewan, as summerfallow does not produce a crop in the year it is chosen. It is therefore important to determine a possible new crop mix that would benefit from the projected change in climate. This study could be improved by including a measure of profitability for each crop group and introducing a new crop group that is better suited to the projected change in climate in Saskatchewan.

This dissertation contains two essays studying panel data econometric models. First, we consider the problem of estimating a nonparametric panel data models with fixed effects. We propose using the profile least squares method to concentrate out the fixed effects and then estimate the unknown function by the kernel method. We show that our proposed estimator is consistent and has an asymptotically normal distribution. Monte Carlo simulations show that our proposed estimator performs well compared with several existing estimators. Second, we study the effects of Hong Kong’s fixed exchange rate against U.S. dollar using a novel panel data method. After the 1997 Asian Financial Crisis, many of the Asia countries adopted flexible exchange rate policies while Hong Kong still keeps its fixed exchange rate. By comparing Hong Kong versus its major trading partners, we show that if, like other Asian countries, Hong Kong had adopted a float exchange rate policy in October 1998, Hong Kong’s (counterfactual) total value of exports would increase by 14.65 %. Similarly, Hong Kong’s total value of imports would increase about 31%. We conclude that Hong Kong dollar is overvalued by 9.34% due to its fixed exchange rate policy.

Has economic integration improved the postwar growth performance of the actual fifteen member states of the European Union (EU)? To answer this question, we first construct an index of integration for each member state that explicitly accounts for global integration (GATT) as well as regional (European) integration. Using this variable, we test for permanent and temporary growth effects in a dynamic growth accounting framework, both in a time series setting for the (aggregate) EU and a panel approach for the EU member states. Although the hypothesis of permanent growth effects as postulated by endogenous growth models with scale effects is clearly rejected, we find significant levels effects: GDP per capita of the EU would be approximately one fifth lower today, if no integration had taken place since 1950. Interestingly, two third of this effect are due to GATT-liberalization. (author's abstract) / Series: EI Working Papers / Europainstitut

Since 1975, the extent of catching-up has been very different across Southern regions. Starting from the common arguments of growth theory, the paper wishes to show whether differences in regional income and growth can be attributed to different endowment in human capital, differences in private or public investment level, to structural imbalances, and labour force participation. The investigated panel consists of regional time series for the period 1975 to 1994 and includes NUTS II level regions of Greece, Spain, and the Italian South. Estimation of the impact of the variables on regional income is effected in a dynamic panel data model applying a GMM estimation procedure. The results indicate that the income level of Southern EU regions is largely determined by employment/educational levels and past public investment, while the impact of private investment is not significant. One may follow that EU regional policies should predominately focus on the human factor. Assistance to member countries to upgrade public infra-structures may be continued, but private investment incentives should be curbed. (author's abstract) / Series: EI Working Papers / Europainstitut

In this dissertation we proposed two generalizations of the Second-Order Least Squares (SLS) approach in two popular dynamic econometrics models. The first one is the regression model with time varying nonlinear mean function and autoregressive conditionally heteroskedastic (ARCH) disturbances. The second one is a linear dynamic panel data model. We used a semiparametric framework in both models where the SLS approach is based only on the first two conditional moments of response variable given the explanatory variables. There is no need to specify the distribution of the error components in both models. For the ARCH model under the assumption of strong-mixing process with finite moments of some order, we established the strong consistency and asymptotic normality of the SLS estimator. It is shown that the optimal SLS estimator, which makes use of the additional information inherent in the conditional skewness and kurtosis of the process, is superior to the commonly used quasi-MLE, and the efficiency gain is significant when the underlying distribution is asymmetric. Moreover, our large scale simulation studies showed that the optimal SLSE behaves better than the corresponding estimating function estimator in finite sample situation. The practical usefulness of the optimal SLSE was tested by an empirical example on the U.K. Inflation. For the linear dynamic panel data model, we showed that the SLS estimator is consistent and asymptotically normal for large N and finite T under fairly general regularity conditions. Moreover, we showed that the optimal SLS estimator reaches a semiparametric efficiency bound. A specification test was developed for the first time to be used whenever the SLS is applied to real data. Our Monte Carlo simulations showed that the optimal SLS estimator performs satisfactorily in finite sample situations compared to the first-differenced GMM and the random effects pseudo ML estimators. The results apply under stationary/nonstationary process and wih/out exogenous regressors. The performance of the optimal SLS is robust under near-unit root case. Finally, the practical usefulness of the optimal SLSE was examined by an empirical study on the U.S. airfares.

Bostadsmarknaden är en av de största tillgångsmarknaderna i ett land varpå förändringar i bostadspriserna får långt gångna konsekvenser för det enskilda hushållet, det finansiella systemet och samhällsekonomin i stort. Flertalet tidigare studier har analyserat den svenska bostadsmarknaden utifrån ett storstadsperspektiv alternativt jämfört Sveriges bostadsmarknad mot andra länder. Vi har identifierat att studiet kring vad som bestämmer prisnivån på regionala bostadsmarknader i Sverige är tämligen oexploaterat varför avsikten med den här studien är att analysera bestämningsfaktorer till de svenska bostadspriserna på länsnivå. Sålunda är ett bidragande mål med denna studie att tillföra en bättre förståelse för dynamiken på den svenska bostadsmarknaden. I studien använder vi multipel regression där vi bearbetar paneldata med en Fixed Effect Model. Ett flertal förtester har gjorts för att få fram den mest tillförlitliga modellen i vilken vi skattat bostadspriserna utifrån teoretiskt belagda förklaringsvariabler. De slutsatser vi har dragit är att disponibel inkomst, befolkningstäthet och sysselsättningsgrad kan förklara bostadspriserna på länsnivå med en procents signifikansnivå. Skillnaden i bostadspriserna mellan länen har relativt sett ökat över tidsperioden för studien. Avslutningsvis diskuteras uppvisade avvikelser mellan de verkliga bostadspriserna och de skattade bostadspriserna vilka kan förklaras av att bostadsmarknaden är känslostyrd med inslag av spekulationer. / The housing market is one of the greatest assets markets in a given country. Therefore, changes in housing prices have a big impact on the single household, the financial system and the economic system as a whole. Due to the housing markets vital role in the society, many scientific studies have been done with the purpose of enlighten and discover the dynamics of the Swedish housing market. The focuses in these earlier studies have more than often taken a metropolitan perspective or compared the Swedish housing market with other countries. However, this study divides the Swedish housing market into regional county level with the purpose of analyzing determinants of housing prices due to county specific variables. By analyzing the housing prices due to county specific factors a contributing goal with this study is to deepen the understanding about the dynamics in the Swedish housing market. In this study we have used multiple regressions in order to work with panel data. The Fixed Effect Model fitted our purpose well which is why that kind of model was used in order to estimate the housing prices for every single Swedish county. The conclusions drawn in this study are that disposable income, people density and employment rate are all statistically significant on one percent level in order to explain the housing price at state level. We have also discovered that, during the observed period, the relative differences in housing prices between the different states have increased. Finally, the differences found between the real housing prices and the estimated housing prices, can be explained by the assumption that the housing market is driven by emotions and speculations.

Several studies have examined the effect of remittances on economic growth,poverty, education, and governance, among other factors, in developing countrieswith inconclusive results. Using annual panel data of 99 developing countries invarious empirical models, this study aim to answer the question how remittances affect a broader aspect of development using the Human Development Index asdependent variable. The findings indicate that there is a positive relationship between remittances and the level of human development in developing countries.