Foreign Direct Investment and Regional Growth in China

China has experienced rapid economic growth and the recent Global Economic Projections 2004 by the World Bank suggest that there is a continuation of Chinese growth of at lest 7 to 8 percent (World Bank, 2003). Nevertheless, on the background of rapid growth came increasing regional disparities. This paper uses the augmented Solow-Swan model of Mankiw, Romer and Weil (1992) to analyze data on provinces of China over the reform period 1978-2003. Our main finding is that FDI has a positive and statistically significant impact on economic growth as theory predicts and the augmented Solow-Swan model provides an excellent fit of the data. The other determinants are significant at one percent level and have the expected sign. However, the human capital is insignificant or the coefficient is negative. --economic growth,conditional convergence,regional disparities

An approximation scheme for semilinear parabolic PDEs with convex and coercive Hamiltonians

We propose an approximation scheme for a class of semilinear parabolic equations that are convex and coercive in their gradients. Such equations arise often in pricing and portfolio management in incomplete markets and, more broadly, are directly connected to the representation of solutions to backward stochastic differential equations. The proposed scheme is based on splitting the equation in two parts, the first corresponding to a linear parabolic equation and the second to a Hamilton-Jacobi equation. The solutions of these two equations are approximated using, respectively, the Feynman-Kac and the Hopf-Lax formulae. We establish the convergence of the scheme and determine the convergence rate, combining Krylov's shaking coefficients technique and Barles-Jakobsen's optimal switching approximation.Comment: 24 page

Growth, unemployment, and business cycle integration: Empirical evidence from China

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis aims to study the macroeconomic performance of China. China has been experiencing rapid economic growth and it has been changing gradually from a planned to a market economy since it initiated the well known “open door policy” combined with a “coastal development strategy” in 1978. However, rapid growth has occurred on the background of increasing regional disparity. Meanwhile, unemployment has increased significantly during last two decades, and has become one of the most pressing problems of the Chinese economy today. Moreover, another major challenge facing the Chinese economy is how to deal with various shocks, and to ensure the sustainability and balance of economic growth in the face of the increasing economic uncertainties associated with its deep reform and integration into the world trade and financial system. Based on the above concerns and literature review, this study, firstly, uses an augmented Solow-Swan model of Mankiw, Romer and Weil (1992) to assess the role FDI plays in underlying regional differences in economic growth across Chinese provinces over the reform period 1978-2008. My analysis indicates that the augmented Solow growth model appears to provide a good description of regional growth patterns in China over the period 1978-2008 and the data display conditional convergence. After controlling for FDI and other determinants of growth, provinces that were initially poor tend to grow faster and the evidence in favour of conditional convergence becomes even stronger after splitting the data into subsamples. I then focus on the study of the relationship between unemployment and growth at both national level and regional level in order to find out how unemployment affects China’s economic growth and economic reform progress overall. I find that Okun’s relationship does not hold in China universally and, furthermore, the nature of the observed relationship has changed during the transition progress. I argue that there are hump shaped relationships both between growth and unemployment and between the speed of transition and unemployment in China. The results are consistent with several theoretical and empirical studies in the literature. Finally, structural VAR methodology pioneered by Bayoumi and Eichengreen (1993) is used to identify and decompose supply and demand shocks to two variables, (the log of) output (annual real GDP) and (the log of) prices (annual GDP deflator). I then compute and discuss the correlation of such shocks across provinces and show how it has evolved over the four main sub-periods of China’s history. Moreover, I investigate which factors contribute to economic integration or divergence in the Chinese economy

Adaptive Sampling with Mobile Sensor Networks

Mobile sensor networks have unique advantages compared with wireless sensor networks. The mobility enables mobile sensors to flexibly reconfigure themselves to meet sensing requirements. In this dissertation, an adaptive sampling method for mobile sensor networks is presented. Based on the consideration of sensing resource constraints, computing abilities, and onboard energy limitations, the adaptive sampling method follows a down sampling scheme, which could reduce the total number of measurements, and lower sampling cost. Compressive sensing is a recently developed down sampling method, using a small number of randomly distributed measurements for signal reconstruction. However, original signals cannot be reconstructed using condensed measurements, as addressed by Shannon Sampling Theory. Measurements have to be processed under a sparse domain, and convex optimization methods should be applied to reconstruct original signals. Restricted isometry property would guarantee signals can be recovered with little information loss. While compressive sensing could effectively lower sampling cost, signal reconstruction is still a great research challenge. Compressive sensing always collects random measurements, whose information amount cannot be determined in prior. If each measurement is optimized as the most informative measurement, the reconstruction performance can perform much better. Based on the above consideration, this dissertation is focusing on an adaptive sampling approach, which could find the most informative measurements in unknown environments and reconstruct original signals. With mobile sensors, measurements are collect sequentially, giving the chance to uniquely optimize each of them. When mobile sensors are about to collect a new measurement from the surrounding environments, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under Haar Wavelet domain, under which most nature signals appear sparse, to infer a model of the environments. The most informative measurements can be determined by optimizing model parameters. As a result, all the measurements collected by the mobile sensor network are the most informative measurements given existing information, and a perfect reconstruction would be expected. To present the adaptive sampling method, a series of research issues will be addressed, including measurement evaluation and collection, mobile network establishment, data fusion, sensor motion, signal reconstruction, etc. Two dimensional scalar field will be reconstructed using the method proposed. Both single mobile sensors and mobile sensor networks will be deployed in the environment, and reconstruction performance of both will be compared.In addition, a particular mobile sensor, a quadrotor UAV is developed, so that the adaptive sampling method can be used in three dimensional scenarios

Controllability analysis and design for underactuated stochastic neurocontrol

Neuroengineering has advanced tremendously over the past decade, but for sensory prosthetics and similar applications, it remains an extraordinary challenge to access neurons at the single cell resolution of most sensory encoding theories. In particular, if each neuron is “tuned” to particular stimulus features, then eliciting a target percept requires activating only neurons tuned to that percept and not others. However, most available technology is underactuated, with orders of magnitude fewer independent control inputs than neural degrees of freedom, possibly limiting its effectiveness given the inherent trade-off of resolution with network size. Here I analyze controllability for pairs of neurons receiving a common input. In particular, I extend previous work on the deterministic control problem to include stochastic membrane dynamics, treating both cases as a bifurcation problem in the noise parameter. I determine controllable regions in parameter space using a combination of mathematical analysis and numerical solution of stochastic differential and Fokker-Planck equations. I explain how boundaries between these regions change with noise level, and connect to the dynamical mechanisms by which controllability is lost. I show that in stochastic systems, in contrast to deterministic systems, expanding the allowable input space to include exponential ramps expands the parameter range over which neuron pairs are controllable. I also describe an alternative controllability definition using only mean spike times, as compared to the probability distribution of spiking within prespecified time intervals. These results could guide future control strategies in the development of sensory neuroprosthetics and other neurocontrol application