基于DMC-Ⅱ数码航空摄影测量区域网布设研究

从航空摄影到基础地理信息数据产品的获取包括航空摄影、地面控制、空三加密、内业测图等几个阶段。相片控制测量是航空摄影测量的基础工作,通过空三加密获得内业测图所需的外方位元素数据。随着DMC等数字航摄仪应用到航测生产领域当中,DMC影像数据在全数字摄影测量中发挥了越来越重要的作用。本文利用DMC-II数码航空影像,通过实验对DMC-II数字航摄资料的在西南控制困难地区外业像控的布点方案、基线数量、点位位置采取不同方案进行内业加密问题进行研究、分析,总结区域网布点方案的规律性。     

Risk and seasonal effects: international evidence

Various explanations have been advanced for the January effect in the existing literature, but no consensus has been arrived at to distinguish one particular explanation from any others. In this paper, a time-series GARCH-M model with conditional variance as a proxy for market systematic risk is applied to investigate the seasonal effects in four countries with different tax system and tax year end: the USA, the UK, China and Australia. Empirical evidence showed a January effect in the USA, a January and an April effect in the UK, a July effect in Australia and no significant seasonal effect in China. This pattern consistently links to tax year end and the tax system in the sample countries; however, no clear evidence has been found to support the proposition that market risk is higher or priced highly only in calendar months with a seasonal effect. However, to reflect the seasonal effect, an interactive dummy variable is added into the time-series GARCH-M model, and the seasonal effects are explained away. The results of the sampled countries support the proposition that market volatility increases when it is close to the date of financial statement performance due to the uncertainty of the financial information.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Rotation Patterns and Trend Estimation for Repeated Surveys Using Rotation Group Estimates

A general approach for constructing filters to produce trend estimates from a repeated survey is described. This approach accounts for the correlation structure induced by the rotation pattern used in the survey. Different filters are developed depending on whether the trend analysis is based on elementary estimates available for each rotation group or overall estimates obtained by combining the rotation group estimates. The properties of trend estimates obtained directly from the elementary estimates, those obtained from the simple average of the rotation group estimates and trend estimates obtained from the best linear unbiased estimates of the population characteristics of interest are compared. These comparisons are done for a number of rotation pattern, enabling an assessment of the impact of the choice of rotation patterns on trend estimation.     

Measuring financial risk and portfolio reversion with time changed tempered stable Lévy processes

Given that underlying assets in financial markets exhibit stylized facts such as leptokurtosis, asymmetry, clustering properties and heteroskedasticity effect, this paper applies the stochastic volatility models driven by tempered stable Lévy processes to construct time changed tempered stable Lévy processes (TSSV) for financial risk measurement and portfolio reversion. The TSSV model framework permits infinite activity jump behaviors of returns dynamics and time varying volatility consistently observed in financial markets by introducing time changing volatility into tempered stable processes which specially refer to normal tempered stable (NTS) distribution as well as classical tempered stable (CTS) distribution, capturing leptokurtosis, fat tailedness and asymmetry features of returns in addition to volatility clustering effect in stochastic volatility. Through employing the analytical characteristic function and fast Fourier transform (FFT) technique, the closed form formulas for probability density function (PDF) of returns, value at risk (VaR) and conditional value at risk (CVaR) can be derived. Finally, in order to forecast extreme events and volatile market, we perform empirical researches on Hangseng index to measure risks and construct portfolio based on risk adjusted reward risk stock selection criteria employing TSSV models, with the stochastic volatility normal tempered stable (NTSSV) model producing superior performances relative to others.     

An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem

This problem involves optimizing product collection and redistribution from production locations to a set of processing plants over a planning horizon. This horizon consists of several days, and the collection-redistribution is performed on a repeating daily basis. A single routing plan must be prepared for the whole horizon, taking into account the seasonal variations in the supply. We model the problem using a sequence of periods, each corresponding to a season. We propose an adaptive large-neighborhood search with several specifically designed operators and features. The results show the excellent performance of the algorithm in terms of solution quality and computational efficiency.     

Mean reversion in annual earnings and its implications for security valuation

This article documents the long-horizon mean reverting character of annual earnings and tests the implications of such mean reversion for security valuation. First, both theory-based and nonparametric measures of earnings persistence decrease as the estimation order increases, revealing 40 percent less long-horizon persistence than expected under the commonly used random walk model. Second, the return responses to the earnings shocks are more closely related across firms to the higher-order measures of persistence that reflect significant long-horizon mean reversion. Third, the persistence measure derived from classical valuation theory outperforms the generic measure in explaining the return responses. Taken as a whole, these results provide evidence for significant mean reversion in the higher-order properties of earnings and for the stock market incorporating these properties in a manner consistent with classical valuation theory.     

河北省城市综合承载力分析与对策研究

针对河北省11个设区城市,选取了资源承载力、环境承载力、经济承载力和社会承载力4个指标,建立了城市综合承载力的评价指标体系。通过综合评价与比较分析,得出如下结论:环境承载力是对河北省综合承载力影响最大的指标;石家庄市的综合承载力最大,为0.083 8;唐山市的经济承载力最大,为0.016 3,综合承载力相对较低,主要限制因素为环境承载力。提出了关于河北省提高综合承载力的建议和对策。     

深沪股市1992—2001年季节性波动的实证研究

股票市场季节性波动是股票投资收益的短期波动理论 ,这种现象在许多国家的股票市场中存在。通过对我国股市 1 992至 2 0 0 1年的实证研究 ,发现我国股市出人意料地不存在显著的季节性波动     

Do Malaysian house prices follow a random walk? Evidence from univariate and panel LM unit root tests with one and two structural breaks

In this article we apply univariate and panel Lagrange Multiplier (LM) unit root tests with one and twostructural breaks proposed by Lee and Strazicich (2003, 2004) and Im et al. (2005) to examine housing prices for five different housing price indices (all housing, detached housing, semi-detached housing, terrace housing and high-rise housing) in 14 states of Malaysia to test whether housing prices exhibit a random walk. Our main finding from the univariate LM unit root tests is that for the vast majority of states housing prices follow a stationary process about a segmented trend. The results of the panel LM unit root tests provide overwhelming evidence that house prices are segmented trend reverting.