新一代航天测量船导航系统信息融合技术

分析了我国航天测量船导航系统的优缺点，提出了一种基于分散化滤波理论的联邦卡尔曼滤波体系结构（FKF），并研究了其最优融合估计算法和实时性。仿真结果表明，它可以有效地提高组合导航系统的精度和容错性能。最终的设计方案已成功应用于一种航天测量船组合导航系统中。     

改进的“当前”统计模型变采样率目标跟踪算法

为满足实际雷达系统对高精度和高实时性的要求,提出了一种改进的“当前”统计模型变采样率机动目标跟踪算法。该算法针对“当前”统计模型必须预设加速度极值和机动频率的问题,提出一种加速度方差和机动频率在线同步自适应方法,建立改进的“当前”统计模型机动目标跟踪算法;针对在线自适应方法计算量大的问题,结合采样周期的大小与目标机动特性的关系,引入变采样率方法。仿真结果表明,与传统“当前”统计模型相比,改进的“当前”统计模型机动目标跟踪算法能显著提高对不同机动强度目标的跟踪精度;变采样率方法通过减少采样点数,节省了系统资源,提高了跟踪实时性;所提算法将两者结合,用传统的“当前”统计模型1.5～2倍的平均采样周期得到了更小的位置均方根误差,实现了用单模型方法同时改善跟踪精度和实时性的目的。     

The tourism-led growth hypothesis: empirical evidence from Turkey

In the economic growth literature, the contribution of tourism to economic development has attracted great attention due to its significant roles as a source of foreign exchange earnings, creation of employment opportunities and an important source of public revenues in many countries. In this paper, we aim to analyse the empirical relationship between economic growth and tourism by employing different econometric techniques. First, we employed the Bound test approach developed by Pesaran, Shin, and Smith (2001, Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326) in order to investigate the co-integration relationship between economic growth and tourism. Second, we used the Granger causality analysis for the 1998–2011 period and found evidence of a long-run uni-directional causality running from tourism to economic growth, but not vice versa. Our findings show that the Turkish case supports the tourism-led growth hypothesis (TLGH). Third, the autoregressive-distributed lag approach was employed in order to investigate the long-term and short-term static relationship between tourism and economic growth. The results show that tourism has a positive effect on gross domestic product and economic growth both in the long-term and short-term. Finally, the effect of tourism on economic growth was also investigated dynamically by employing the Kalman filter method. The findings of this method support the TLGH for Turkey.     

A Partially Observed Model for Micromovement of Asset Prices with Bayes Estimation via Filtering

A general micromovement model that describes transactional price behavior is proposed. The model ties the sample characteristics of micromovement and macromovement in a consistent manner. An important feature of the model is that it can be transformed to a filtering problem with counting process observations. Consequently, the complete information of price and trading time is captured and then utilized in Bayes estimation via filtering for the parameters. The filtering equations are derived. A theorem on the convergence of conditional expectation of the model is proved. A consistent recursive algorithm is constructed via the Markov chain approximation method to compute the approximate posterior and then the Bayes estimates. A simplified model and its recursive algorithm are presented in detail. Simulations show that the computed Bayes estimates converge to their true values. The algorithm is applied to one month of intraday transaction prices for Microsoft and the Bayes estimates are obtained.     

实时OD估计中卡尔曼滤波算法研究

ITS研究中，动态OD矩阵估计是交通动态分配的关键因素。针对OD矩阵难以获得的问题，回顾动态OD矩阵估计理论的发展历程，明确该领域研究中的三个关键问题：状态变量的选择、系统方程的确定、OD量与路段流量的关系（分配矩阵）。在此基础上介绍一种基于历史OD矩阵数据的估计方法，卡尔曼滤波算法，为动态OD矩阵估计理论的进一步的研究奠定基础。     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

中国的潜在产出、产出缺口及产量--通货膨胀交替关系--基于"Kalman滤波"方法的研究

本文介绍并运用“Kalman滤波”方法估计1979-2004年间我国的潜在经济增长率和产出缺口，然后根据这些结果检验了我国经济增长与通货膨胀率之间的交替关系、社会的通货膨胀预期对经济的影响、决定产出缺口大小的影响因素等，最后利用研究结果对我国2005年的经济发展进行了预测。     

Testing the significance and the non-linearity of the Phillips trade-off in the Euro Area

This paper tests the significance and the non-linearity of the Phillips trade-off in the aggregate Euro Area, in an unobserved components model of stochastic NAIRU and trend output featuring the Phillips equation and the Okun law as main identifying equations, with quarterly data for 1970:I-2002:III. The Phillips curve turns out to be linear and its trade-off statistically significant, while non-linearity shows up in the Okun relation. The results prove to be robust to alternative lag length structures of the model, and to alternative non-linear functional forms. The trend-cycle decompositions estimated with the model capture the main features of the Euro Area’s recent macroeconomic record.First version received: 1 September 2003 / Final version received: June 2004CEMPRE - Centro de Estudos Macroeconómicos e Previsão - is supported by the Fundação para a Ciência e a Tecnologia, Portugal, through the Programa Operacional Ciência, Tecnologia e Inovação (POCTI) of the Quadro Comunitário de Apoio III, which is financed by FEDER and Portuguese funds.We thank comments on earlier versions by Fabio Canova, Miguel St Aubyn, Alvaro Almeida, Pete Richardson, Kevin Ross, and two anonymous referees. We acknowledge James D. Hamilton’s help with the confidence bands. The usual disclaimer applies.     

Seasonality and Markov switching in an unobserved component time series model

It is generally acknowledged that the growth rate of output, the seasonal pattern, and the business cycle are best estimated simultaneously. To achieve this, we develop an unobserved component time series model for seasonally unadjusted US GDP. Our model incorporates a Markov switching regime to produce periods of expansion and recession, both of which are characterized by different underlying growth rates. Although both growth rates are time-varying, they are assumed to be cointegrated. The analysis is Bayesian, which fully accounts for all sources of uncertainty. Comparison with results from a similar model for seasonally adjusted data indicates that the seasonal adjustment of the data significantly alters several aspects of the full model. First Version Received: January 2001/Final Version Received: February 2002 Send offprint requests to: Rob Luginbuhl?Correspondence to: Rob Luginbuhl