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     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.     

Time varying country risk: an assessment of alternative modelling techniques

Three different techniques for the estimation of a time-varying beta are investigated: a bivariate GARCH model, the Schwert and Seguin approach, and the Kalman filter method. These approaches are applied to a set of monthly Morgan Stanley country index data over the period 1970 to 1995 and their relative performances compared. In-sample forecast tests of the performance of each of these methods for generating conditional beta suggest that the GARCH-based estimates of risk generate the lowest forecast error although these are not necessarily significantly less than those generated by the other techniques considered.     

话音的频域编码

本文阐述了语言信号的子带及变换编码原理,为了消除分块效应,介绍了一种作为语言编码新方法的搭接式变换和与此有关的快速算法及滤波器设计。最后,对于为实现最佳比特分配所需的提取副信息的一种方法作了简要的说明。     

一种基于预测值量测转换的卡尔曼滤波跟踪算法

在雷达目标跟踪中,系统量测信息通常在球坐标系下获得。为了采用经典卡尔曼滤波算法实现有效目标跟踪,通常采用量测转换方法将非线性量测信息转换到直角坐标系中。针对传统量测转换方法基于量测值计算转换误差统计特性而导致的估计结果有偏问题,提出了一种基于预测值的量测转换方法,并将其与卡尔曼滤波算法相结合,获得了一种基于预测值量测转换的卡尔曼滤波跟踪算法。仿真结果表明,与现有的基于量测转换的卡尔曼滤波算法相比,该算法能在不提高运算量的情况下有效改善目标跟踪效果,跟踪精度提升约20%。     

一种适用于机场场面监视MLAT系统的状态模型

根据直角坐标系的目标运动模型和无源多点时差定位系统(MLAT)的特点,推导出了一种到达时间差（TDOA)状态模型方程,并基于该模型方程,设计了一种TDOA跟踪滤波器。与传统TDOA状态模型方程相比,所推导出的状态模型方程和滤波器不但能提高TDOA的估计精度,还可更有效地消除非视距（NLOS）成分。仿真和实测数据证实了所推导状态模型方程和所设计跟踪滤波器的有效性。     

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.     

Modelling the implied volatility surface based on Shanghai 50ETF options

We develop a dynamic factor model to forecast the implied volatility surface (IVS) of Shanghai Stock Exchange 50ETF options. Based on the assumption that dynamic change in IVS is mean-reverting and Markovian, we use a state space model to capture the dynamics of IVS, and set the latent factors to be the Ornstein–Uhlenbeck processes. We obtain the optimal estimations of parameters using the Kalman filter algorithm. Empirical results show that our model performs better than the traditional IVS model in terms of fitting ability and prediction performance.     

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.     

北斗/CDMA组合导航数据融合及其在军事物流中的应用研究

提出了一种新的组合导航定位技术-北斗/CDMA导航定位,进一步研究了如何利用数据融合技术提高定位精度与可靠性。设计了一种简化的联邦卡尔曼滤波器并给出了相应的滤波算法流程。最后提出了北斗/CDMA组合导航定位技术在军事物流监控调度系统中的应用解决方案。