scholarly journals Estimation of Uncertainty in Mortality Projections Using State-Space Lee-Carter Model

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1053 ◽  
Author(s):  
Rokas Gylys ◽  
Jonas Šiaulys
Keyword(s):  
State Space ◽  
Regime Switching ◽  
State Space Model ◽  
Gaussian State ◽  
Space Model ◽  
Mortality Projections ◽  
Markovian Regime Switching ◽  
Carter Model ◽  
Markovian Regime ◽  
Swedish Data

The study develops alternatives of the classical Lee-Carter stochastic mortality model in assessment of uncertainty of mortality rates forecasts. We use the Lee-Carter model expressed as linear Gaussian state-space model or state-space model with Markovian regime-switching to derive coherent estimates of parameters and to introduce additional flexibility required to capture change in trend and non-Gaussian volatility of mortality improvements. For model-fitting, we use a Bayesian Gibbs sampler. We illustrate the application of the models by deriving the confidence intervals of mortality projections using Lithuanian and Swedish data. The results show that state-space model with Markovian regime-switching adequately captures the effect of pandemic, which is present in the Swedish data. However, it is less suitable to model less sharp but more prolonged fluctuations of mortality trends in Lithuania.

PMCMC for Term Structure of Interest Rates under Markov Regime Switching and Jumps

2020 ◽  
Vol 8 (2) ◽  
pp. 159-169
Author(s):  
Xiangdong Liu ◽  
Xianglong Li ◽  
Shaozhi Zheng ◽  
Hangyong Qian
Keyword(s):  
Parameter Estimation ◽  
Interest Rates ◽  
Closed Form ◽  
State Space ◽  
Term Structure ◽  
Regime Switching ◽  
State Space Model ◽  
Gaussian State ◽  
Space Model ◽  
Non Gaussian

AbstractA parameter estimation method, called PMCMC in this paper, is proposed to estimate a continuous-time model of the term structure of interests under Markov regime switching and jumps. There is a closed form solution to term structure of interest rates under Markov regime. However, the model is extended to be a CKLS model with non-closed form solutions which is a typical nonlinear and non-Gaussian state-space model(SSM) in the case of adding jumps. Although the difficulty of parameter estimation greatly prevents from researching models, we prove that the nonlinear and non-Gaussian state-space model has better performances in studying volatility. The method proposed in this paper will be implemented in simulation and empirical study for SHIBOR. Empirical results illustrate that the PMCMC algorithm has powerful advantages in tackling the models.

scholarly journals Estimation of N*0 for the Two-Scale Gamma Raindrop Size Distribution Model and Its Statistical Properties at Several Locations in Asia

2010 ◽  
Vol 49 (4) ◽  
pp. 676-686 ◽  
Author(s):  
Toshiaki Kozu ◽  
Kazuhiro Masuzawa ◽  
Toyoshi Shimomai ◽  
Nobuhisa Kashiwagi
Keyword(s):  
Time Series ◽  
State Space ◽  
Size Distribution ◽  
State Space Model ◽  
Statistical Properties ◽  
Gaussian State ◽  
Space Model ◽  
The Third ◽  
Raindrop Size Distribution ◽  
Raindrop Size

Abstract An automatic estimation method is developed to detect stepwise changes in the amplitude parameter of the normalized raindrop size distribution (DSD) N*0. To estimate N*0, it is also assumed that the variation of three DSD parameters follows the two-scale gamma DSD model; this is defined as a DSD model in which one DSD parameter is fixed, the second is allowed to vary rapidly, and the third is constant over a certain space or time domain and sometimes exhibits stepwise transitions. For this study, it is assumed that N*0 is the third DSD parameter. To estimate this stepwise-varying parameter automatically, a non-Gaussian state-space model is used for the time series of log10N*0. The smoothed time series of log10N*0 fit well to the stepwise transition of log10N*0 when it was assumed that the state transition probability follows a Cauchy distribution. By analyzing the long-term disdrometer data using this state-space model, statistical properties for log10N*0 are obtained at several Asian locations. It is confirmed that the N*0 thus estimated is useful to improve the rain-rate estimation from the measurement of radar reflectivity factor.

Data-based linear Gaussian state-space model for dynamic process monitoring

AIChE Journal ◽  
2012 ◽  
Vol 58 (12) ◽  
pp. 3763-3776 ◽  
Author(s):  
Qiaojun Wen ◽  
Zhiqiang Ge ◽  
Zhihuan Song
Keyword(s):  
State Space ◽  
Process Monitoring ◽  
Dynamic Process ◽  
State Space Model ◽  
Gaussian State ◽  
Space Model
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