{"title":"KNW模型的卡尔曼滤波估计","authors":"A. Pelsser","doi":"10.2139/ssrn.3885556","DOIUrl":null,"url":null,"abstract":"This technical note gives implementation notes for estimating the Koijen-Nijman-Werker model from historical data based on a Kalman filter. We provide an independent derivation of the KNW model. We propose a different implementation of the state-space formulation of the KNW model and we test the impact of two different specifications for the initialisation of the Kalman filter maximum-likelihood estimation. By doing so, we provide an independent verification of the parameter estimations provided by DNB for the Committee Parameters. We find that the parameter estimates reported by DNB and our own parameter estimates are very similar.","PeriodicalId":222384,"journal":{"name":"DecisionSciRN: Other Forecasting (Sub-Topic)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Kalman Filter Estimation of the KNW Model\",\"authors\":\"A. Pelsser\",\"doi\":\"10.2139/ssrn.3885556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This technical note gives implementation notes for estimating the Koijen-Nijman-Werker model from historical data based on a Kalman filter. We provide an independent derivation of the KNW model. We propose a different implementation of the state-space formulation of the KNW model and we test the impact of two different specifications for the initialisation of the Kalman filter maximum-likelihood estimation. By doing so, we provide an independent verification of the parameter estimations provided by DNB for the Committee Parameters. We find that the parameter estimates reported by DNB and our own parameter estimates are very similar.\",\"PeriodicalId\":222384,\"journal\":{\"name\":\"DecisionSciRN: Other Forecasting (Sub-Topic)\",\"volume\":\"119 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DecisionSciRN: Other Forecasting (Sub-Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3885556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DecisionSciRN: Other Forecasting (Sub-Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3885556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This technical note gives implementation notes for estimating the Koijen-Nijman-Werker model from historical data based on a Kalman filter. We provide an independent derivation of the KNW model. We propose a different implementation of the state-space formulation of the KNW model and we test the impact of two different specifications for the initialisation of the Kalman filter maximum-likelihood estimation. By doing so, we provide an independent verification of the parameter estimations provided by DNB for the Committee Parameters. We find that the parameter estimates reported by DNB and our own parameter estimates are very similar.