Bridging DSGE models and the raw data

A method to estimate DSGE models using the raw data is proposed. The approach links the observables to the model counterparts via a flexible specification which does not require the model-based component to be located solely at business cycle frequencies, allows the non-model-based component to take various time series patterns, and permits certain types of model misspecification. Applying standard data transformations induces biases in structural estimates and distortions in the policy conclusions. The proposed approach recovers important model-based features in selected experimental designs. Two widely discussed issues are used to illustrate its practical use.     

Testing of nonstationary cycles in financial time series data

In this article we propose a method for testing nonstationary cycles in financial time series data. We use a procedure that permits us to test unit root cycles in raw time series. The test has several distinguishing features compared with other procedures. In particular, it has a standard null limit distribution and is the most efficient test when directed against the appropriate (fractional) alternatives. In addition, it allows us to test unit root cycles at each of the frequencies, and, thus, it permits us to approximate the number of periods per cycle. The results, based on the daily structure of Spanish Stock Market prices (IBEX35), show that some intra-year cycles occur, and they take place at approximately 6, 9 or between 24 and 50 periods. The analysis was extended to several other stock market indices of various countries and though the results differ in terms of frequencies, the same conclusions hold, finding evidence of intra-year cyclical effects in all countries. JEL Classification C22; G14.     

Survival analysis of pension scheme mortality when data are missing

Missing data is a problem that may be faced by actuaries when analysing mortality data. In this paper we deal with pension scheme data, where the future lifetime of each member is modelled by means of parametric survival models incorporating covariates, which may be missing for some individuals. Parameters are estimated by likelihood-based techniques. We analyse statistical issues, such as parameter identifiability, and propose an algorithm to handle the estimation task. Finally, we analyse the financial impact of including covariates maximally, compared with excluding parts of the mortality experience where data are missing; in particular we consider annuity factors and mis-estimation risk capital requirements.     

我国保险业与经济增长关系的实证研究

为研究保险业和经济增长的关系,选取2001～2011年我国大陆30个省份(不含西藏)保险密度和人均GDP的面板数据,分别进行全国以及区域(东部、中部、西部地区)的保险业和经济增长之间的长期、短期及因果关系检验,分组计算经济增长对保险业发展的贡献度,得出如下结论:全国层面经济增长是保险业发展的重要因素,保险业发展又反过来促进了经济增长;区域层面经济增长和保险业发展之间的关系不一致,保险业发展与经济发展不协调。