BAYESIAN INFERENCE FOR STATE SPACE MODEL WITH PANEL DATA
نویسندگان
چکیده
منابع مشابه
Sequential Bayesian Inference for Dynamic State Space Model Parameters
Dynamic state-space models [24], consisting of a latent Markov process X0, X1, . . . and noisy observations Y1, Y2, . . . that are conditionally independent, are used in a wide variety of applications e.g. wireless networks [8], object tracking [21], econometrics [7] etc. The model is specified by an initial distribution p(x0|✓), a transition kernel p(xt|xt 1, ✓) and an observation distribution...
متن کاملBayesian Inference in a Cointegrating Panel Data Model∗
This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deterministic c...
متن کاملBayesian Inference for Random Coefficient Dynamic Panel Data Models
We develop a hierarchical Bayesian approach for inference in random coefficient dynamic panel data models. Our approach allows for the initial values of each unit’s process to be correlated with the unit-specific coefficients. We impose a stationarity assumption for each unit’s process by assuming that the unit-specific autoregressive coefficient is drawn from a logitnormal distribution. Our me...
متن کاملBayesian Quantile Regression with Adaptive Lasso Penalty for Dynamic Panel Data
Dynamic panel data models include the important part of medicine, social and economic studies. Existence of the lagged dependent variable as an explanatory variable is a sensible trait of these models. The estimation problem of these models arises from the correlation between the lagged depended variable and the current disturbance. Recently, quantile regression to analyze dynamic pa...
متن کاملUnified Inference for Variational Bayesian Linear Gaussian State-Space Models
Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics to bioinformatics. The most challenging aspect of implementing the method is in performing inference on the hidden s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics in Transition. New Series
سال: 2016
ISSN: 1234-7655,2450-0291
DOI: 10.21307/stattrans-2016-014