Question: State Space Model是否可用于panel data分析？

Answer: Yes!

A State Space Approach for Estimating VAR Models for Panel Data with Latent Dynamic Components

The econometric literature offers various modeling approaches for analyzing micro data in combination with time series of aggregate data. This paper discusses the estimation of a VAR model that allows unobserved heterogeneity across observation unit, as well as unobserved time-specific variables. The time-latent component is assumed to consist of a persistent and a transient term. By using a Helmert-type orthogonal transformation of the variables it is demonstrated that the likelihood function can be expressed on a state space form. The dimension of the state vector is low and independent of the time and cross section dimensions. This fact makes it convenient to employ an ECM algorithm for estimating the parameters of the model. An empirical application provides new insight into the problem of making forecasts for aggregate variables based on information from micro data.

http://www.ssb.no/publikasjoner/DP/pdf/dp295.pdf

Bayesian Analysis of State Space Models with Panel Data by Ranjita Mishra Department of Statistics, Banasthali Vidyapith, Rajasthan Coauthors: Anoop Chaturvedi (Department of Statistics, University of Allahabad, Allahabad)

The panel data models are gaining popularity among applied researchers as the longitudnal nature of these data can easily handle the complexity of biological, social, economic and enviornmental phenomenon. This paper considers the analysis of State space models with panel data under Bayesian framework. The conditional posterior distributions of various parameters of the model have been obtained under suitable prior assumptions. Gibbs sampler scheme has been proposed to estimate the marginal posterior densities for these parameters.

Simulated Likelihood Estimation of Affine Term Structure Models ...

Gaussian state-space model. For this special case, the assumptions of the Kalman filter are ... affine term structure model from noisy panel data that overcomes the problems of the Kalman. filter based QML estimates. ...

Panel-Data Estimation of Non-Linear Term-Structure Models

The State-Space Model. We assume that the data set consists of M yields of zero-coupon bonds, Z. t. , with ¯xed. time to maturity. ... the panel-data results in a one-factor model are convincing for shorter time horizons as