Please use this identifier to cite or link to this item:
http://hdl.handle.net/2123/8337
|
| Title: | Maximum likelihood estimation of time series models: the Kalman filter and beyond |
| Authors: | Proietti, Tommaso Luati, Alessandra Business Analytics |
| Keywords: | state space models missing data non linear models |
| Issue Date: | May-2012 |
| Publisher: | Business Analytics. |
| Series/Report no.: | BAWP 2012_02 |
| Abstract: | The purpose of this chapter is to provide a comprehensive treatment of likelihood inference for state space models. These are a class of time series models relating an observable time series to quantities called states, which are characterized by a simple temporal dependence structure, typically a first order Markov process. The states have sometimes substantial interpretation. Key estimation problems in economics concern latent variables, such as the output gap, potential output, the non-accelerating-inflation rate of unemployment, or NAIRU, core inflation, and so forth. Time-varying volatility, which is quintessential to finance, is an important feature also in macroeconomics. In the multivariate framework relevant features can be common to different series, meaning that the driving forces of a particular feature and/or the transmission mechanism are the same. The objective of this chapter is reviewing this algorithm and discussing maximum likelihood inference, starting from the linear Gaussian case and discussing the extensions to a nonlinear and non Gaussian framework. |
| URI: | http://hdl.handle.net/2123/8337 |
| Department/Unit/Centre: | Business Analytics |
| Type of Work: | Working Paper |
| Appears in Collections: | Working Papers - Business Analytics |
Files in This Item:
|
Items in Sydney eScholarship Repository are protected by copyright, with all rights reserved, unless otherwise indicated.