Estimating aggregate autoregressive processes when only macro data are available

Eric Jondeau and Florian Pelgrin

The aggregation of individual random AR(1) models generally leads to an AR(∞∞) process. We provide two consistent estimators of aggregate dynamics based on either a parametric regression or a minimum distance approach for use when only macro data are available. Notably, both estimators allow us to recover some moments of the cross-sectional distribution of the autoregressive parameter. Both estimators perform very well in our Monte-Carlo experiment, even with finite samples

MoreLess

Year of publication:	2014
Authors:	Jondeau, Eric ; Pelgrin, Florian
Publisher:	Genève : Swiss Finance Inst.
Subject:	Autoregressive process \| Aggregation \| Heterogeneity \| Schätztheorie \| Estimation theory \| Autokorrelation \| Autocorrelation \| Zeitreihenanalyse \| Time series analysis

freely available

Full text |

More access options

doi.org

Check Google Scholar

In German libraries (KVK)

I need help

Extent:	Online-Ressource (22 S.) graph. Darst.
Series:	Research paper series / Swiss Finance Institute. - Geneva, ZDB-ID 2392286-2. - Vol. 14,43
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Arbeitspapier ; Working Paper ; Graue Literatur ; Non-commercial literature
Language:	English
Other identifiers:	10.2139/ssrn.2464621 [DOI]
Classification:	C2 - Econometric Methods: Single Equation Models ; C13 - Estimation
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10010412648