Kalman Filters for Estimating the potential GDP
Journal Title: Journal of Applied Computer Science & Mathematics - Year 2018, Vol 12, Issue 25
Abstract
The estimation of the potential GDP has a twofold importance: on one hand its accurate estimation allows the correct dimensioning of the macroeconomic policies and on the other hand, the study of potential GDP is a research activity allowing a deeper understanding of the economy works. The methods of estimating the potential GDP can be divided into two categories: statistical and structural. Because the potential GDP is unobservable and cannot be derived directly from the statistical data, we used the Kalman Filter (KF) algorithm to estimate it using a model that connects the unobserved with the observed variables. The results were compared to those obtained by applying a Hodrick – Prescott (HP) filter.
Authors and Affiliations
Sorin VLAD, Ionut BALAN
Keywords
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- EP ID EP532967
- DOI 10.4316/JACSM.201801006
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