Abstract
The aim of the paper is to provide a recent overview of Kalman filter employment in the non-accelerating inflation rate of unemployment (NAIRU) estimation. The NAIRU plays a key part in an economic system. A certain unemployment rate which is consistent with a stable rate of inflation is one of the conditions for economic system stability. Since the NAIRU cannot be directly observed and measured, it is one of the most fitting problems for the Kalman filter application. The search for original, NAIRU focused and Kalman filter employment studies was performed in three scientific databases: Web of Science, Scopus, and ScienceDirect. A sample of 152 papers was narrowed down to 25 studies, which were described in greater detail regarding the focus, methods, model features, limitations, and other characteristics. A group of studies using a purely statistical approach of decomposing unemployment into a trend and cyclical component was identified. The next group uses the reduced-form approach which is sometimes combined with statistical decomposition. In such cases, the models are usually based on the backward-looking Phillips curve. Nevertheless, the forward-looking, New Keynesian or rarely hybrid New Keynesian variant can also be encountered.
Highlights
The Kalman filter is considered to be a theoretical basis for various recursive methods applied in stochastic dynamic systems
The non-accelerating inflation rate of unemployment (NAIRU) is in its nature an unobservable phenomenon, and the Kalman filter can serve as one of the means for its estimation
It represents the decomposition of unemployment into structural, frictional and cyclical, where the first two components are considered as the NAIRU
Summary
The Kalman filter is considered to be a theoretical basis for various recursive methods applied in stochastic (linear) dynamic systems. The Kalman filter can be applied in varied domains of the prevailingly technical character, as for example in case of the localization of moving objects and navigation—the Kalman filter or Kalman filters in general are used in global navigation satellite systems (GPS, etc.), in radars, in the case of navigation and control of robots, in autopilots or autonomous vehicles, in computer vision for tracking objects in videos, in augmented and virtual reality, etc Their application in the sphere of econometrics cannot be ignored, especially in the case of econometric models in which there is at least one variable which cannot be directly observed and measured
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
