Estimation of the potential GDP by a new robust filter method

Éva Gyurkovics () and Tibor Takacs
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Éva Gyurkovics: Budapest University of Technology and Economics

Central European Journal of Operations Research, 2023, vol. 31, issue 4, No 8, 1183-1207

Abstract: Abstract The first purpose of this paper is to propose a theoretically new robust filter method to estimate non-observable macroeconomic indicators. The second purpose is to apply the proposed method to estimate the Hungarian potential GDP in 2000–2021. The novelty of the proposed filter method is that — unlike papers published so far — it does not require the stability of the dynamic model, only a partial stability condition must be satisfied. Moreover, such time-dependent uncertainties and nonlinearities can arise in the model that satisfy a general quadratic constraint. An important advantage of the proposed robust filter method over the traditional Kalman filter is that no stochastic assumptions is needed that may not be valid for the problem at hand. The proposed filter method has never been applied to estimate the potential GDP. To estimate the Hungarian potential GDP, the proposed method is applied using uni-, bi- and trivariate models. Estimations up to 2021 has not been published yet for the Hungarian economy. The examined period includes both the financial world crisis and the Covid-19 crisis. The results of the different models are consistent. It turned out that the economic policy was very procyclical after 2012, and the GDP gap was still positive during and also after the Covid-19 crisis.

Keywords: Potential GDP; Robust filtering; Polytopic and quadratically bounded uncertainties; Linear matrix inequality; Unobserved components model; Trend-cycle decomposition (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C52 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10100-023-00851-7

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