Review of Kalman Filter Employment in the NAIRU Estimation

Fronckova, Katerina; Pražák, Pavel; Soukal, Ivan

doi:10.3390/systems7030033

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…In a state-space model, Kalman filter is also an algorithm for generating a minimum mean square error forecast. The filter is a recursive algorithm for linearly updating the onestep-ahead estimate of the state mean and variance, given new information (that is, measurement knowledge of the system model and statistical descriptions of its inaccuracies, noise, and errors), (Ole, 2015, Fronckova, Prazak, & Soukal (2019.…”

Section: 332b) Kalman Filtermentioning

confidence: 99%

Measuring Nigeria’s output gap: Kalman Filter

Uzoagulu

2022

Preprint

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The deviation between an economy's estimated potential output and its actual level of output is known as the output gap. The goal of assessing the output gap is to determine the scope or limit for long-term non-inflationary growth and to assess policymakers' opinions on macroeconomic policies. Using the state-space model and Kalman filtering, the study used seasonally adjusted quarterly and annual GDP data from Nigeria. In order to state the predictability of output, the paper extended the univerate model to a multivariate model. However, we used the univariate HP filter as a baseline for comparing the output gap. The result shows that the gap size varies depending on the approach applied. The univariate unobserved component utilizing the Kalman filter produces a more robust result than the HP filter for annual and quarterly data. The estimated output gap reveals sensitivity due to the inclusion of unemployment in the quarterly data as well.

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Section: 332b) Kalman Filtermentioning

confidence: 99%

Measuring Nigeria’s output gap: Kalman Filter

Uzoagulu

2022

Preprint

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“…In a state-space model, it is also an algorithm for obtaining a minimum mean square error forecast. The "Kalman filter" is a recursive algorithm for linearly updating the one-step-ahead forecast of the state mean and variance, given new information (that is, measurement knowledge of the system model and statistical descriptions of its inaccuracies, noise, and errors), (Ole, 2015, Fronckova, Prazak, & Soukal (2019.…”

Section: 332b) Kalman Filtermentioning

confidence: 99%

Measuring Nigeria’s output gap: An Application of Kalman Filter

Uzoagulu

Onuigbo

2022

Preprint

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The deviation between an economy's estimated potential output and its actual level of output is known as the output gap. The goal of assessing the output gap is to determine the scope or limit for long-term non-inflationary growth and to assess policymakers' opinions on macroeconomic policies. Using the state-space model and Kalman filtering, the study used seasonally adjusted quarterly and annual GDP data from Nigeria. In order to state the predictability of output, the paper extended the univariate model to a multivariate model. However, we used the univariate Hodrick-Prescott (HP) filter as a baseline for comparing the output gap. The result shows that the gap size varies depending on the approach applied. As a result, the model shows that inflation outperforms unemployment as the quantitative measure of Nigeria's output gap. The results were satisfactory, and the univariate unobserved component utilizing the Kalman filter, surprisingly, yielded significant outcomes toward the output gap. The output gap estimation also reveals sensitivity due to the inclusion of unemployment in the quarterly data. This implies that policymakers should avoid depending on one approach of estimating output gap.

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“…By using this filter, the logarithmic likelihood function of the state-space model can be estimated and, thus, parameters of the model areAlichi et al (2017) shows that the use of additional exogenous variables contributes to reducing the sensitivity of the model. For a systematic review of the literature related to the summary results of the studies carried out on this subject,Richardson et al (2019) andFronckova et al (2019) can be suggested.Recent studies on the structural unemployment and Beveridge curve in the Turkish economy is limited. Us (2014) estimates NAIRU and a time-varying Phillips curve for the Turkish economy by system approach for 2000Q1-2013Q3.…”

mentioning

confidence: 99%

Türki̇ye’de İşsi̇zli̇ği̇n Ne Kadari Yapisal? Gözlemlenemeyen Bi̇leşenler Yaklaşimi

Şiklar

Bari

2020

Yönetim Ve Ekonomi Araştırmaları Dergisi

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This study aims to determine the course of structural unemployment by decomposing trend and cyclical components of 1987-2019 actual unemployment in Turkey. This decomposition will be carried out in the context of time-varying NAIRU (Non-Accelerating Inflation Rate of Unemployment). It is accepted that structural unemployment is the unemployment that is consistent with the performance of the economy at the potential output level and, therefore, can be proxied by the time-varying NAIRU. The developments in Turkish labor markets during the investigation period emphasize that the importance of structural factors besides the cyclical ones. Therefore, it is crucial to examine how much of the increase in the unemployment rate is structural in the Turkish economy. Structural unemployment rate figures obtained through the state-space model estimated by using reduced form accelerationist Phillips curve indicate the presence of high and increasing structural unemployment problems in Turkey, specifically during the last years. Alternative models like Beveridge curve and Hodrick-Prescott filtering also produce results confirming this determination and reveal that the unemployment gap is almost closing in the country.

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Review of Kalman Filter Employment in the NAIRU Estimation

Cited by 3 publications

References 27 publications

Measuring Nigeria’s output gap: Kalman Filter

Measuring Nigeria’s output gap: Kalman Filter

Measuring Nigeria’s output gap: An Application of Kalman Filter

Türki̇ye’de İşsi̇zli̇ği̇n Ne Kadari Yapisal? Gözlemlenemeyen Bi̇leşenler Yaklaşimi

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