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Forecast Comparison of Models Based on SARIMA and the Kalman Filter for Inflation
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Inflation is one of the most important macroeconomic variables. It is vital that policy makers receive accurate forecasts of inflation so that they can adjust their monetary policy to attain stability in the economy which has been shown to lead to economic growth. The purpose of this study is to model inflation and evaluate if applying the Kalman filter to SARIMA models lead to higher forecast accuracy compared to just using the SARIMA model. The Box-Jenkins approach to SARIMA modelling is used to obtain well-fitted SARIMA models and then to use a subset of observations to estimate a SARIMA model on which the Kalman filter is applied for the rest of the observations. These models are identified and then estimated with the use of monthly inflation for Luxembourg, Mexico, Portugal and Switzerland with the target to use them for forecasting. The accuracy of the forecasts are then evaluated with the error measures mean squared error (MSE), mean average deviation (MAD), mean average percentage error (MAPE) and the statistic Theil's U. For all countries these measures indicate that the Kalman filtered model yield more accurate forecasts. The significance of these differences are then evaluated with the Diebold-Mariano test for which only the difference in forecast accuracy of Swiss inflation is proven significant. Thus, applying the Kalman filter to SARIMA models with the target to obtain forecasts of monthly inflation seem to lead to higher or at least not lower predictive accuracy for the monthly inflation of these countries.

Place, publisher, year, edition, pages
2013. , 66 p.
Keyword [en]
Inflation, SARIMA model, Hyndman-Khandakar algorithm, State-Space models, Kalman filter, Forecast comparison, Diebold-Mariano test.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-202204OAI: oai:DiVA.org:uu-202204DiVA: diva2:631413
Subject / course
Statistics
Educational program
Master Programme in Statistics
Presentation
2013-05-29, Ekonomikum, Room F332, Kyrkogårdsgatan 10, Uppsala, 13:50 (English)
Supervisors
Examiners
Available from: 2013-06-24 Created: 2013-06-20 Last updated: 2013-06-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
  • de-DE
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  • en-US
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More languages
Output format
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