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Modelling Swedish Inflation Using Market Data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modellering av svensk inflation med marknadsdata (Swedish)
Abstract [en]

This study is an attempt to model Swedish CPI inflation using ARIMA and variations of distributed lag model with market data as explanatory variables. The model will be constructed on the CPI subcomponents level and the results are aggregated to the CPI. Three approaches are tested in this report. In the first approach, only ARIMA model is used to model each of the subcomponents. In the second approach we use a distributed lag model (DLM) on subcomponents with significant correlation to the market data, the residual of the DLM is then modelled using ARIMA. In the third approach we use an restricted finite distributed lag model (RFDLM) instead of DLM. The study found that RFDLM was the best approach to model inflation with 20% RMSE compared to 32% of the naive forecast. However, there is little forecast potential using this approach due to the short lag of market data used as input. The model would be most useful in testing CPI inflation scenarios using predictions or assumptions of market data as input.

Abstract [sv]

 

Denna studie är ett försök att modellera svensk inflation genom att använda ARIMA-modell och variationer av distributed lag modell med marknadsdata som förklarande variabler. Modellen är konstrukterad på underkomponents nivå av KPI och sedan aggregerad till KPI. Tre metoder prövas i denna studie. I första metoden modelleras underkomponenterna direkt med ARIMA-modeller. I andra metoden används distributed lag modell (DLM) på underkomponenter med signifikant korrelation till marknadsdata, residualen från DLM modelleras i sin tur med ARIMA-modeller. I den tredje metoden ersätter vi DLM med restricted finite distributed lag modell (RFDLM). Resultaten från studien visar att RFDLM är den bästa metoden att modellera inflationen och hade ett RMSE på 20 %. Detta jämfört med den naiva prognosen som hade en RMSE på 32 %. Däremot har RFDLM inte särskilt mycket praktiskt nytta i prognostisering av inflationen då man behöver marknadsdata för prognosperiod i förhand på grund att modellen använder sig av väldigt korta lagg. Däremot skulle modellen kunna ha nytta i scenario byggande med prognoser and antagande på marknadsdata som input.

Place, publisher, year, edition, pages
2017.
Series
TRITA-MAT-E, 2017:16
National Category
Mathematical Analysis
Identifiers
URN: urn:nbn:se:kth:diva-207012OAI: oai:DiVA.org:kth-207012DiVA: diva2:1105332
External cooperation
Nordea
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2017-06-02Bibliographically approved

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CiteExportLink to record
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