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Bayesian VAR Models for Forecasting Irish Inflation

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  • Kenny, Geoff
  • Meyler, Aidan
  • Quinn, Terry

Abstract

In this paper we focus on the development of multiple time series models for forecasting Irish Inflation. The Bayesian approach to the estimation of vector autoregressive (VAR) models is employed. This allows the estimated models combine the evidence in the data with any prior information which may also be available. A large selection of inflation indicators are assessed as potential candidates for inclusion in a VAR. The results confirm the significant improvement in forecasting performance which can be obtained by the use of Bayesian techniques. In general, however, forecasts of inflation contain a high degree of uncertainty. The results are also consistent with previous research in the Central Bank of Ireland which stresses a strong role for the exchange rate and foreign prices as a determinant of Irish prices.

Suggested Citation

  • Kenny, Geoff & Meyler, Aidan & Quinn, Terry, 1998. "Bayesian VAR Models for Forecasting Irish Inflation," MPRA Paper 11360, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:11360
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    Cited by:

    1. Quinn, Terry & Kenny, Geoff & Meyler, Aidan, 1999. "Inflation Analysis: An Overview," MPRA Paper 11361, University Library of Munich, Germany.
    2. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    3. Meyler, Aidan & Kenny, Geoff & Quinn, Terry, 1998. "Forecasting irish inflation using ARIMA models," MPRA Paper 11359, University Library of Munich, Germany.
    4. Aleksandra Nocoń, 2020. "Sustainable Approach to the Normalization Process of the UK’s Monetary Policy," Sustainability, MDPI, vol. 12(21), pages 1-14, November.
    5. Matkovskyy, Roman, 2012. "Прогнозування розвитку економіки України на основі баєсівських авторегресійних (BVAR) моделей з різними priors [Forecasting Economic Development of Ukraine based on BVAR models with different prior," MPRA Paper 44725, University Library of Munich, Germany, revised Nov 2012.
    6. Nikola N. Nenovsky, 2023. "Are Monetary Aggregates Good Predictors for the Bulgarian Inflation Rate?," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 483-506.
    7. Rumler, Fabio & Valderrama, Maria Teresa, 2010. "Comparing the New Keynesian Phillips Curve with time series models to forecast inflation," The North American Journal of Economics and Finance, Elsevier, vol. 21(2), pages 126-144, August.
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    9. Cindrella Shah & Nilesh Ghonasgi, 2016. "Determinants and Forecast of Price Level in India: a VAR Framework," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 57-86, June.
    10. EMERSON Abraham Jackson, 2018. "Comparison Between Static And Dynamic Forecast In Autoregressive Integrated Moving Average For Seasonally Adjusted Headline Consumer Price Index," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 70(1), pages 53-65, August.
    11. Lozano, Francisco-Javier, 2013. "Evaluación de modelos de predicción para la venta de viviendas [Evaluation of forecasting models for house sales]," MPRA Paper 118652, University Library of Munich, Germany.
    12. Nyoni, Thabani, 2019. "An ARIMA analysis of the Indian Rupee/USD exchange rate in India," MPRA Paper 96908, University Library of Munich, Germany.
    13. Patricio Jaramillo, 2009. "Estimación de VAR Bayesianos para la Economía Chilena," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 24(1), pages 101-126, Junio.
    14. Ngomba Bodi, Francis Ghislain & Bikai, Landry, 2017. "Prévisions de l’inflation et de la croissance en zone CEMAC [Inflation and real growth forecasts in CEMAC zone]," MPRA Paper 116433, University Library of Munich, Germany.
    15. Matkovskyy, Roman, 2012. "The Index of the Financial Safety (IFS) of South Africa and Bayesian Estimates for IFS Vector-Autoregressive Model," MPRA Paper 42173, University Library of Munich, Germany.
    16. Christian Pinshi, 2020. "COVID-19 uncertainty and monetary policy," Working Papers hal-02566796, HAL.
    17. Meri Papavangjeli, 2019. "Forecasting the Albanian short-term inflation through a Bayesian VAR model," IHEID Working Papers 16-2019, Economics Section, The Graduate Institute of International Studies, revised 09 Oct 2019.
    18. Sebastian Coralia Emilia POPA, 2017. "Quantification model of the consequences of monetary policy shocks," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(19), pages 122-128, November.
    19. Nyoni, Thabani, 2018. "Box-Jenkins ARIMA approach to predicting net FDI inflows in Zimbabwe," MPRA Paper 87737, University Library of Munich, Germany.
    20. PINSHI, Christian P., 2020. "Uncertainty, monetary policy and COVID-19," MPRA Paper 100147, University Library of Munich, Germany.
    21. Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.
    22. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    23. Pinshi, Christian P., 2020. "Monetary policy, uncertainty and COVID-19," MPRA Paper 100836, University Library of Munich, Germany, revised 27 May 2020.
    24. Roma, Moreno & Skudelny, Frauke & Benalal, Nicholai & Diaz del Hoyo, Juan Luis & Landau, Bettina, 2004. "To aggregate or not to aggregate? Euro area inflation forecasting," Working Paper Series 374, European Central Bank.

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    More about this item

    Keywords

    Bayesian; BVAR; inflation forecasts; Ireland;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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