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Forecasting Inflation using Survey Expectations and Target Inflation: Evidence for Brazil and Turkey

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  • Altug, Sumru
  • Çakmaklı, Cem
Abstract
In this paper, we formulate a statistical model of inflation that combines data on survey expectations and the inflation target set by central banks.. Our model produces inflation forecasts that are aligned with survey expectations, thereby integrating the predictive power of the survey expectations together with the baseline model. We further incorporate the inflation target set by the monetary authority to examine the effectiveness of monetary policy in forming inflation expectations and therefore, predicting inflation accurately. Results indicate superior predictive power of the proposed framework compared to the model without survey expectations as well as several popular benchmarks such as the backward and forward looking Phillips curves and naive forecasting rule

Suggested Citation

  • Altug, Sumru & Çakmaklı, Cem, 2015. "Forecasting Inflation using Survey Expectations and Target Inflation: Evidence for Brazil and Turkey," CEPR Discussion Papers 10419, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:10419
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    2. Kenourgios, Dimitris & Papadamou, Stephanos & Dimitriou, Dimitrios & Zopounidis, Constantin, 2020. "Modelling the dynamics of unconventional monetary policies’ impact on professionals’ forecasts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 64(C).
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," AQR Working Papers 201801, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2018.
    4. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "Let the data do the talking: Empirical modelling of survey-based expectations by means of genetic programming," IREA Working Papers 201711, University of Barcelona, Research Institute of Applied Economics, revised May 2017.
    6. Cem Cakmakli & Selva Demiralp, 2020. "A Dynamic Evaluation of Central Bank Credibility," Koç University-TUSIAD Economic Research Forum Working Papers 2015, Koc University-TUSIAD Economic Research Forum.
    7. Nibbering, Didier & Paap, Richard & van der Wel, Michel, 2018. "What do professional forecasters actually predict?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 288-311.
    8. Fabrizio Almeida Marodin & Marcelo Savino Portugal, 2019. "Exchange Rate Pass-Through in Brazil: À Markov Switching DSGE Estimation for the Inflation Targeting Period," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 36-66, March.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2020. "Spectral analysis of business and consumer survey data," IREA Working Papers 202006, University of Barcelona, Research Institute of Applied Economics, revised May 2020.
    10. Dibyendu Maiti & Naveen Kumar & Debajit Jha & Soumyadipta Sarkar, 2024. "Post-COVID Recovery and Long-Run Forecasting of Indian GDP with Factor-Augmented Error Correction Model (FECM)," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1095-1120, March.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2021. "Frequency domain analysis and filtering of business and consumer survey expectations," International Economics, CEPII research center, issue 166, pages 42-57.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    13. Gilberto Boaretto & Marcelo C. Medeiros, 2023. "Forecasting inflation using disaggregates and machine learning," Papers 2308.11173, arXiv.org.
    14. Nyoni, Thabani & Nathaniel, Solomon Prince, 2018. "Modeling rates of inflation in Nigeria: an application of ARMA, ARIMA and GARCH models," MPRA Paper 91351, University Library of Munich, Germany.
    15. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Empirical modelling of survey-based expectations for the design of economic indicators in five European regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 46(2), pages 205-227, May.
    16. Xu, Kun & Cheng, Jian-hua & Xu, Wenli, 2016. "通胀及通胀预期冲击的动态特征分析 [Study on Dynamics of Inflation and Inflation Expectation Shocks in China]," MPRA Paper 71977, University Library of Munich, Germany.
    17. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    18. Faisal Rachman, 2016. "Is Inflation Target Announced by Bank Indonesia the Most Accurate Inflation Forecast?," Economics and Finance in Indonesia, Faculty of Economics and Business, University of Indonesia, vol. 62, pages 98-120, August.
    19. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    20. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    21. Carlos Medel, 2021. "Forecasting Brazilian Inflation with the Hybrid New Keynesian Phillips Curve: Assessing the Predictive Role of Trading Partners," Working Papers Central Bank of Chile 900, Central Bank of Chile.
    22. Tumala, Mohammed M & Olubusoye, Olusanya E & Yaaba, Baba N & Yaya, OlaOluwa S & Akanbi, Olawale B, 2017. "Forecasting Nigerian Inflation using Model Averaging methods: Modelling Frameworks to Central Banks," MPRA Paper 88754, University Library of Munich, Germany, revised Feb 2018.
    23. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.

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    More about this item

    Keywords

    Inflation targeting; State space models; Inflation forecasting; Survey-based expectation; Term structure of inflation expectations;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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