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Inflation expectations in India: Learning from household tendency surveys

Author

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  • Das, Abhiman
  • Lahiri, Kajal
  • Zhao, Yongchen
Abstract
We use a large household survey that is being conducted by the Reserve Bank of India since 2005 to estimate the dynamics of aggregate inflation expectations over a volatile inflation regime. A simple average of the quantitative responses produces biased estimates of the official inflation data. We therefore estimate expectations by quantifying the reported directional responses. We perform quantification by using the hierarchical ordered probit model, in addition to the balance statistic. We find that the quantified expectations from qualitative forecasts track the actual inflation rate better than the averages of the quantitative forecasts, highlighting the filtering role of qualitative tendency surveys. We also report estimates of the disagreement among households. The proposed approach is particularly suitable in emerging economies, where inflation tends to be high and volatile.

Suggested Citation

  • Das, Abhiman & Lahiri, Kajal & Zhao, Yongchen, 2019. "Inflation expectations in India: Learning from household tendency surveys," International Journal of Forecasting, Elsevier, vol. 35(3), pages 980-993.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:3:p:980-993
    DOI: 10.1016/j.ijforecast.2019.03.007
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    Cited by:

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    2. Ashima Goyal & Prashant Mehul Parab, 2019. "Modeling Consumers' Confidence and Inflation Expectations," Economics Bulletin, AccessEcon, vol. 39(3), pages 1817-1832.
    3. Oscar Claveria, 2020. "Business and consumer uncertainty in the face of the pandemic: A sector analysis in European countries," Papers 2012.02091, arXiv.org.
    4. Pijush Kanti Das & Prabir Kumar Das, 2024. "Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 493-517, June.
    5. Yongchen Zhao, 2022. "Uncertainty and disagreement of inflation expectations: Evidence from household‐level qualitative survey responses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 810-828, July.
    6. Sheen, Jeffrey & Wang, Ben Zhe, 2021. "Measuring macroeconomic disagreement – A mixed frequency approach," Journal of Economic Behavior & Organization, Elsevier, vol. 189(C), pages 547-566.
    7. Gaurav Kumar Singh & Tathagata Bandyopadhyay, 2024. "Determinants of disagreement: Learning from inflation expectations survey of households," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 326-343, March.
    8. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    9. Ashima Goyal & Prashant Parab, 2019. "Modeling heterogeneity and rationality of inflation expectations across Indian households," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2019-02, Indira Gandhi Institute of Development Research, Mumbai, India.
    10. Taniya Ghosh & Abhishek Gorsi, 2024. "Inflation expectations and keeping up with the Joneses," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2024-018, Indira Gandhi Institute of Development Research, Mumbai, India.
    11. Young Bin Ahn & Yoichi Tsuchiya, 2022. "Consumer’s perceived and expected inflation in Japan—irrationality or asymmetric loss?," Empirical Economics, Springer, vol. 63(3), pages 1247-1292, September.

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    More about this item

    Keywords

    Hierarchical ordered probit model; Quantification; Tendency survey; Disagreement; Indian inflation;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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