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Inflation Expectations in India: Learning from Household Tendency Surveys

Author

Listed:
  • Abhiman Das

    (Indian Institute of Management Ahmedabad)

  • Kajal Lahiri

    (Department of Economics, University at Albany, State University of New York)

  • Yongchen Zhao

    (Department of Economics, Towson University)

Abstract
Using a large household survey conducted by the Reserve Bank of India since 2005, we 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. For quantification, we use 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 disagreement among households. The proposed approach is particularly suitable in emerging economies where inflation tends to be high and volatile.

Suggested Citation

  • Abhiman Das & Kajal Lahiri & Yongchen Zhao, 2018. "Inflation Expectations in India: Learning from Household Tendency Surveys," Working Papers 2018-03, Towson University, Department of Economics, revised Aug 2018.
    • Handle: RePEc:tow:wpaper:2018-03
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    References listed on IDEAS

    as
    1. Dasgupta, Susmita & Lahiri, Kajal, 1992. "A Comparative Study of Alternative Methods of Quantifying Qualitative Survey Responses Using NAPM Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 391-400, October.
    2. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    3. Lahiri, Kajal & Monokroussos, George, 2013. "Nowcasting US GDP: The role of ISM business surveys," International Journal of Forecasting, Elsevier, vol. 29(4), pages 644-658.
    4. Easaw, Joshy & Golinelli, Roberto & Malgarini, Marco, 2013. "What determines households inflation expectations? Theory and evidence from a household survey," European Economic Review, Elsevier, vol. 61(C), pages 1-13.
    5. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment Over Business Cycles: Evidence from the US Surveys of Consumers," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 187-215, December.
    6. Krkoska, Libor & Teksoz, Utku, 2007. "Accuracy of GDP growth forecasts for transition countries: Ten years of forecasting assessed," International Journal of Forecasting, Elsevier, vol. 23(1), pages 29-45.
    7. Tommaso Proietti & Cecilia Frale, 2011. "New proposals for the quantification of qualitative survey data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(4), pages 393-408, July.
    8. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially-Biased Statistics: Household Inflation Perceptions and Expectations in Argentina," NBER Working Papers 22103, National Bureau of Economic Research, Inc.
    9. Kaufmann, Daniel & Scheufele, Rolf, 2017. "Business tendency surveys and macroeconomic fluctuations," International Journal of Forecasting, Elsevier, vol. 33(4), pages 878-893.
    10. Breitung, Jörg & Schmeling, Maik, 2013. "Quantifying survey expectations: What’s wrong with the probability approach?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 142-154.
    11. Ash, J. C. K. & Smyth, D. J. & Heravi, S. M., 1998. "Are OECD forecasts rational and useful?: a directional analysis," International Journal of Forecasting, Elsevier, vol. 14(3), pages 381-391, September.
    12. Taniya Ghosh & Sohini Sahu & Siddhartha Chattopadhyay, 2017. "Households' Inflation Expectations in India: Role of Economic Policy Uncertainty and Global Financial Uncertainty Spill-over," Working Papers id:11890, eSocialSciences.
    13. R?diger Bachmann & Steffen Elstner & Eric R. Sims, 2013. "Uncertainty and Economic Activity: Evidence from Business Survey Data," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 217-249, April.
    14. Michela Nardo, 2003. "The Quantification of Qualitative Survey Data: A Critical Assessment," Journal of Economic Surveys, Wiley Blackwell, vol. 17(5), pages 645-668, December.
    15. Dräger, Lena, 2016. "Recursive Inattentiveness With Heterogeneous Expectations," Macroeconomic Dynamics, Cambridge University Press, vol. 20(4), pages 1073-1100, June.
    16. Richard Curtin, 2007. "Consumer Sentiment Surveys: Worldwide Review and Assessment," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(1), pages 7-42.
    17. Ball, Laurence & Chari, Anusha & Mishra, Prachi, 2016. "Understanding Inflation in India," India Policy Forum, National Council of Applied Economic Research, vol. 12(1), pages 1-45.
    18. Yuichiro Ito & Sohei Kaihatsu, 2016. "Effects of Inflation and Wage Expectations on Consumer Spending: Evidence from Micro Data," Bank of Japan Working Paper Series 16-E-7, Bank of Japan.
    19. Lahiri, Kajal & Zhao, Yongchen, 2015. "Quantifying survey expectations: A critical review and generalization of the Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 31(1), pages 51-62.
    20. Kishor, N. Kundan, 2011. "Data revisions in India: Implications for monetary policy," Journal of Asian Economics, Elsevier, vol. 22(2), pages 164-173, April.
    21. Rina Rosenblatt-Wisch & Rolf Scheufele, 2015. "Quantification and characteristics of household inflation expectations in Switzerland," Applied Economics, Taylor & Francis Journals, vol. 47(26), pages 2699-2716, June.
    22. Michael Debabrata Patra & Partha Ray, 2010. "Inflation Expectations and Monetary Policy in India: An Empirical Exploration," IMF Working Papers 2010/084, International Monetary Fund.
    23. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 59-108.
    24. Vermeulen, Philip, 2014. "An evaluation of business survey indices for short-term forecasting: Balance method versus Carlson–Parkin method," International Journal of Forecasting, Elsevier, vol. 30(4), pages 882-897.
    25. repec:bla:econom:v:42:y:1975:i:166:p:123-38 is not listed on IDEAS
    26. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2016. "Learning from Potentially Biased Statistics," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 52(1 (Spring), pages 59-108.
    27. Fishe, Raymond P. H. & Lahiri, Kajal, 1981. "On the estimation of inflationary expectations from qualitative responses," Journal of Econometrics, Elsevier, vol. 16(1), pages 89-102, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Pooja Kapoor & Sujata Kar, 2022. "A Critical Evaluation of the Consumer Confidence Survey from India," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 172-198.
    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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