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Aggregate vs Disaggregate Data Analysis - A Paradox in the Estimation of Money Demand Function of Japan Under the Low Interest Rate Policy

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  • Cheng Hsiao

    (Department of Economics, University of Southern California)

  • Yan Shen

    (Department of Economics, University of Southern California)

  • Hiroshi Fujiki

    (Institute of Monetary and Economic Studies, Bank of Japan)

Abstract

We investigate the issue of whether there was a stable money demand function for Japan in 1990's using both aggregate and disaggregate time series data. The aggregate data appears to support the contention that there was no stable money demand function. The disaggregate data shows that there was a stable money demand function. Neither was there any indication of the presence of liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglect of heterogeneity among micro units. We also conduct simulation analysis to highlight the importance of paying proper attention to heterogeneity among micro units for macro policy analysis and prediction.

Suggested Citation

  • Cheng Hsiao & Yan Shen & Hiroshi Fujiki, 2002. "Aggregate vs Disaggregate Data Analysis - A Paradox in the Estimation of Money Demand Function of Japan Under the Low Interest Rate Policy," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A4-1, International Conferences on Panel Data.
    • Handle: RePEc:cpd:pd2002:a4-1
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    2. Pesaran, M. Hashem & Chudik, Alexander, 2014. "Aggregation in large dynamic panels," Journal of Econometrics, Elsevier, vol. 178(P2), pages 273-285.
    3. Hiroshi Fujiki, 2014. "Japanese Money Demand from the Regional Data: An Update and Some Additional Results," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 32, pages 45-102, November.
    4. Markus Eberhardt & Francis Teal, 2010. "Aggregation versus Heterogeneity in Cross-Country Growth Empirics," CSAE Working Paper Series 2010-32, Centre for the Study of African Economies, University of Oxford.
    5. Fushang Liu & Kajal Lahiri, 2006. "Modelling multi-period inflation uncertainty using a panel of density forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(8), pages 1199-1219.
    6. Trapani, Lorenzo & Urga, Giovanni, 2010. "Micro versus macro cointegration in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 155(1), pages 1-18, March.
    7. Cheng Hsiao, 2016. "Panel Macroeconometric Modeling," Working Papers 2016-02-21, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    8. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    9. Cheng Hsiao & Qi Li & Zhongwen Liang & Wei Xie, 2019. "Panel Data Estimation for Correlated Random Coefficients Models," Econometrics, MDPI, vol. 7(1), pages 1-18, February.
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    11. Rostom,Ahmed Mohamed Tawfick, 2016. "Money demand in the Arab Republic of Egypt : a vector equilibrium correction model," Policy Research Working Paper Series 7679, The World Bank.
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    14. Cheng Hsiao, 2005. "Longitudinal Data Analysis," Economic Growth Centre Working Paper Series 0510, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    15. Jia, Junxue & Guo, Qingwang & Zhang, Jing, 2014. "Fiscal decentralization and local expenditure policy in China," China Economic Review, Elsevier, vol. 28(C), pages 107-122.
    16. Sarah Moon, 2024. "Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model," Papers 2403.07236, arXiv.org, revised May 2024.
    17. Nora A. Mothafar & Jingxiao Zhang & Ibrahim Al-Maqrami, 2022. "The Evolution of Human Development Through the Eyes of ICT in Developing Countries Based on Panel Data from 2007 to 2017," Indian Journal of Human Development, , vol. 16(3), pages 578-601, December.
    18. Stefano Fachin & Andrea Gavosto, 2010. "Trends of labour productivity in Italy: a study with panel co‐integration methods," International Journal of Manpower, Emerald Group Publishing Limited, vol. 31(7), pages 755-769, October.
    19. Song, Nianfu & Chang, Sun Joseph & Aguilar, Francisco X., 2011. "U.S. softwood lumber demand and supply estimation using cointegration in dynamic equations," Journal of Forest Economics, Elsevier, vol. 17(1), pages 19-33, January.
    20. Azeem, Muhammad Masood & Mugera, Amin W. & Schilizzi, Steven, 2016. "Poverty and vulnerability in the Punjab, Pakistan: A multilevel analysis," Journal of Asian Economics, Elsevier, vol. 44(C), pages 57-72.
    21. Helmut Herwartz & Jordi Sardà & Bernd Theilen, 2016. "Money demand and the shadow economy: empirical evidence from OECD countries," Empirical Economics, Springer, vol. 50(4), pages 1627-1645, June.
    22. Barry Abrams & Santharajah Kumaradevan & Vasilis Sarafidis & Frank Spaninks, 2012. "An Econometric Assessment of Pricing Sydney’s Residential Water Use," The Economic Record, The Economic Society of Australia, vol. 88(280), pages 89-105, March.
    23. Hiroshi Fujiki & Cheng Hsiao, 2008. "Aggregate and Household Demand for Money: Evidence from the Public Opinion Survey on Household Financial Assets and Liabilities," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 26, pages 159-194, December.
    24. Qurat ul Ain & Tahir Yousaf & Yan Jie & Yasmeen Akhtar, 2020. "The Impact of Devolution on Government Size and Provision of Social Services: Evi¬dence from Pakistan," Hacienda Pública Española / Review of Public Economics, IEF, vol. 234(3), pages 105-135, September.
    25. Giacomo Sbrana, 2007. "Testing for Model Selection in Predicting Aggregate Variables," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(1), pages 3-28, March.

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