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Aggregate vs. disaggregate data analysis-a paradox in the estimation of a money demand function of Japan under the low interest rate policy

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  • Yan Shen
  • Cheng Hsiao
  • Hiroshi Fujiki

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

Abstract

We use Japanese aggregate and disaggregate money demand data to show that conflicting inferences can arise. 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 a liquidity trap. Possible sources of discrepancy are explored and the diametrically opposite results between the aggregate and disaggregate analysis are attributed to the neglected heterogeneity among micro units. We provide necessary and sufficient conditions for the existence of a cointegrating relation among aggregate variables when heterogeneous cointegration relations among micro units exist. We also conduct simulation analysis to show that when such conditions are violated, it is possible to observe stable micro relations, but unit root phenomena among macro variables. Moreover, the prediction of aggregate outcomes, using aggregate data, is less accurate than the prediction based on micro equations, and policy evaluation based on aggregate data ignoring heterogeneity in micro units can be grossly misleading. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Yan Shen & Cheng Hsiao & Hiroshi Fujiki, 2005. "Aggregate vs. disaggregate data analysis-a paradox in the estimation of a money demand function of Japan under the low interest rate policy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 579-601.
  • Handle: RePEc:jae:japmet:v:20:y:2005:i:5:p:579-601
    DOI: 10.1002/jae.806
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    1. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," CORE Discussion Papers 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    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, "undated". "Aggregation versus Heterogeneity in Cross-Country Growth Empirics," Discussion Papers 11/08, University of Nottingham, CREDIT.
    5. Trapani, Lorenzo & Urga, Giovanni, 2010. "Micro versus macro cointegration in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 155(1), pages 1-18, March.
    6. Cheng Hsiao, 2016. "Panel Macroeconometric Modeling," WISE Working Papers 2016-02-21, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    7. 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.
    8. Jun Nagayasu, 2012. "Financial innovation and regional money," Applied Economics, Taylor & Francis Journals, vol. 44(35), pages 4617-4629, December.
    9. 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.
    10. Talha Yalta, A. & Cakar, Hatice, 2012. "Energy consumption and economic growth in China: A reconciliation," Energy Policy, Elsevier, vol. 41(C), pages 666-675.
    11. Kausik Chaudhuri & Payel Chowdhury & Subal Kumbhakar, 2015. "Crime in India: specification and estimation of violent crime index," Journal of Productivity Analysis, Springer, vol. 43(1), pages 13-28, February.
    12. Cheng Hsiao, 2005. "Longitudinal Data Analysis," Economic Growth Centre Working Paper Series 0510, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    13. 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.
    14. 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.
    15. 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, vol. 31(7), pages 755-769, October.
    16. 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.
    17. 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.
    18. 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.
    19. Hiroshi Fujiki & Cheng Hsiao, 2008. "Aggregate and Household Demand for Money: Evidence from Public Opinion Survey on Household Financial Assets and Liabilities," IMES Discussion Paper Series 08-E-17, Institute for Monetary and Economic Studies, Bank of Japan.
    20. 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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