IDEAS home Printed from https://ideas.repec.org/a/jae/japmet/v20y2005i5p579-601.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1002/jae.806
    File Function: Link to full text; subscription required
    Download Restriction: no
    File URL: http://qed.econ.queensu.ca:80/jae/2005-v20.5/
    File Function: Supporting data files and programs
    Download Restriction: no
    File URL: https://libkey.io/10.1002/jae.806?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    2. Cheng Hsiao, 1997. "Statistical Properties of the Two-Stage Least Squares Estimator Under Cointegration," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(3), pages 385-398.
    3. Hashem Pesaran, M., 2003. "Aggregation of linear dynamic models: an application to life-cycle consumption models under habit formation," Economic Modelling, Elsevier, vol. 20(2), pages 383-415, March.
    4. Stoker, Thomas M, 1993. "Empirical Approaches to the Problem of Aggregation Over Individuals," Journal of Economic Literature, American Economic Association, vol. 31(4), pages 1827-1874, December.
    5. Fujiki, Hiroshi & Hsiao, Cheng & Shen, Yan, 2002. "Is There a Stable Money Demand Function under the Low Interest Rate Policy? A Panel Data Analysis," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 20(2), pages 1-23, April.
    6. Poirier, Dale J & Melino, Angelo, 1978. "A Note on the Interpretation of Regression Coefficients within a Class of Truncated Distributions," Econometrica, Econometric Society, vol. 46(5), pages 1207-1209, September.
    7. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    8. Stephen M. Goldfeld, 1973. "The Demand for Money Revisited," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 4(3), pages 577-646.
    9. Fujiki, Hiroshi & Okina, Kunio & Shiratsuka, Shigenori, 2001. "Monetary Policy under Zero Interest Rate: Viewpoints of Central Bank Economists," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 19(1), pages 89-130, February.
    10. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    11. Forni, Mario & Lippi, Marco, 1997. "Aggregation and the Microfoundations of Dynamic Macroeconomics," OUP Catalogue, Oxford University Press, number 9780198288008.
    12. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    13. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1975. "Transcendental Logarithmic Utility Functions," American Economic Review, American Economic Association, vol. 65(3), pages 367-383, June.
    14. Karim Abadir & Gabriel Talmain, 2002. "Aggregation, Persistence and Volatility in a Macro Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 749-779.
    15. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    16. Kajal Lahiri, 2005. "Analysis of Panel Data," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(4), pages 1093-1095.
    17. Bohn, Henning, 1995. "The Sustainability of Budget Deficits in a Stochastic Economy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 27(1), pages 257-271, February.
    18. Anderson, T. W., 2002. "Reduced rank regression in cointegrated models," Journal of Econometrics, Elsevier, vol. 106(2), pages 203-216, February.
    19. Goldfeld, Stephen M. & Sichel, Daniel E., 1990. "The demand for money," Handbook of Monetary Economics, in: B. M. Friedman & F. H. Hahn (ed.), Handbook of Monetary Economics, edition 1, volume 1, chapter 8, pages 299-356, Elsevier.
    20. Arthur Lewbel, 1992. "Aggregation with Log-Linear Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(3), pages 635-642.
    21. Zellner, Arnold, 1996. "Models, prior information, and Bayesian analysis," Journal of Econometrics, Elsevier, vol. 75(1), pages 51-68, November.
    22. Pesaran, M Hashem & Pierse, Richard G & Kumar, Mohan S, 1989. "Econometric Analysis of Aggregation in the Context of Linear Prediction Models," Econometrica, Econometric Society, vol. 57(4), pages 861-888, July.
    23. Forni, Mario & Lippi, Marco, 1999. "Aggregation of linear dynamic microeconomic models," Journal of Mathematical Economics, Elsevier, vol. 31(1), pages 131-158, February.
    24. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    25. Lewbel, Arthur, 1994. "Aggregation and Simple Dynamics," American Economic Review, American Economic Association, vol. 84(4), pages 905-918, September.
    26. Bennett T. McCallum & Marvin S. Goodfriend, 1987. "Money: Theoretical Analysis of the Demand for Money," NBER Working Papers 2157, National Bureau of Economic Research, Inc.
    27. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
    28. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    29. P. K. Trivedi, 1985. "Distributed Lags, Aggregation and Compounding: Some Econometric Implications," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 52(1), pages 19-35.
    30. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    31. Friedman, Milton & Schwartz, Anna J, 1991. "Alternative Approaches to Analyzing Economic Data," American Economic Review, American Economic Association, vol. 81(1), pages 39-49, March.
    32. Cheng Hsiao, 1997. "Cointegration and Dynamic Simultaneous Equations Model," Econometrica, Econometric Society, vol. 65(3), pages 647-670, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 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. Trapani, Lorenzo & Urga, Giovanni, 2010. "Micro versus macro cointegration in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 155(1), pages 1-18, March.
    4. Cheng Hsiao, 2016. "Panel Macroeconometric Modeling," Working Papers 2016-02-21, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    5. 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.
    6. 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.
    7. Jun Nagayasu, 2012. "Financial innovation and regional money," Applied Economics, Taylor & Francis Journals, vol. 44(35), pages 4617-4629, December.
    8. 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.
    9. 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.
    10. Cheng Hsiao, 2005. "Longitudinal Data Analysis," Economic Growth Centre Working Paper Series 0510, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Talha Yalta, A. & Cakar, Hatice, 2012. "Energy consumption and economic growth in China: A reconciliation," Energy Policy, Elsevier, vol. 41(C), pages 666-675.
    21. Sarah Moon, 2024. "Partial Identification of Individual-Level Parameters Using Aggregate Data in a Nonparametric Model," Papers 2403.07236, arXiv.org, revised May 2024.
    22. 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.
    23. 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.
    24. 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.
    25. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pesaran, M. Hashem & Chudik, Alexander, 2014. "Aggregation in large dynamic panels," Journal of Econometrics, Elsevier, vol. 178(P2), pages 273-285.
    2. 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.
    3. Jean Imbs & Eric Jondeau & Florian Pelgrin, 2006. "Aggregating Phillips curves," 2006 Meeting Papers 640, Society for Economic Dynamics.
    4. Stephan von Cramon-Taubadel & Jens-Peter Loy & Jochen Meyer, 2006. "The impact of cross-sectional data aggregation on the measurement of vertical price transmission: An experiment with German food prices," Agribusiness, John Wiley & Sons, Ltd., vol. 22(4), pages 505-522.
    5. Geweke, J. & Joel Horowitz & Pesaran, M.H., 2006. "Econometrics: A Bird’s Eye View," Cambridge Working Papers in Economics 0655, Faculty of Economics, University of Cambridge.
    6. Kajal Lahiri & Fushang Liu, 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, December.
    7. Balaguer, Jacint & Ripollés, Jordi, 2016. "Asymmetric fuel price responses under heterogeneity," Energy Economics, Elsevier, vol. 54(C), pages 281-290.
    8. von Cramon-Taubadel, Stephan & Loy, Jens-Peter & Meyer, Jochen, 2003. "The Impact Of Data Aggregation On The Measurement Of Vertical Price Transmission: Evidence From German Food Prices," 2003 Annual meeting, July 27-30, Montreal, Canada 21987, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Forni, Mario & Lippi, Marco, 1999. "Aggregation of linear dynamic microeconomic models," Journal of Mathematical Economics, Elsevier, vol. 31(1), pages 131-158, February.
    10. Avouyi-Dovi, S. & Diop, A. & Fonteny, E-C. & Gervais, E. & Jacquinot, P. & Mésonnier, J-S. & Sahuc, J-G., 2003. "Estimation d’une fonction de demande de monnaie pour la zone euro : une synthèse des résultats," Bulletin de la Banque de France, Banque de France, issue 111, pages 47-72.
    11. Bettina Becker & Stephen Hall, 2013. "Do R&D strategies in high-tech sectors differ from those in low-tech sectors? An alternative approach to testing the pooling assumption," Economic Change and Restructuring, Springer, vol. 46(2), pages 183-202, May.
    12. Trapani, Lorenzo & Urga, Giovanni, 2010. "Micro versus macro cointegration in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 155(1), pages 1-18, March.
    13. Jondeau, Eric & Pelgrin, Florian, 2014. "Estimating aggregate autoregressive processes when only macro data are available," Economics Letters, Elsevier, vol. 124(3), pages 341-347.
    14. repec:wyi:journl:002076 is not listed on IDEAS
    15. Hsiao, Cheng & Fujiki, Hiroshi, 1998. "Nonstationary Time-Series Modeling versus Structural Equation Modeling: With an Application to Japanese Money Demand," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 16(1), pages 57-79, May.
    16. Boswijk, H. Peter & Franses, Philip Hans & van Dijk, Dick, 2010. "Cointegration in a historical perspective," Journal of Econometrics, Elsevier, vol. 158(1), pages 156-159, September.
    17. Carlos Acevedo, 2000. "Mecanismos de transmisión de política monetaria con liberalización financiera: El Salvador en los noventa," Monetaria, CEMLA, vol. 0(4), pages 361-412, octubre-d.
    18. Marcellino, Massimiliano, 2000. "Linear aggregation with common trends and cycles," Research in Economics, Elsevier, vol. 54(2), pages 117-131, June.
    19. Al-Jahwari, Salim Ahmed Said, 2021. "Does the Twin-Deficits doctrine apply to the Gulf Cooperation Council? A dynamic panel VAR-X model approach," MPRA Paper 111232, University Library of Munich, Germany.
    20. Sanvi Avouyi-Dovi & Françoise Drumetz & Jean-Guillaume Sahuc, 2012. "The Money Demand Function For The Euro Area: Some Empirical Evidence," Bulletin of Economic Research, Wiley Blackwell, vol. 64(3), pages 377-392, July.
    21. Betty C. Daniel & Christos Shiamptanis, 2008. "Fiscal policy in the European Monetary Union," International Finance Discussion Papers 961, Board of Governors of the Federal Reserve System (U.S.).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jae:japmet:v:20:y:2005:i:5:p:579-601. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/0883-7252/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.