Letters in Spatial and Resource Sciences

, Volume 9, Issue 1, pp 73–91 | Cite as

Household disaggregation and forecasting in a regional econometric input–output model

  • Kijin Kim
  • Geoffrey J. D. Hewings
  • Kurt Kratena
Original Paper


The overwhelming attention to disaggregation of the interindustry components of the regional economy has neglected the problems generated by the adoption of the representative household in the modeling of economic impacts and forecasting in many regional economic models. Drawing on a recently modified regional econometric input–output model (REIM) for the Chicago metropolitan region in which households were disaggregated by age (Kim et al., Econ Syst Res. doi: 10.1080/09535314.2014.991778, 2014), this paper provides an assessment of the differences generated by consumption of a representative and disaggregated households using data at the corresponding level of aggregation. The results reveal that the total effects of disaggregation that can be ascribed to population ageing vary by a much smaller extent than those generated by model specification and data. The disaggregate REIM with heterogeneous households by age yields smaller RMSEs than the aggregate REIM with a representative household, but a statistical testing suggests that forecasting gains from disaggregation are modest compared to the aggregate model.


Econometric input–output model Almost ideal demand system Heterogeneity Forecasting accuracy 

JEL Classification

C53 D12 R15 



The authors are grateful to Anil Bera, Woong Young Park and two anonymous referees for helpful comments.


  1. Bardazzi, R., Barnabani, M.: A long-run disaggregated cross-section and time-series demand system: an application to Italy. Econ. Syst. Res. 13(4), 365–389 (2001)CrossRefGoogle Scholar
  2. Barker, T., Pesaran, M.H.: Disaggregation in econometric modelling-an introduction. In: Disaggregation in Econometric Modelling. Routledge, London (1990)Google Scholar
  3. Blundell, R., Stoker, T.M.: Heterogeneity and aggregation. J. Econ. Lit. 43(2), 347–391 (2005)CrossRefGoogle Scholar
  4. Conway, R.: The Washington Projection and Simulation Model: a regional interindustry econometric model. Int. Reg. Sci. Rev. 13(1–2), 141–165 (1990)CrossRefGoogle Scholar
  5. Cumby, R.E., Huizinga, J., Obstfeld, M.: Two-step two-stage least squares estimation in models with rational expectations. J. Econom. 21(3), 333–355 (1983)CrossRefGoogle Scholar
  6. Davidson, R., MacKinnon, J.G.: Several tests of model specification in the presence of alternative hypotheses. Econometrica 49(3), 781–793 (1981)CrossRefGoogle Scholar
  7. Deaton, A., Muellbauer, J.: An almost ideal demand system. Am. Econ. Rev. 70(3), 312–326 (1980a)Google Scholar
  8. Deaton, A., Muellbauer, J.: Economics and Consumer Behavior. Cambridge University Press, New York (1980b)Google Scholar
  9. Denton, F.T., Mountain, D.C., Spencer, B.G.: Age, trend, and cohort effects in a macro model of Canadian expenditure patterns. J. Bus. Econ. Stat. 17(4), 430–443 (1999)Google Scholar
  10. Denton, F.T., Mountain, D.C.: Exploring the effects of aggregation error in the estimation of consumer demand elasticities. Econ. Model. 28(4), 1747–1755 (2011)CrossRefGoogle Scholar
  11. Dowd, T.A., Monaco, R.M., Janoska, J.J.: Effects of future demographic changes on the US economy: evidence from a long-term simulation model. Econ. Syst. Res. 10(3), 239–262 (1998)CrossRefGoogle Scholar
  12. Erlandsen, S., Nymoen, R.: Consumption and population age structure. J. Popul. Econ. 21(3), 505–520 (2008)CrossRefGoogle Scholar
  13. Fair, R.C., Shiller, R.J.: Comparing information in forecasts from econometric models. Am. Econ. Rev. 80(3), 375–389 (1990)Google Scholar
  14. Fair, R., Dominguez, K.M.: Effects of changing U.S. age distribution on macroeconomics equations. Am. Econ. Rev. 81(5), 1276–1294 (1991)Google Scholar
  15. Fawson, C., Criddle, K.R.: A comparative analysis of time series approaches to modeling intersectoral and intercounty employment linkages in rural regional labor markets. J. Reg. Sci. 34(1), 57–74 (1994)CrossRefGoogle Scholar
  16. Greene, W.H.: Econometric Analysis, 5th edn. Prentice Hall, Upper Saddle River (2003)Google Scholar
  17. Hansen, L.P.: Large sample properties of generalized method of moments estimators. Econometrica 50(4), 1029–1054 (1982)CrossRefGoogle Scholar
  18. Hyndman, R.J., Koehler, A.B.: Another look at measures of forecast accuracy. Int. J. Forecast. 22(4), 679–688 (2006)CrossRefGoogle Scholar
  19. Israilevich, P., Hewings, G., Schindler, G., Mahidahra, R.: Forecasting structural change with a regional econometric input–output model. J. Reg. Sci. 37(4), 565–590 (1997)CrossRefGoogle Scholar
  20. Kendrick, J.W., Jaycox, C.M.: The concept and estimation of gross state product. South. Econ. J. 32(2), 153–168 (1965)CrossRefGoogle Scholar
  21. Kim, K., Kratena, K., Hewings, G.: The extended econometric input–output model with heterogeneous household demand system. Econ. Syst. Res. (2014). doi: 10.1080/09535314.2014.991778
  22. Klein, L.R.: The specification of regional econometric models. Pap. Reg. Sci. 23(1), 105–116 (1969)CrossRefGoogle Scholar
  23. Klein, L.R., Welfe, A., Welfe, W.: Principles of Macroeconometric Modeling. Elsevier, Amsterdam (1999)Google Scholar
  24. LeSage, J.P., Rey, S.: Restrictions in integrated econometric+input–output modeling. In: Hewings, G.J.D., Sonis, M., Boyce, D. (eds.) Trade, Networks, and Hierarchies. Advances in Spatial Sciences. Springer, Heidelberg (2002)Google Scholar
  25. Lührmann, M.L.: Effects of population ageing on aggregated UK consumer demand. IFS and CEMMAP, London (2008)Google Scholar
  26. Lütkepohl, H.: Forecasting contemporaneously aggregated vector ARMA processes. J. Bus. Econ. Stat. 2(3), 201–214 (1984)Google Scholar
  27. Lütkepohl, H.: Forecasting with VARMA Models. In: Elliott, G., Granger, C.W.J., Timmermann, A. (eds.) Handbook of Economic Forecasting, vol. 1. Elsevier, Amsterdam (2006)Google Scholar
  28. Martín, J.J.M., Puerto Lopez del Amo Gonzalez, M., Dolores Cano Garcia, M.: Review of the literature on the determinants of healthcare expenditure. Appl. Econ. 43(1), 19–46 (2011)CrossRefGoogle Scholar
  29. Matteo, D.L.: The macro determinants of health expenditure in the United States and Canada: assessing the impact of income, age distribution and time. Health Policy 71(1), 23–42 (2005)CrossRefGoogle Scholar
  30. Motti, B.B.: A dynamic integration approach in regional input–output and econometric models. Rev. Reg. Stud. 35(2), 139–160 (2005)Google Scholar
  31. Motti, B.B., Blevins, D.R.: New directions in regional employment forecasting models. Int. J. Bus. Res. 7(3), 150–159 (2007)Google Scholar
  32. Newey, W.K., West, K.D.: A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica 55(3), 703–708 (1987)CrossRefGoogle Scholar
  33. Newey, W.K., West, K.D.: Automatic lag selection in covariance matrix estimation. Rev. Econ. Stud. 61(4), 631–653 (1994)CrossRefGoogle Scholar
  34. Piketty, T.: Capital in the Twenty-first Century. Harvard University Press, Cambridge (2013)Google Scholar
  35. Pyatt, G.: Some early multiplier models of the relationship between income distribution and production structure. Econ. Syst. Res. 13(2), 139–163 (2001)CrossRefGoogle Scholar
  36. Rey, S.: The performance of alternative integration strategies for combining regional econometric and input–output models. Int. Reg. Sci. Rev. 21(1), 1–35 (1998)CrossRefGoogle Scholar
  37. Rey, S.: Integrated regional econometirc+input–output modeling: issues and opportunities. Pap. Reg. Sci. 79(3), 271–292 (2000)CrossRefGoogle Scholar
  38. Taylor, L.D., Houthakker, H.S.: Consumer demand in the US: prices, income, and consumption behavior, 3rd edn. Springer, New York (2010)CrossRefGoogle Scholar
  39. White, H., Domowitz, I.: Nonlinear regression with dependent observations. Econometrica 52(1), 143–161 (1984)CrossRefGoogle Scholar
  40. Yoon, S.G., Hewings, G.: Impacts of demographic changes in the Chicago region. Discussion Paper 06–T07 Regional Economics Applications Laboratory (REAL), University of Illinois, Urbana (2006).

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kijin Kim
    • 1
  • Geoffrey J. D. Hewings
    • 1
  • Kurt Kratena
    • 2
  1. 1.Regional Economics Applications LaboratoryUniversity of IllinoisUrbanaUSA
  2. 2.Austrian Institute of Economics Research (WIFO)ViennaAustria

Personalised recommendations