Advertisement

Empirical Economics

, Volume 21, Issue 2, pp 203–220 | Cite as

Residential energy demand analysis: An empirical application of the closure test principle

  • Reinhard Madlener
  • Raimund Alt
Article

Abstract

In this paper a set of ten different single-equation models of residential energy demand is being analyzed, derived by the imposition of linear parameter restrictions on a fairly general autoregressive distributed lag (ADL) model. Residential energy consumption is assumed to be explainable by households' real disposable income, movements in the real price of energy, and the temperature variable ‘heating degree days’. In the empirical application, Austrian annual data for the period 1970 to 1992 are used. The main focus of the paper is on the control of the overall significance level of the tests based on the application of the closure test principle, introduced by Marcus, Peritz, and Gabriel (1976). The application illustrates nicely how one can, by defining a closed system of hypotheses, control the significance level α in supporting the search for a suitable specific model. The wide range of estimated elasticities, however, indicates that the estimation results depend strongly on the choice of the model specification.

JEL Classification System-Numbers

C12 C22 C52 Q41 R22 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alt R (1991) Multiple test procedures and the closure principle: A new look at multiple hypotheses testing in the linear regression model. Research Memorandum No. 278, Institute of Advanced Studies. (Paper presented at the 6th World Congress of the Econometric Society, Barcelona, 1990)Google Scholar
  2. Charemza WW, Deadman DF (1992) New directions in econometric practice. Aldershot (UK)/Brookfield (USA): Edward ElgarGoogle Scholar
  3. Davidson JH, Hendry DH, Srba F, Yeo S (1978) Econometric modelling of the aggregate time-series relationship between consumers expenditure and income in the United Kingdom. The Economic Journal 88:661–92Google Scholar
  4. Doornik JA, Hendry DF (1994) PcGive 8.0: Interactive econometric modelling of dynamic systems. London: International Thomson PublishingGoogle Scholar
  5. Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflations. Econometrica 55:251–76Google Scholar
  6. Hendry DF (1974) Stochastic specification in an aggregate demand model of the United Kingdom. Econometrica 42:559–78Google Scholar
  7. Hendry DF (1993) Econometrics: Alchemy or science? Oxford: Blackwell PublishersGoogle Scholar
  8. Hendry DF (1995) Dynamic econometrics. Oxford: Oxford University PressGoogle Scholar
  9. Hendry DF, Doornik JA (1994) Modelling linear dynamic econometric systems. Scottish Journal of Political Economy 41:1–33Google Scholar
  10. Hendry DF, Mizon GE (1990) Procrustean econometrics: Or stretching and squeezing data. In: CWJ Granger (ed) Modelling Economic Series: Readings in Econometric Methodology. Oxford: Oxford University Press: 121–36Google Scholar
  11. Holm S (1979) A simple sequentially rejective multiple test procedure, Scandinavian Journal of Statistics 6:65–70Google Scholar
  12. Jones CT (1993) A single-equation study of U.S. petroleum consumption: The role of model specification. Southern Economic Journal 59:4:687–700Google Scholar
  13. Judge GG, Griffiths WE, Hill RC, Lütkepohl H, Lee TC (1985) The theory and practice of econometrics. 2nd ed. New York: John WileyGoogle Scholar
  14. Kouris G (1981) Elasticities — science or fiction? Energy Economics 3:2:66–70Google Scholar
  15. Krämer W, Sonnberger H (1986) The linear regression model under test. Heidelberg: Physica-VerlagGoogle Scholar
  16. Lovell MC (1983) Data mining. The Review of Economics and Statistics 65:1–12Google Scholar
  17. Marcus R, Peritz E, Gabriel KR (1976) On closed testing procedures with special reference to ordered analysis of variance. Biometrika 63:3:655–660Google Scholar
  18. Miller RG (1981) Simultaneous statistical inference. 2nd ed. New York: SpringerGoogle Scholar
  19. Mizon GE (1995) Progressive modelling of macroeconomic time series: The LSE Methodology. EUI Working Paper ECO No 95/10, European University Institute, Florence (Italy)Google Scholar
  20. Neusser K (1991) Testing the long-run implications of the neoclassical growth model. Journal of Monetary Economics 27:3–37Google Scholar
  21. Ramsey JB (1969) Tests for specification errors in classical linear least squares regression analysis. Journal of the Royal Statistical Society B 31:350–71Google Scholar
  22. Savin NE (1984) Multiple hypothesis testing. In: Z Griliches, MD Intriligator (eds) Handbook of Econometrics. Vol. II. Amsterdam: North-Holland: 827–79Google Scholar
  23. Sonnemann E (1982) Allgemeine Lösungen multipler Testprobleme. EDV in Medizin und Biologie 13:120–128Google Scholar

Copyright information

© Physica-Verlag 1996

Authors and Affiliations

  • Reinhard Madlener
    • 1
  • Raimund Alt
    • 1
  1. 1.Institute for Advanced StudiesViennaAustria

Personalised recommendations