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Econometric Modelling of Canadian Long Distance Calling: A Comparison of Aggregate Time Series Versus Point-to-Point Panel Data Approaches

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Panel Data Analysis

Part of the book series: Studies in Empirical Economics ((STUDEMP))

Abstract

Aggregate time series and point-to-point panel data approaches to modelling Canadian long distance calling are compared. It is found that, while both models yield similar sets of aggregate elasticities, the point-to-point panel data models provide more detailed information on consumer behavior. Furthermore, the panel data models take advantage of the disaggregate data available and avoid the problems inherent in aggregate models when the rates of changes in the micro data are different.

Cheng Hsiao’s work is supported in part by National Science Foundation grant SES 88–21205. We wish to thank referees for helpful comments.

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Author information

Authors and Affiliations

  1. Telecom Canada, Fl. 4, 105 Hotel de Ville St. Hull, Quebec, Canada, J8X 4H7

    Trent W. Appelbe, Christopher R. Dineen & D. Lynn Solvason

  2. University of Southern California, USA

    Cheng Hsiao

  3. University of California at Inine, USA

    Cheng Hsiao

Authors
  1. Trent W. Appelbe
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  2. Christopher R. Dineen
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  3. D. Lynn Solvason
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  4. Cheng Hsiao
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Editor information

Editors and Affiliations

  1. School of Business and Economics, Wilfrid Laurier University, Waterloo, Ontario, N2L 3C5, Canada

    Baldev Raj

  2. Department of Economics, Texas A&M University, College Station, 77843-4228, Texas, USA

    Badi H. Baltagi

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© 1992 Physica-Verlag Heidelberg

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Appelbe, T.W., Dineen, C.R., Solvason, D.L., Hsiao, C. (1992). Econometric Modelling of Canadian Long Distance Calling: A Comparison of Aggregate Time Series Versus Point-to-Point Panel Data Approaches. In: Raj, B., Baltagi, B.H. (eds) Panel Data Analysis. Studies in Empirical Economics. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-50127-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-50127-2_10

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-642-50129-6

  • Online ISBN: 978-3-642-50127-2

  • eBook Packages: Springer Book Archive

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