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Solving for Coffee Demand Using Regression Analysis

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In their article, "The Demand for Coffee in the United States: 1963-1977" (Quarterly Review of Economics and Business, Summer 1980 pp.36-50), C.J. Huang, J.J. Siegfried, and F. Zardoshty estimated the following regression equation using quarterly data for the 58 quarters running from the first quarter of 1963 through the second quarter of 1977:

ln Qr = 1.2789 - 0.1647ln Pt + 0.5115ln It + 0.1483ln P't1 - 0.0089T - 0.0961D1t - 0.1570D2t - 0.0097D3t
(-2.14) (1.23) (0.55) (-3.36) (-3.74) (-6.03) (-0.37)
R^2 = 0.50 D-W=2.08

The numbers in parenthesis below the estimated regression coefficients are t statistics.

Using the above estimated regression equation for the seasonal demand for coffee in the United States and predicting that the values of the independent or explanatory variables in the demand equation from the third quarter of 1977 to the second quarter of 1978 are those indicated in the following table, forecast the demand for coffee (a) the third quarter of 1977, (b) the fourth quarter of 1977, (c) the first quarter of 1978, (d) the second quarter of 1978. (e) How much confidence can we have in these forecasts? What could cause the large forecasting error to be very large?

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The solution uses regression analysis to solve for coffee demand.

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Robert Reinhart, MBA
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  • MBA, Merage School of Business, Univ of Cal, Irvine
  • BA, Univ of Cal, Irvine
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