Before 1985 Reviews

This paper examined the value of different estimating procedures for an econometric model. Specifically it forecasted the number of U.S. visitors to Hawaii using real airfare to Honolulu and U.S. real per capita income. Data fro 1961-73 were used to calibrate the model with ordinary least squares (OLS), generalized lease squares (GLS) and ridge regression. Conditional forecasts were then made for five years (1974-78). Ridge regression produced more 'reasonable' coefficients (because, they say, the signs were correct and the standard errors of the coefficients were smaller) than did either OLS or GLS. Ridge regression also tended to produce the most accurate forecasts, while GLS was worst. This study would have been more valuable had the sample been used more effectively for validation (that Response by E. J. Fujii and J. Mak The authors feel the above review to be incorrect in a number of aspects: 1. The focus if the paper is 2. The issue of a potentially larger sample size ignores a number of salient facts about the Hawaiian economy. The tourist industry in Hawaii developed in earnest only after the Korean War and was profoundly disrupted and changed with the advent of Statehood in 1959. Hence, the decision was made to begin the time series with 1961. 3. The year 1978 was the last year for which data were available as the paper was written in 1979. 4. the statement: "The authored failed to mention the studies showing that the magnitude of the estimated coefficients had little impact on forecast accuracy,' is contradicted directly in p. 76 of the paper. As the quote below indicates, the magnitude of the coefficients The inability to isolate the separate effects of the income and airfare variables stemming from multicollinearity, however, is not a problem in forecasting with OLS provided that the pattern of collinearity, i.e., the approximately linear relationship among the independent variables, which existed in the estimation period, persists in the forecast period. This condition holds, however, as Farrar and Glaube1r (1967) note, only in a situation in which the forecasting problem is all but trivial. Farrar and Glauber (1967, p. 95) note, 'Successful forecasts with multicollinear variables require not only the perpetuation of a stable dependency relationship between With the world-wide oil crisis and the passage by Congress of the Airline Deregulation Bill (1978), there is strong reason to believe that the pattern of collinearity between airfares and income (Table 1 of the article) will not be the same as in the past. If so, successful forecasting of visitor flows will depend on the degree of accuracy with which the separate effects of the explanatory variables can be estimated.
Farrar, D. E. and Glauber, R. R. (1967), "Multicollinearity in
regression analysis: the problem revisited," |