Revisiting Generalized Models of Japanese Seafood Demand: A Forecast Combination Approach

We revisit the generalized inverse and ordinary demand systems for fish using four decades of monthly Japanese seafood data (1985–2025), a period over which the market shifted from domestic catch to heavy import dependence. The earlier consensus no longer holds. The ordinary specification is rejected under every instrument set, but the inverse specification is also rejected when the full set of instruments is used, so the preferred model depends on which instruments are chosen. Additionally, lag and macroeconomic instruments that once identified the supply side have also lost most of their explanatory power. Together these results indicate that the structure of the market has changed and that a single demand model may no longer describe the market well. Faced with the model selection uncertainty, we examine whether combining the competing forecasts is preferable to selecting one. Using unconditional rolling-window forecasts and a range of combination methods, we find that no single model forecasts best at every horizon, while the combinations are at least as accurate as the individual models and never as poor as the weakest among them. The gains from combination are small, because the demand systems are very similar yielding similarl forecasts. Combination therefore offers a reliable hedge against choosing the wrong model, even where it does not clearly improve on the best one