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Forecasting quarterly hog prices: Simple autoregressive models vs. naive predictions
Author(s) -
Gjølberg Ole,
Bengtsson BerthArne
Publication year - 1997
Publication title -
agribusiness
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.57
H-Index - 43
eISSN - 1520-6297
pISSN - 0742-4477
DOI - 10.1002/(sici)1520-6297(199711/12)13:6<673::aid-agr11>3.0.co;2-1
Subject(s) - autoregressive model , econometrics , simple (philosophy) , benchmark (surveying) , economics , point (geometry) , star model , vector autoregression , lag , computer science , autoregressive integrated moving average , statistics , mathematics , time series , computer network , philosophy , geometry , geodesy , epistemology , geography
In this note, we study the forecasting performance of some simple models applied to the hog markets in the Nordic countries. In terms of accuracy (MSE and MAPE), a simple autoregressive model outperforms the naive expectations benchmark in some samples, as does a very simple VAR‐type model in which lagged piglet prices are added to the lagged hog prices as RHS variables. Forecasting performance is, however, quite sensitive to the chosen lag structure, and there is reason to doubt whether the simple autoregressive model from an economic point of view yields significantly better results than those of the naive model. Focusing on directional forecasts, on the other hand, the simple VAR‐models perform clearly better. Thus, for producers whose main concern it is whether the price moves up or down, these models may be quite useful. © 1997 John Wiley & Sons, Inc.