У статті представлено набір багатовимірних моделей для прогнозування світових цін на: 1) сиру нафту; 2) природний газ; 3) залізну руду; 4) сталь. Різні версії моделей векторної авторегресії та виправлення помилок застосовуються до місячних даних для короткострокового прогнозування номінальних цін на товари на шість місяців вперед і для перевірки точності прогнозу. Основні показники для прогнозування цін на метал та енергоносії включають зміни запасів, зміни в обсягах виробництва товарів, обсяги експорту найбільших учасників ринку, зміни у виробничому секторі найбільших споживачів, стан глобальної реальної економічної діяльності, фрахтові ставки, рецесію і так далі. Встановлено, що індекс глобальної реальної економічної активності Кіліана (Kilian 2009) є корисним показником світового попиту і надійним джерелом для прогнозування цін на енергоносії та метал. Отримані дані свідчать про те, що моделі з меншою кількістю лагів, як правило, ефективніші за моделі, де лагів більше. Водночас, окремі моделі, що демонструють високу автономну ефективність, дають різну точність прогнозу протягом різних періодів, і жодна окрема модель не перевершує інші стабільно протягом горизонту прогнозування.
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