Customary empirical gravity models are parametric and rely on the assumption that exports from country i to j depend on an additive index of three components: i-specific supply potential which is common across importers j; j-specific demand potential which is common across exporters i; and bilateral trade frictions which are indexed by ij and common within ij in a generic period. We demonstrate that the data reject the constancy of these components within category. The variation of trade-agreement-depth effects within category across country pairs and time is striking.

This paper proposes a novel forecast reconciliation framework using Bayesian state-space methods. It allows for the joint reconciliation at all forecast horizons and uses predictive distributions rather than past variation of forecast errors. Informative priors are used to assign weights to specific predictions, which makes it possible to reconcile forecasts such that they accommodate specific judgmental predictions or managerial decisions. The reconciled forecasts adhere to hierarchical constraints, which facilitates communication and supports aligned decision-making at all levels of complex hierarchical structures. An extensive forecasting study is conducted on a large collection of 13,118 time series that measure Swiss merchandise exports, grouped hierarchically by export destination and product category. We find strong evidence that in addition to producing coherent forecasts, reconciliation also leads to substantial improvements in forecast accuracy. The use of state-space methods is particularly promising for optimal decision-making under conditions with increased model uncertainty and data volatility. © 2020 Elsevier B.V.