An Empirical Dynamic Model of Trade with Consumer Accumulation

2021 ◽  
Vol 13 (4) ◽  
pp. 23-63
Author(s):  
Paul Piveteau

This paper develops a dynamic structural model of trade in which firms slowly accumulate consumers in foreign markets. Estimating the model using export data from individual firms and a particle Markov chain Monte Carlo estimator, the model predicts lower survival rates for new exporters and estimates low entry costs of exporting—less than half of those estimated in the absence of consumer accumulation. Using simulations and out-of-sample predictions, I show that the introduction of such frictions and the reduction in estimated entry costs allow the model to match important facts regarding the aggregate response of international trade to shocks. (JEL D22, F12, F14, L66)

2020 ◽  
Vol 6 (5) ◽  
pp. eaav6971 ◽  
Author(s):  
Roger Guimerà ◽  
Ignasi Reichardt ◽  
Antoni Aguilar-Mogas ◽  
Francesco A. Massucci ◽  
Manuel Miranda ◽  
...  

Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need “machine scientists” that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.


1994 ◽  
Author(s):  
Alan E. Gelfand ◽  
Sujit K. Sahu

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