IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T08:11:01ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2021-07-19T09:36:23Z2021-07-19T09:39:43Zhttp://eprints.imtlucca.it/id/eprint/4082This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40822021-07-19T09:36:23ZPredicting Exporters with Machine LearningIn this contribution, we exploit machine learning techniques to predict out-of-sample firms'
ability to export based on the financial accounts of both exporters and non-exporters. Therefore,
we show how forecasts can be used as exporting scores, i.e., to measure the distance of
non-exporters from export status. For our purpose, we train and test various algorithms on the
financial reports of 57,021 manufacturing firms in France in 2010-2018. We find that a Bayesian
Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than
other techniques with a prediction accuracy of up to 0:90. Predictions are robust to changes in
definitions of exporters and in the presence of discontinuous exporters. Eventually, we argue
that exporting scores can be helpful for trade promotion, trade credit, and to assess firms'
competitiveness. For example, back-of-the-envelope estimates show that a representative firm
with just below-average exporting scores needs up to 44% more cash resources and up to 2:5
times more capital expenses to reach full export status.Francesca Micoccifrancesca.micocci@imtlucca.itArmando Rungiarmando.rungi@imtlucca.it2021-03-17T10:04:23Z2021-03-17T10:04:50Zhttp://eprints.imtlucca.it/id/eprint/4081This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40812021-03-17T10:04:23ZA Neural Network Ensemble Approach for GDP
ForecastingWe propose an ensemble learning methodology to forecast the future US GDP
growth release. Our approach combines a Recurrent Neural Network (RNN) with
a Dynamic Factor model accounting for time-variation in mean with a General-
ized Autoregressive Score (DFM-GAS). The analysis is based on a set of predictors
encompassing a wide range of variables measured at different frequencies. The
forecast exercise is aimed at evaluating the predictive ability of each model's com-
ponent of the ensemble by considering variations in mean, potentially caused by
recessions affecting the economy. Thus, we show how the combination of RNN and
DFM-GAS improves forecasts of the US GDP growth rate in the aftermath of the
2008-09 global financial crisis. We find that a neural network ensemble markedly
reduces the root mean squared error for the short-term forecast horizon.Luigi Longoluigi.longo@imtlucca.itMassimo Riccabonimassimo.riccaboni@imtlucca.itArmando Rungiarmando.rungi@imtlucca.it