ESTIMATING AND FORECASTING TRADE FLOWS BY PANEL DATA ANALYSIS AND NEURAL NETWORKS

Year 2014, Volume: 64 Issue: 1, 85 - 111, 02.06.2014

Elif Nuroğlu

Abstract

This paper aims to investigate bilateral trade flows among EU15countries
from 1964 to 2003 with their determinants by using panel data analysis
and neural network modeling. When we compare explanatory power of
both models, it appears that neural networks can explain larger variation in
bilateral exports compared to the panel data analysis. Moreover, in comparing
out-of-sample forecasting performances of panel model and neural networks,
it is seen that neural networks produce much lower MSE which makes
them superior to the panel model. One of the main relative benefits of the
neural network model is nonlinearity, as it uses sigmoid functions instead
of linear functions as building blocks. This partly explains its success in our
study. Another advantage of neural networks is that they make no a priori
assumptions about the population distribution and the relationship between
explanatory variables and the dependent variable.
Keywords: gravity model, panel data, neural networks, EU15, bilateral trade
TİCARET AKIMLARININ PANEL VERİ ANALİZİ
VE YAPAY SİNİR AĞLARI İLE TAHMİN VE
ÖNGÖRÜSÜ
ÖZET
Bu çalışmada 15 Avrupa Birliği ülkesi arasında 1964’ten 2003’e kadar
gerçekleşen ticaret akımları ve bunları etkileyen faktörler panel veri analizi
ve yapay sinir ağları modellemesi kullanılarak incelenecektir. Her iki modelin
açıklama gücü karşılaştırıldığında yapay sinir ağlarının karşılıklı ticareti
panel veri analizine göre daha iyi açıkladığı görülmüştür. Ayrıca, örneklem
dışı tahmin performansları karşılaştırıldığında da yapay sinir ağlarının panel
veri analizine göre çok daha düşük ortalama karesel hata verdiği tespit edilmiştir.
Yapay sinir ağlarının en önemli avantajı doğrusal olmamaları, yani
yapı taşlarının doğrusal fonksiyonlar değil de sigmoid fonksiyonlardan oluşmasıdır.
Bu onların çalışmamızdaki başarısını kısmen açıklar. Yapay sinir
ağlarının diğer bir avantajı da nüfus dağılımı ile bağımlı ve bağımsız değişkenler
arasındaki ilişki hakkında apriyori varsayımlarda bulunmamalarıdır.
Anahtar Kelimeler: çekim modeli, panel veri, yapay sinir ağları, AB15,
iki taraflı ticaret

Keywords

çekim modeli, panel veri, yapay sinir ağları, AB15, iki taraflı ticaret

ESTIMATING AND FORECASTING TRADE FLOWS BY PANEL DATA ANALYSIS AND NEURAL NETWORKS

Abstract

This paper aims to investigate bilateral trade flows among EU15countries from 1964 to 2003 with their determinants by using panel data analysis and neural network modeling. When we compare explanatory power of both models, it appears that neural networks can explain larger variation in bilateral exports compared to the panel data analysis. Moreover, in comparing out-of-sample forecasting performances of panel model and neural networks, it is seen that neural networks produce much lower MSE which makes them superior to the panel model. One of the main relative benefits of the neural network model is nonlinearity, as it uses sigmoid functions instead of linear functions as building blocks. This partly explains its success in our study. Another advantage of neural networks is that they make no a priori assumptions about the population distribution and the relationship between explanatory variables and the dependent variable.

Keywords

gravity model, panel data, neural networks, EU15, bilateral trade

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