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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-02032021-145547


Tipo di tesi
Tesi di laurea magistrale
Autore
BOUCHS, ALESSANDRO
URN
etd-02032021-145547
Titolo
Macroeconomic Forecasting with High Dimensional Data: A Deep Learning Approach
Dipartimento
ECONOMIA E MANAGEMENT
Corso di studi
ECONOMICS
Relatori
relatore Prof. Ragusa, Giuseppe
Parole chiave
  • big data
  • forecasting
  • machine learning
  • macroeconomics
  • neural networks
Data inizio appello
22/02/2021
Consultabilità
Non consultabile
Data di rilascio
22/02/2091
Riassunto
This project tackles the issue of dimension reduction and forecasting with high dimensional macroeconomic data. The most common dimension reduction technique is Principal Component Analysis, which forms the backbone of Dynamic Factor Models and is based on linear transformations of the data. Yet, scholars have argued that non-linear structures in the data could be exploited to obtain an improved representation of the original data and more accurate forecasts. In this project, I experiment the use of neural networks, part of the arsenal of techniques coming from deep learning, as a dimension reduction method that exploits non-linear interactions. Using monthly data from the United States from 1960 to 2019, I estimate factors by Principal Component Analysis within a Dynamic Factor Model and also by training two autoencoders with differing depth. Subsequently, I generate factor-augmented forecasts of US Industrial Production and compare the results in order to assess both methodologies. A basic one-layer autoencoder is shown to yield factors that are able to collect a significant amount of features in the data, comparable to a Dynamic Factor Model, and which improve the forecasting accuracy with respect to the Dynamic Factor Model at all horizons, especially at the six and twelve steps ahead. On the other hand, a deep autoencoder with five hidden layers struggles to find an improved lower-dimensional representation, but the factors obtained may still lead to noteworthy gains in forecasting accuracy. Further research may be conducted in order to improve the performance of these networks, reducing overall loss and overfitting and increasing the amount of information retained by the factors, while further experiments could see other architectures being exploited to obtain more accurate forecasts.
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