Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques

By Anders Bredahl Kock, Timo Teräsvirta. 2016

46 citations recorded by the source corpus.

When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that converts the specification and nonlinear estimation problem into a linear model selection and estimation problem. We shall compare its performance to that of two other procedures building on the linearization idea: the Marginal Bridge Estimator and Autometrics. Second, one must decide whether forecasting should be carried out recursively or directly. This choice is investigated in this work. The economic time series used in this study are the consumer price indices for the G7 and the Scandinavian countries. In addition, a number of simulations are carried out and results reported in the article.

Subfields: Artificial Intelligence, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Laurent Callot
2. Anders Bredahl Kock
3. Shinya Tanaka
4. Yoshimasa Uematsu

In Management Science and Operations Research

1. Laurent Callot
2. Anders Bredahl Kock
3. Shinya Tanaka
4. Yoshimasa Uematsu

Citations in the corpus

• Machine Learning Advances for Time Series Forecasting (2020, unknown): “The Bagging algorithm is defined as follows.”
• Oracle Efficient Estimation and Forecasting with the Adaptive Lasso and the Adaptive Group Lasso in Vector Autoregressions (2012, background): “As noted in Kock and Teräsvirta (2012) the particular choice of insanity filter is not overly important – what matters is that the insane forecasts are weeded out.”
• High‐dimensional macroeconomic forecasting and variable selection via penalized regression (2019, background): “Kock (2016) considers adaptive lasso in not only stationary but also nonstationary autoregressions.”