Linear models, smooth transition autoregression, and neural networks for forecasting macroeconomic time series: A re-examination

Timo Teräsvirta Y · Dick J.C. van Dijk · Marcelo Cunha Medeiros

275 citations0/0 files storedEUR Research Repository (Erasmus University Rotterdam)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Stock Market Forecasting Methods Time Series Analysis and Forecasting

Abstract

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 275 citations exist upstream.

References

1998Forecasting with artificial neural networks:1969The Combination of Forecasts 1997Testing the equality of prediction mean squared errors 1988Testing Linearity Against Smooth Transition Autoregressive Models 2001Tests of equal forecast accuracy and encompassing for nested models 2002SMOOTH TRANSITION AUTOREGRESSIVE MODELS — A SURVEY OF RECENT DEVELOPMENTS 1992Characterizing nonlinearities in business cycles using smooth transition autoregressive models 1998Tests for Forecast Encompassing

Cited by

2018Decision-making for financial trading: A fusion approach of machine learning and portfolio selection 2011Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction 2017Forecasting of nonlinear time series using ANN 2007Quarterly Time-Series Forecasting With Neural Networks 2011Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.