Enhanced Forecasting with LSTVAR-ANN Hybrid Model: Application in Monetary Policy and Inflation Forecasting

Michał Chojnowski ()
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Michał Chojnowski: Warsaw School of Economics

Chapter Chapter 13 in Forecasting with Artificial Intelligence, 2023, pp 341-372 from Palgrave Macmillan

Abstract: Abstract This chapter presents a novel method in monetary policy analysis and inflation forecasting. The author presents a hybrid model, which imposes different economy dynamics in different periods of the Business Cycle—LSTVAR-ANN. LSTVAR-ANN provides a plethora of insights such as impact of monetary policy in expansion or recession periods, components of the Business Cycle or inflation forecasts. The research was conducted on US data. In LSTVAR-ANN model regimes are defined by a smooth, continuous transition function. The output of the transition function can be interpreted as a metric of the Business Cycle momentum. In this research, the author used Index of Customer ConfidenceCustomer Confidence of University of Michigan as a proxy. ANN part of the model helps to “observe“ consumer confidence via Internet search data (here: Google Trends). This chapter answers three questions stated by the author: is it possible to observe consumer confidence (thus the Business Cycle) using Internet searches? Does monetary policy affect prices differently in different business cycle periods? Does differentiation of regimes enhance inflation forecasts?

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:pal:paiecp:978-3-031-35879-1_13

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DOI: 10.1007/978-3-031-35879-1_13

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