Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58529
Title: Forecasting credit-to-GDP
Authors: Kobpongkit Navapan
Jianxu Liu
Songsak Sriboonchitta
Keywords: Computer Science
Issue Date: 1-Jan-2018
Abstract: © 2018, Springer International Publishing AG. After the Global Financial Crisis in 2008, a great attempt has been placed on studying of early warning indicators (EWIs) in order to forecast possible future crises. EWIs have played a crucial role not only in explaining which macroprudential policies should be involved and put into effect, but also indicating when it is an appropriate timing for implementation of the policies. Accurate prediction of EWIs therefore has become a big issue. The paper aims to forecast a credit-to-GDP gap, by using three different models: linear, Markov switching, quantile models with some selected macroeconomic variables; set index, exchange rate and export. The empirical results show that the quantile 25th model performs the most accurate forecasting ability based on RMSE and MAPE. Furthermore, the forecast results indicates that there is a slight downturn of the predicted values during 2006 to 2007.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038869301&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/58529
ISSN: 1860949X
Appears in Collections:CMUL: Journal Articles

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