An Interpretable Machine Learning Workflow with an Application to Economic Forecasting
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Kock, Anders Bredahl & Teräsvirta, Timo, 2014.
"Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009,"
International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
- Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers 2011-28, Department of Economics and Business Economics, Aarhus University.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023.
"Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach,"
Journal of International Economics, Elsevier, vol. 145(C).
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kang, Miao & Kapadia, Sujit & Simsek, Özgür, 2020. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Bank of England working papers 848, Bank of England.
- Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2021. "Credit growth, the yield curve and financial crisis prediction: evidence from a machine learning approach," Working Paper Series 2614, European Central Bank.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhidan Luo & Wei Guo & Qingfu Liu & Yiuman Tse, 2023. "A hybrid prediction model with time‐varying gain tracking differentiator in Taylor expansion: Evidence from precious metals," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1138-1149, August.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2023.
"Machine learning advances for time series forecasting,"
Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 76-111, February.
- Ricardo P. Masini & Marcelo C. Medeiros & Eduardo F. Mendes, 2020. "Machine Learning Advances for Time Series Forecasting," Papers 2012.12802, arXiv.org, revised Apr 2021.
- Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
- Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015.
"Macroeconomic forecasting during the Great Recession: The return of non-linearity?,"
International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
- Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
- Laurent Ferrara & Massimiliano Marcellino & Matteo Mogliani, 2015. "Macroeconomic forecasting during the Great Recession: the return of non-linearity?," Post-Print hal-01635951, HAL.
- Marcellino, Massimiliano & Ferrara, Laurent & Mogliani, Matteo, 2013. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," CEPR Discussion Papers 9313, C.E.P.R. Discussion Papers.
- Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
- Oscar Claveria & Enric Monte & Salvador Torra, 2015.
"“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”,"
AQR Working Papers
201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
- Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
- Oscar Claveria & Enric Monte & Salvador Torra, 2017.
"“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”,"
AQR Working Papers
201701, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2017.
- Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
- Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
- Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
- Jahn, Malte, 2018. "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers 185, Hamburg Institute of International Economics (HWWI).
- Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.
- Jena, Pradyot Ranjan & Majhi, Ritanjali & Kalli, Rajesh & Managi, Shunsuke & Majhi, Babita, 2021. "Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 324-339.
- Ahmed Ramzy Mohamed, 2022. "Artificial Neural Network for Modeling the Economic Performance: A New Perspective," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(3), pages 555-575, September.
- Malte Jahn, 2023. "Regressing on distributions: The nonlinear effect of temperature on regional economic growth," Papers 2309.10481, arXiv.org.
More about this item
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ijc:ijcjou:y:2023:q:4:a:10. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bank for International Settlements (email available below). General contact details of provider: https://www.ijcb.org/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.