Detecting business cycle asymmetries using artificial neural networks and time series models
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Publication:1020512
DOI10.1007/s10614-005-7366-2zbMath1161.91469OpenAlexW2021335888MaRDI QIDQ1020512
Publication date: 29 May 2009
Published in: Computational Economics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10614-005-7366-2
Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
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- Money as a medium of exchange in an economy with artificially intelligent agents
- On the asymptotic properties of the jackknife histogram
- Testing the adequacy of smooth transition autoregressive models
- Testing for neglected nonlinearity in time series models. A comparison of neural network methods and alternative tests
- Estimating the dimension of a model
- Recognizing business cycle turning points by means of a neural network
- Subsampling for heteroskedastic time series
- Multilayer feedforward networks are universal approximators
- Large sample confidence regions based on subsamples under minimal assumptions
- NOTES ON BIAS IN ESTIMATION
- Sectoral Investigation of Asymmetries in the Conditional Mean Dynamics of the Real U.S. GDP
- A Tukey nonadditivity-type test for time series nonlinearity
- Nonlinearity tests for time series
- Testing linearity against smooth transition autoregressive models
- Time to Build and Aggregate Fluctuations
- Hypothesis Testing When a Nuisance Parameter is Present Only Under the Alternative
- Feedforward Neural Nets as Models for Time Series Forecasting
- Testing When a Parameter is on the Boundary of the Maintained Hypothesis
- Modelling Nonlinear Relationships between Extended-Memory Variables
- Specification, Estimation, and Evaluation of Smooth Transition Autoregressive Models
- Some Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models
