A conditionally heteroskedastic binary choice model for macro-financial time series

作者：Ahmed Jameel^*

来源：Journal of Statistical Computation and Simulation, 2016, 86(10): 2007-2035.

DOI：10.1080/00949655.2015.1099159

• Binary choice models
• heteroskedastic-probit
• heteroskedasticity
• ARCH LM test
• probit-GARCH
• 91B84
• 62M10
• 91B55

The macro-financial data are characterized by heteroskedasticity which leads to inconsistent estimates and inference from binary choice models (BCMs). To address this problem, we propose a generalized autoregressive conditionally heteroskedastic-type adjustment for the conditional variance of model errors. A data augmentation type algorithm is developed for estimation while Lagrange multiplier (LM)-type tests are derived for testing ARCH effects in BCMs. Simulation results show that the proposed model leads to bias reduction in estimates, while the expected Information matrix-based LM test exhibits smaller size distortions and higher power properties. Empirically, predictions of the US business and financial cycles reaffirm the effectiveness of the extended model.

• 出版日期2016-7-2