Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model...Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model that employs a Bayesian additive regression tree (BART). The performance of the BART model is compared with selection models like Lasso, Elastic Net, and Bayesian networks in simulation experiments with linear and non-linear data generating processes (DGP), and on US macroeconomic time series data. The results show that the BART model is quite competitive against the linear parametric methods when the DGP is linear, and outperforms the competing methods when the DGP is non-linear. The empirical results suggest that the BART estimators are generally more efficient than the traditional linear methods when modeling and forecasting macroeconomic time series.展开更多
We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a n...We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. Using data of 20 major stock markets, including G10 economies, we find spikes in our EMR index during the dotcom bubble, the global financial crisis, the European sovereign debt crisis, and the novel coronavirus pandemic. The result shows that the global financial crisis and the COVID-19 induced crisis record the historic highest spikes in financial market risk, suggesting stronger evidence of contagion in both periods.展开更多
文摘Modeling dynamic systems with linear parametric models usually suffer limitation which affects forecasting performance and policy implications. This paper advances a non-parametric autoregressive distributed lag model that employs a Bayesian additive regression tree (BART). The performance of the BART model is compared with selection models like Lasso, Elastic Net, and Bayesian networks in simulation experiments with linear and non-linear data generating processes (DGP), and on US macroeconomic time series data. The results show that the BART model is quite competitive against the linear parametric methods when the DGP is linear, and outperforms the competing methods when the DGP is non-linear. The empirical results suggest that the BART estimators are generally more efficient than the traditional linear methods when modeling and forecasting macroeconomic time series.
文摘We construct a new index of global equity market risk (EMR) using market interconnectedness and volatilities. We study the relationship between our EMR and the VIX over the last two decades. The EMR is shown to be a novel approach to measuring global market risk, and an alternative to the VIX. Using data of 20 major stock markets, including G10 economies, we find spikes in our EMR index during the dotcom bubble, the global financial crisis, the European sovereign debt crisis, and the novel coronavirus pandemic. The result shows that the global financial crisis and the COVID-19 induced crisis record the historic highest spikes in financial market risk, suggesting stronger evidence of contagion in both periods.
