RESUMO
BACKGROUND: Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments. OBJECTIVE: We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department. MATERIAL AND METHODS: We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals. RESULTS: We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals. CONCLUSION: Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.
RESUMO
This paper examines price-switching spillovers between the US and Chinese stock, crude oil, and gold futures markets before and during the COVID-19 pandemic. Using a Markov-switching vector autoregressive model, we show that stock markets were mainly influenced by their own shocks, with effects that were sensitive to regime shifts. Connectedness network analysis reveals that gold and stock markets were net contributors (receivers) of spillovers in the low-volatility regime (high-volatility regime), while oil was a major receiver (contributor) of spillovers in the low-volatility regime (high volatility regime). Regimes were mainly low-volatility from January 2019 to February 2020 and high-volatility from March 2020 to May 2020. We conclude that the COVID-19 pandemic intensified spillovers from commodity markets to the US and Chinese stock markets.
