This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and...This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.展开更多
In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2,2020.In particular,we consider the forecast of the number of new daily confirmed cases.A ...In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2,2020.In particular,we consider the forecast of the number of new daily confirmed cases.A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed.An out-of-sample comparison with forecasts from an autoregressive integrated moving average(ARIMA)model is considered.This comparison indicates that our procedure outperforms the ARIMA model.The Root Mean Square Error(RMSE)of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE.We have also conducted Diebold and Mariano(1995)tests of equal mean square error(MSE).The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons.We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.展开更多
文摘This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.
文摘In this paper we forecast the spread of the coronavirus disease 2019 outbreak in Italy in the time window from May 19 to June 2,2020.In particular,we consider the forecast of the number of new daily confirmed cases.A forecast procedure combining a log-polynomial model together with a first-order integer-valued autoregressive model is proposed.An out-of-sample comparison with forecasts from an autoregressive integrated moving average(ARIMA)model is considered.This comparison indicates that our procedure outperforms the ARIMA model.The Root Mean Square Error(RMSE)of the ARIMA is always greater than that of the our procedure and generally more than twice as high as the our procedure RMSE.We have also conducted Diebold and Mariano(1995)tests of equal mean square error(MSE).The tests results confirm that forecasts from our procedure are significantly more accurate at all horizons.We think that the advantage of our approach comes from the fact that it explicitly takes into account the number of swabs.
