This paper proposes an international collaboration between researchers in academia and policymaking institutions to stimulate and coordinate research on probability forecasting in macroeconomics, developing a toolbox for short-term prediction. The toolbox should include time series models,...

This paper proposes an accurate, parsimonious and fast-to-estimate forecasting model for integer-valued time series with long memory and seasonality. The modelling is achieved through an autoregressive Poisson process with a predictable stochastic intensity that is determined by two factors: a...

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of...

By its emissions of greenhouse gases, economic activity is the source of climate change which affects pandemics that in turn can impact badly on economies. Across the three highly interacting disciplines in our title, time-series observations are measured at vastly different data frequencies:...

This paper attempts to examine, compare and forecast the per capita GDP of India, the USA, China, and Japan for a period of ten years from 2020 to 2029. It studies the concept of economic convergence which states that the developing economies’ per capita income levels tend to move at a faster...