Predicting future economic trends appropriately is essential to economic policy making. Currently, the DSGE model approach is a benchmark economic forecasting technique widely employed. However, large external shocks, such as large-scale natural disasters and COVID-19, challenge current approaches to economic forecasting. Multiple approaches will be needed in this situation, including reduced-form model and indicator-based approaches. This paper discusses different forecasting approaches, by comparing forecasts during normal times and crisis periods. The Medium-term Projection Framework (MPF), used in the Economic Outlook for Southeast Asia, China and India series, receives particular attention. The paper also examines challenges unique to developing Asia and large external shock periods. The measurement of potential output, difficulties in modelling the credit channel, and the incorporation of Big Data pose challenges regarding developing Asian countries, and large external shocks may
Abstract
This paper is one of the limited studies to investigate rebound effects in sectoral electricity consumption and the specific case of New Zealand. New Zealand, like other OECD economies, has aimed for energy efficiency improvements and reduced electricity consumption from 9.2 MWh per capita in 2010 to 8.6 MWh per capita in 2015. However, following a significant decline since 2010, electricity consumption in the main New Zealand sectors is increasing. Energy conservation could play an important role in meeting the growing demand for electricity but rebound effects can affect the effectiveness of conservation policies. We decompose the sectoral electricity prices to capture the asymmetric demand response to electricity price changes and estimate electricity demand elasticity during 1980 and 2015 to estimate the sectoral rebound effects. We find partial rebound effects of 54% and 23% in the industrial and commercial sectors respectively while we find no rebound effect at the ag
Credits: Image: Jose-Luis Olivares, MIT
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MIT researchers have developed a type of neural network that learns on the job, not just during its training phase. These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs. The advance could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.
“This is a way forward for the future of robot control, natural language processing, video processing any form of time series data processing,” says Ramin Hasani, the study’s lead author. “The potential is really significant.”
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