Sharing economic benefits equitably across all segments of society includes addressing the specific challenges of different generations. At present, youth and elderly are particularly vulnerable to poverty relative to adults in their middle years. Broad-based policies should aim to foster youth integration into the labor market and ensure adequate income and health care support for the elderly. Turning to the intergenerational dimension, everyone should have the same chances in life, regardless of their family background. Policies that promote social mobility include improving access to high-quality care and education starting from a very early age, supporting lifelong learning, effective social protection schemes, and investing in infrastructure and other services to reduce spatial segregation.
This paper discusses the implications of climate change for fiscal, financial, and macroeconomic policies. Most pressing is the use of carbon taxes (or equivalent trading systems) to implement the emissions mitigation pledges submitted by 186 countries for the December 2015 Paris Agreement while providing revenue for lowering other taxes or debt. Carbon pricing in developing countries would effectively mobilize climate finance, and carbon price floor arrangements are a promising way to coordinate policies internationally. Targeted fiscal measures that are tailored to national circumstances and robust across climate scenarios are needed to counter private sector under-investment in climate adaptation. And increased disclosure of carbon footprints, stress testing of asset values, and greater proliferation of hedging instruments, will facilitate low-emission investments and climate risk diversification through financial markets.
External Assessments in Special Cases presents the pilot External Balances Assessment methodology developed by IMF staff for estimating current account and exchange rate gaps for a group of advanced and emerging market economies, and discusses modifications to take account of special cases. Different approaches to external assessments for countries with special circumstances are evaluated, and some tools presented that could be used to inform sound judgment on the part of those conducting such assessments.
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting.
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