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In this paper we propose a novel approach to obtain the predictive density of global GDP growth. It hinges upon a bottom-up probabilistic model that estimates and combines single countries’ predictive GDP growth densities, taking into account cross-country interdependencies. Speci?cally, we model non-parametrically the contemporaneous interdependencies across the United States, the euro area, and China via a conditional kernel density estimation of a joint distribution. Then, we characterize the potential ampli?cation e?ects stemming from other large economies in each region—also with kernel density estimations—and the reaction of all other economies with para-metric assumptions. Importantly, each economy’s predictive density also depends on a set of observable country-speci?c factors. Finally, the use of sampling techniques allows us to aggregate individual countries’ densities into a world aggregate while preserving the non-i.i.d. nature of the global GDP growth distribution. Out-of-sample metrics con?rm the accuracy of our approach.

Macroeconomics --- Personal Finance -Taxation --- Hypothesis Testing --- General Aggregative Models: Forecasting and Simulation --- Prices, Business Fluctuations, and Cycles: Forecasting and Simulation --- Financial Crises --- Personal Income and Other Nonbusiness Taxes and Subsidies --- Economic & financial crises & disasters --- Public finance & taxation --- Global financial crisis of 2008-2009 --- Personal income tax --- Financial crises --- Taxes --- Global Financial Crisis, 2008-2009 --- Income tax --- United States

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The growth-at-risk (GaR) framework links current macrofinancial conditions to the distribution of future growth. Its main strength is its ability to assess the entire distribution of future GDP growth (in contrast to point forecasts), quantify macrofinancial risks in terms of growth, and monitor the evolution of risks to economic activity over time. By using GaR analysis, policymakers can quantify the likelihood of risk scenarios, which would serve as a basis for preemptive action. This paper offers practical guidance on how to conduct GaR analysis and draws lessons from country case studies. It also discusses an Excel-based GaR tool developed to support the IMF’s bilateral surveillance efforts.

Accounting --- Macroeconomics --- Money and Monetary Policy --- Real Estate --- General Aggregative Models: Forecasting and Simulation --- International Business Cycles --- Monetary Growth Models --- Price Level --- Inflation --- Deflation --- Housing Supply and Markets --- Public Administration --- Public Sector Accounting and Audits --- Monetary Policy, Central Banking, and the Supply of Money and Credit: General --- Property & real estate --- Financial reporting, financial statements --- Monetary economics --- Growth-at-risk assessment --- Asset prices --- Housing prices --- Financial statements --- Credit aggregates --- Financial sector policy and analysis --- Prices --- Public financial management (PFM) --- Money --- Financial risk management --- Housing --- Finance, Public --- Credit --- Peru