COVID-19 was declared a pandemic by WHO on March 11, 2020, the first non-influenza pandemic, affecting more than 200 countries and areas, with more than 5•9 million cases by May 31, 2020. Countries have developed strategies to deal with the COVID-19 pandemic that fit their epidemiological situations, capacities, and values. We describe China's strategies for prevention and control of COVID-19 (containment and suppression) and their application, from the perspective of the COVID-19 experience to date in China. Although China has contained severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and nearly stopped indigenous transmission, a strong suppression effort must continue to prevent re-establishment of community transmission from importation-related cases. We believe that case finding and management, with identification and quarantine of close contacts, are vitally important containment measures and are essential in China's pathway forward. We describe the next steps planned in China that follow the containment effort. We believe that sharing countries' experiences will help the global community manage the COVID-19 pandemic by identifying what works in the struggle against SARS-CoV-2.

This paper evaluates the impact of housing market spillovers on a small open economy, namely South Africa, using a small-open economy new Keynesian dynamic stochastic general equilibrium model (SOE-NKDSGE) which explicitly incorporates a housing sector. Using quarterly data covering the period of 1971:Q1-2015:Q3, we obtain the following set of results: (a) Over the business cycle, the housing preference shock and the technology shock in the consumption sector drive most of the fluctuations of real house price; (b) The spillover effects of the housing market to the boarder economy are not negligible; (c) The central bank of South Africa has actively responded to house price movements over the past 45 years; and (d) The flexible exchange rate policy has helped South Africa maintain the macroeconomic stability to a large extent.

This paper utilizes the recently developed methods of compressing the parameters and the data for a high-dimensional vector autoregression (VAR) to forecast economic policy uncertainty (EPU) of Brazil, China, India and Russia (BRIC) based on EPUs of additional 18 other developed and developing countries. In line with the recent literature on spillover of EPUs across countries, we show that incorporating information of EPUs of other countries does indeed produce gains in forecasting the EPU of the BRIC bloc, irrespective of whether we compress the parameters or the data.

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This paper explores the role of business cycle proxies, measured by the output gap at the global, regional and local levels, as potential predictors of stock market volatility in the emerging BRICS nations. We observe that the emerging BRICS nations display a rather heterogeneous pattern when it comes to the relative role of idiosyncratic factors as a predictor of stock market volatility. While domestic output gap is found to capture significant predictive information for India and China particularly, the business cycles associated with emerging economies and the world in general are strongly important for the BRIC countries and weakly for South Africa, especially in the post-global financial crisis era. The findings suggest that despite the increase in the financial integration of world capital markets, emerging economies can still bear significant exposures to idiosyncratic risk factors, an issue of high importance for the profitability of global diversification strategies.

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Most dynamic stochastic general equilibrium (DSGE) models with a housing market do not explicitly include a rental market and assume a tight mapping between house prices and rents over the business cycle. However, rents are much smoother than house prices in the data. We match this feature of the data by adding both an owner‐occupied housing market and a rental market in a standard DSGE model. The intertemporal preference shock accounts for more than half of the variation in house prices and contributes to residential investment fluctuations through the liquidity constraint, and nominal rigidity in rental contracts captures the variation in the price‐rent ratio.

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