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Jel Classification:F47 

Working Paper
Nowcasting Tail Risks to Economic Activity with Many Indicators

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, as well as classical and Bayesian quantile regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically improves as time moves ...
Working Papers , Paper 202013R2

Working Paper
Oil prices and the global economy: is it different this time around?

The recent plunge in oil prices has brought into question the generally accepted view that lower oil prices are good for the US and the global economy. In this paper, using a quarterly multi-country econometric model, we first show that a fall in oil prices tends relatively quickly to lower interest rates and inflation in most countries, and increase global real equity prices. The effects on real output are positive, although they take longer to materialize (around 4 quarters after the shock). We then re-examine the effects of low oil prices on the US economy over different sub-periods using ...
Globalization Institute Working Papers , Paper 277

Working Paper
Current Account Adjustment and Retained Earnings

This paper develops a formal strategy to calculate current accounts with retained earnings (RE) on equity investment and analyzes their adjustment during the global financial crisis. RE are the part of companies' profits which are reinvested and not distributed to shareholders as dividends. International statistical standards treat RE on foreign direct investment and RE on portfolio investment differently: while the former enter the current and financial account, the latter do not. We show that this differential treatment strongly affects current accounts of several advanced economies, ...
Globalization Institute Working Papers , Paper 345

Journal Article
Alternative Indicators for Chinese Economic Activity Using Sparse PLS Regression

Official Chinese GDP growth rates have been remarkably smooth over the past decade, in contrast with alternative Chinese economic data. To better identify Chinese business cycles, we construct a sparse partial least squares (PLS) factor from a wide array of Chinese higher-frequency data, targeted toward variables that are highly correlated with important aspects of the Chinese economy. Our resulting alternative growth indicator clearly identifies Chinese business cycle fluctuations and it performs well both in out-of-sample testing for China as well as when applied to other economies. Using ...
Economic Policy Review , Volume 26 , Issue 4 , Pages 39-68

Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that ...
Working Papers , Paper 202102R

Working Paper
Nowcasting Tail Risks to Economic Activity with Many Indicators

This paper focuses on nowcasts of tail risk to GDP growth, with a potentially wide array of monthly and weekly information. We consider different models (Bayesian mixed frequency regressions with stochastic volatility, classical and Bayesian quantile regressions, quantile MIDAS regressions) and also different methods for data reduction (either forecasts from models that incorporate data reduction or the combination of forecasts from smaller models). Our results show that, within some limits, more information helps the accuracy of nowcasts of tail risk to GDP growth. Accuracy typically ...
Working Papers , Paper 202013R

Working Paper
New Perspectives on Forecasting Inflation in Emerging Market Economies: An Empirical Assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1 to 12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors - based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic ...
Globalization Institute Working Papers , Paper 338

Working Paper
The U.S. oil supply revolution and the global economy

This paper investigates the global macroeconomic consequences of falling oil prices due to the oil revolution in the United States, using a Global VAR model estimated for 38 countries/regions over the period 1979Q2 to 2011Q2. Set-identification of the U.S. oil supply shock is achieved through imposing dynamic sign restrictions on the impulse responses of the model. The results show that there are considerable heterogeneities in the responses of different countries to a U.S. supply-driven oil price shock, with real GDP increasing in both advanced and emerging market oil-importing economies, ...
Globalization Institute Working Papers , Paper 263

Working Paper
Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Incoming data in 2020 posed sizable challenges for the use of VARs in economic analysis: Enormous movements in a number of series have had strong effects on parameters and forecasts constructed with standard VAR methods. We propose the use of VAR models with time-varying volatility that include a treatment of the COVID extremes as outlier observations. Typical VARs with time-varying volatility assume changes in uncertainty to be highly persistent. Instead, we adopt an outlier-adjusted stochastic volatility (SV) model for VAR residuals that combines transitory and persistent changes in ...
Working Papers , Paper 202102

Working Paper
Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and ...
Working Papers , Paper 202002R

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