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Brandon Buell, Reda Cherif, Carissa Chen, Jiawen Tang, and Nils Wendt
The COVID-19 pandemic underscores the critical need for detailed, timely information on its evolving economic impacts, particularly for Sub-Saharan Africa (SSA) where data availability and lack of generalizable nowcasting methodologies limit efforts for coordinated policy responses. This paper presents a suite of high frequency and granular country-level indicator tools that can be used to nowcast GDP and track changes in economic activity for countries in SSA. We make two main contributions: (1) demonstration of the predictive power of alternative data variables such as Google search trends and mobile payments, and (2) implementation of two types of modelling methodologies, machine learning and parametric factor models, that have flexibility to incorporate mixed-frequency data variables. We present nowcast results for 2019Q4 and 2020Q1 GDP for Kenya, Nigeria, South Africa, Uganda, and Ghana, and argue that our factor model methodology can be generalized to nowcast and forecast GDP for other SSA countries with limited data availability and shorter timeframes.
Brandon Buell, Reda Cherif, Carissa Chen, Jiawen Tang, and Nils Wendt

regression Figure 20. All non-zero coefficients for Kenya XGBoost regression Figure 21. Top 20 most important variables for Kenya random forest regression Figure 22. Uganda GDP: Autocorrelation, Partial Autocorrelation, & Normal Quantile–Quantile Plots of Year-over-Year Quarterly GDP Figure 23. Kenya GDP: Autocorrelation, Partial Autocorrelation, & Normal Quantile–Quantile Plots of Year-over-Year Quarterly GDP Figure 24. Ghana GDP: Autocorrelation, Partial Autocorrelation, & Normal Quantile–Quantile Plots of Year-over-Year Quarterly GDP Figure 25. Uganda

Travis Mitchell, Roland Craigwell, and Mr. Rupert D Worrell

resulting matrices. The problem with this approach is that the distributions, in general, are not nested. An alternative strategy, employed in this paper, is to start by comparing the unconditional distributions (standardized on their means) with commonly used distributions such as the normal and t distributions, using the quantile plots found in the econometric software package EVIEWS. This provides a first sense of the nature of the distribution. We then consider the conditional mean derived using Equation (5) , using an equation specification that ensures that the

Travis Mitchell, Roland Craigwell, and Mr. Rupert D Worrell
This paper is a first analysis of daily transactions in the foreign exchange market of Barbados, a small open economy that has had an unchanged peg to the U.S. dollar for over 30 years. As a result of the credibility of the peg, we expect that capital flows will respond to differentials between U.S. and comparable Barbadian interest rates and that this will result in uncovered interest parity, when allowance is made for market frictions and large discrete events. The results are consistent with this hypothesis about the motivation for foreign exchange transactions.
Brandon Buell, Reda Cherif, Carissa Chen, Jiawen Tang, and Nils Wendt

Kenya Figure 20. All non-zero coefficients for Kenya XGBoost regression Figure 21. Top 20 most important variables for Kenya random forest regression Appendix II. Factor Model Supplemental Materials Figure 22. Uganda GDP: Autocorrelation, Partial Autocorrelation, & Normal Quantile– Quantile Plots of Year-over-Year Quarterly GDP Figure 23. Kenya GDP: Autocorrelation, Partial Autocorrelation, & Normal Quantile– Quantile Plots of Year-over-Year Quarterly GDP Figure 24. Ghana GDP: Autocorrelation, Partial

Mr. Alessandro Prati, Mr. Giuseppe Bertola, and Mr. Leonardo Bartolini
We propose a model of the interbank money market with an explicit role for central bank intervention and periodic reserve requirements, and study the interaction of profit-maximizing banks with a central bank targeting interest rates at high frequency. The model yields predictions on biweekly patterns of the federal funds rate’s volatility and on its response to changes in target rates and in intervention procedures, such as those implemented by the Federal Reserve in 1994. Theoretical results are consistent with empirical patterns of interest rate volatility in the U.S. market for federal funds.
Mr. Alessandro Prati, Mr. Giuseppe Bertola, and Mr. Leonardo Bartolini

essentially unchanged. 6 Confirming this property, a quantile-quantile plot of the distribution of estimates of v t against a randomly generated t -distribution (with the estimated degrees of freedom) was very close to a straight line. To verify the robustness of our results to the assumed distribution of the errors v t , we re-estimated the model using a Generalized Error Distribution (GED), obtaining very similar results. Hamilton ( 1996 , 1997) captures the same features by a mixture of normal distributions for the innovations.

Antoine Bouveret, Mr. Peter Breuer, Ms. Yingyuan Chen, David Jones, and Tsuyoshi Sasaki

a l ⁢     ( η R T ) ( 0 ) and V n o n − R T ( t ) | N n o n − R T ( t ) > N n o n − R T ( t − ) ∼ E x p o n e n t i a l ⁢     ( η n o n − R T ) ( 14 ) The choice of exponential distributions is justified by looking at the Quantile-Quantile plot of the volume traded on October 15

Mr. Tobias Adrian, Christopher J. Erceg, Marcin Kolasa, Jesper Lindé, and Pawel Zabczyk

outflows and exchange rate depreciations. Figure 7. Relationship Between Selected Simulated Variables Figure 8 presents the probability distributions of key model variables, both in the form of standard kernel density estimates as well as quantile-quantile plots, meant to highlight non-normal tail dynamics. The first row shows that output is clearly “at risk” in vulnerable EME economies, with “sudden stops” generating a significantly heavier left tail. An occasionally binding debt limit also explains the asymmetry in the trade balance (second row) as it

Antoine Bouveret, Mr. Peter Breuer, Ms. Yingyuan Chen, David Jones, and Tsuyoshi Sasaki
Changes in the structure of the U.S. Treasury market over recent years may have increased risks to financial stability. Traditional market makers have changed their liquidity provision by increasingly switching from risk warehousing to risk distribution, and a new breed of market maker has emerged with the rise of electronic trading. The “flash rally” of October 15, 2014 provides a clear example of how those risks can materialize. Based on an in-depth analysis of the event—complementing the authorities’ work—we suggest i) providing incentives for liquidity provision, ii) improving market safeguards, and iii) enhancing the regulation of the Treasury market.