Karim Barhoumi, Seung Mo Choi, Tara Iyer, Jiakun Li, Franck Ouattara, Mr. Andrew J Tiffin, and Jiaxiong Yao
The COVID-19 crisis has had a tremendous economic impact for all countries. Yet, assessing the full impact of the crisis has been frequently hampered by the delayed publication of official GDP statistics in several emerging market and developing economies. This paper outlines a machine-learning framework that helps track economic activity in real time for these economies. As illustrative examples, the framework is applied to selected sub-Saharan African economies. The framework is able to provide timely information on economic activity more swiftly than official statistics.
Nils Mæhle, Tibor Hlédik, Mikhail Pranovich, Carina Selander, and Mikhail Pranovich
This paper takes stock of forecasting and policy analysis system capacity development (FPAS CD), drawing extensively on the experience and lessons learned from developing FPAS capacity in the central banks. By sharing the insights gained during FPAS CD delivery and outlining the typical tools developed in the process, the paper aims to facilitate the understanding of FPAS CD within the IMF and to inform future CD on building macroeconomic frameworks. As such, the paper offers a qualitative assessment of the experience with FPAS CD delivery and the use of FPAS in the decision-making process in central banks.
Bertrand Gruss, Mrs. Sandra V Lizarazo Ruiz, and Mr. Francesco Grigoli
Anchoring of inflation expectations is of paramount importance for central banks’ ability to deliver stable inflation and minimize price dispersion. Relying on daily interest rates and inflation forecasts from major financial institutions in the United States, we calculate monetary policy surprises of individual analysts as the unexpected changes in the federal funds rate before the meetings of the Federal Reserve Board. We then assess the effect of monetary policy surprises on the dispersion of inflation expectations, a proxy for the extent of anchoring, which is based on the same analysts’ inflation projections submit-ted after the Fed meetings. With an identification strategy that hinges on a tight window around the Fed meetings, we find that monetary policy surprises lead to an increase in the dispersion of inflation expectations up to nine months after the policy meeting. We rationalize these results with a partial equilibrium model that features rational expectations and sticky information. When we allow the degree of information rigidity to depend on the realization of firm-specific shocks, the theoretical results are qualitatively consistent and quantitatively close to the empirical evidence.
Against the backdrop of an ongoing review of the inflation-targeting framework, this paper examines the real-time inflation forecasts of the Bank of Canada with the aim of identifying potential areas for improvement. Not surprisingly, the results show that errors in forecasting non-core inflation (commodity prices etc.) are found to be the largest contributors to overall inflation forecast errors. Perhaps more importantly, relatively small core inflation forecast errors appear to mask large and offsetting errors related to the output gap and the policy interest rate, partly reflecting a tendency to overestimate the neutral nominal policy rate in real time. Faced with these uncertainties, the Governing Council’s gradual approach to changing its policy settings appears to have served it well.
Inflation forecasts are modelled as monotonically diverging from an estimated long-run anchor point, or “implicit anchor”, towards actual inflation as the forecast horizon shortens. Fitting the model with forecasts by analysts, businesses and trade unions for South Africa, we find that inflation expectations have become increasingly strongly anchored. That is, the degree to which the estimated implicit anchor pins down inflation expectations at longer horizons has generally increased. Estimated inflation anchors of analysts lie within the 3–6 percent inflation target range of the central bank. However, the implicit anchors of businesses and trade unions, who are directly involved in the setting of wages and prices that drive the inflation process, have remained above the top end of the official target range. Possible explanations for these phenomena are discussed.
This study documents a semi-structural model developed for Sri Lanka. This model, extended with a fiscal sector block, is expected to serve as a core forecasting model in the process of the Central Bank of Sri Lanka’s move towards flexible inflation targeting. The model includes a forward-looking endogenous interest rate and foreign exchange rate policy rules allowing for flexible change in policy behavior. It is a gap model that allows for simultaneous identification of business cycle position and long-term equilibrium. The model was first calibrated and then its data-fit was improved using Bayesian estimation technique with relatively tight priors.
Mr. Sergi Lanau, Adrian Robles, and Mr. Frederik G Toscani
We study inflation dynamics in Colombia using a bottom-up Phillips curve approach. This allows us to capture the different drivers of individual inflation components. We find that the Phillips curve is relatively flat in Colombia but steeper than recent estimates for the U.S. Supply side shocks play an important role for tradable and food prices, while indexation dynamics are important for non-tradable goods. We show that besides allowing for a more detailed understanding of inflation drivers, the bottom-up approach also improves on an aggregate Phillips curve in terms of forecasting ability. In the baseline forecast scenario, both headline and core inflation converge towards the Central Bank’s inflation target of 3 percent by end-2018 but these favorable inflation dynamics are vulnerable to large supply shocks.
Macroeconomic forecasts are persistently too optimistic. This paper finds that common factors related to general uncertainty about U.S. macrofinancial prospects and global demand drive this overoptimism. These common factors matter most for advanced economies and G- 20 countries. The results suggest that an increase in uncertainty-driven overoptimism has dampening effects on next-year real GDP growth rates. This implies that incorporating the common structure governing forecast errors across countries can help improve subsequent forecasts.
External headwinds, together with domestic vulnerabilities, have loomed over the prospects of emerging markets in recent years. We propose an empirical toolbox to quantify the impact of external macro-financial shocks on domestic economies in parsimonious way. Our model is a Bayesian VAR consisting of two blocks representing home and foreign factors, which is particularly useful for small open economies. By exploiting the mixed-frequency nature of the model, we show how the toolbox can be used for “nowcasting” the output growth. The conditional forecast results illustrate that regular updates of external information, as well as domestic leading indicators, would significantly enhance the accuracy of forecasts. Moreover, the analysis of variance decompositions shows that external shocks are important drivers of the domestic business cycle.