Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
This paper improves short-term forecasting models of monthly tourism arrivals by estimating and evaluating a time-series model with exogenous regressors (ARIMA-X) using a case of Aruba, a small open tourism-dependent economy. Given importance of the US market for Aruba, it investigates informational value of Google Searches originating in the USA, flight capacity utilization on the US air-carriers, and per capita demand of the US consumers, given the volatility index in stock markets (VIX). It yields several insights. First, flight capacity is the best variable to account for the travel restrictions during the pandemic. Second, US real personal consumption expenditure becomes a more significnat predictor than income as the former better captured impact of the COVID-19 restrictions on the consumers’ behavior, while income boosted by the pandemic fiscal support was not fully directed to spending. Third, intercept correction improves the model in the estimation period. Finally, the pandemic changed econometric relationships between the tourism arrivals and their main determinants, and accuracy of the forecast models. Going forward, the analysts should re-estimate the models. Out-of-sample forecasts with 5 percent confidence intervals are produced for 18 months ahead.
Mr. Jiaqian Chen, Lucyna Gornicka, and Vaclav Zdarek
This paper documents five facts about inflation expectations in the euro area. First, individual inflation forecasts overreact to individual news. Second, the cross-section average of individual forecasts of inflation underreact to shocks initially, but overreacts in the medium term. Third, disagreement about future inflation increases in response to news when the current inflation is high, and declines when inflation is low, consistent with a zero lower bound of expectations. Fourth, overreaction of individual inflation forecasts to news increased after the global financial crisis (GFC). Fifth, the reaction of average expectations (and of actual inflation) to shocks became more muted post-GFC in the euro area, but not in the U.S.
Elías Albagli, Mr. Francesco Grigoli, and Emiliano Luttini
We show that firms rely on price changes observed along their supply chain to form expectations about aggregate inflation, and that these expectations have a complete pass-through to sales prices. Leveraging a unique dataset on Chilean firms merging expectation surveys and records from the VAT and customs registries, we document that changes in prices at which firms purchase inputs inform their forecasts of the economy’s inflation. This is the case even if changes in input costs do not determine the inflation outcome. These findings reject the full-information rational-expectations hypothesis and are consistent with firms’ disagreement about future inflation and inattention to macroeconomic news, which we document for Chile. Our results from a firm-level Phillips’ curve estimation suggest that firms’ beliefs about inflation are a key determinant for their price-setting decisions. Therefore, we argue that the channel we highlight in this paper has the potential to lead to dispersion in inflation expectations, price dispersion, and weaken the expectation channel of policies.
Inflation has been rising during the pandemic against supply chain disruptions and a multi-year boom in global owner-occupied house prices. We present some stylized facts pointing to house prices as a leading indicator of headline inflation in the U.S. and eight other major economies with fast-rising house prices. We then apply machine learning methods to forecast inflation in two housing components (rent and owner-occupied housing cost) of the headline inflation and draw tentative inferences about inflationary impact. Our results suggest that for most of these countries, the housing components could have a relatively large and sustained contribution to headline inflation, as inflation is just starting to reflect the higher house prices. Methodologically, for the vast majority of countries we analyze, machine-learning models outperform the VAR model, suggesting some potential value for incorporating such models into inflation forecasting.
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.