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.
This paper studies the historical importance of OPEC for oil price fluctuations. An event-study approach is used to identify the effects of OPEC announcements on oil price fluctuations. Results show that price volatility is higher than typical around OPEC meetings. Also, members' compliance, a proxy for credibility, has strongly fluctuated over time. An ordered multinomial logit framework identifies the main factors that explain OPEC's decisions to cut, maintain, or boost members' oil production and is able to successfully predict OPEC meeting outcomes 66 percent of the time, between 1989 and 2019. Cyclical oil price fluctuations (as opposed to persistent shifts in levels) drive OPEC’s decisions, suggesting that OPEC's objective is to stabilize the oil price rather than countering fundamental shifts in demand and supply. Low OPEC’s market share reduces the probability of a production cut. Finally, the transparency of OPEC's statements has modestly improved between 2002 and 2019.
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.