Copyright Page
© 2022 International Monetary Fund
WP/22/226
IMF Working Paper
Western Hemisphere Department
Modeling and Forecasting Monthly Tourism Arrivals to Aruba Since COVID-19 Pandemic
Prepared by Olga G. Bespalova
Authorized for distribution by Nicole Laframboise
November 2022
IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.
ABSTRACT: 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 significant 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.
RECOMMENDED CITATION: Olga G. Bespalova, 2022. “Modeling and Forecasting Monthly Tourism Arrivals Since the COVID-19 Pandemic: Aruba Case,” IMF Working Papers 2022/226, International Monetary Fund.
| JEL Classification Numbers: | C22; C53; F47; L83 |
| Keywords: | Econometric Modeling; Forecasting; tourism; arrivals; tourist arrivals; Google Trends; flight capacity utiilization; load factor; time-series models; time-series econometrics; arima; Aruba; Covid-19; Pandemic |
| Author’s E-Mail Address: | obespalova@imf.org |
| JEL Classification Numbers: | C22; C53; F47; L83 |
| Keywords: | Econometric Modeling; Forecasting; tourism; arrivals; tourist arrivals; Google Trends; flight capacity utiilization; load factor; time-series models; time-series econometrics; arima; Aruba; Covid-19; Pandemic |
| Author’s E-Mail Address: | obespalova@imf.org |
Title Page
WORKING PAPERS
Modeling and Forecasting Monthly Tourism Arrivals to Aruba Since COVID-19 Pandemic
Prepared by Olga G. Bespalova
Contents
Glossary
Executive Summary
I. Introduction
II. Literature Review
III. Data: Sample, Sources, and Their Properties
IV. Methodology, Models, and Assumptions
V. Main Empirical Results: Estimated Models and Forecasts
VI. Conclusion
Annex 1. Notes on Google Trends Data .
Annex 2. Panel Charts: Dynamics in Data
Annex 3. Main Emirical Results
Annex 4. Panel Charts: Projected Arrivals
Annex 5. Robustness Check
References
FIGURES
Figure 1. Tourist Arrivals to Aruba: Dynamics, Sources of Origin, and Macroeconomic Impact
Figure 2. Role of Regressors and Model Terms in the RMSE improvement
Figure 3. In-Sample Tourist Arrivals Projections
Figure A2-1. Dynamics in Tourist Arrivals and Explanatory Variables
Figure A4-1. Projected Arrivals: Comparison across ARIMA classes, before and after IC
TABLES
Table 1. Summary Statistics and Data Properties (2004m1 – 2019m12)
Table 2. Tourist Arrivals to Aruba—Model w/RPCE, GT, CAP, VIX (with intercept correction)
Table 3. Tourist Arrivals to Aruba—ARIMA 202—Models’ Comparison
Table 4. Variables’ Role in Reduction of the Forecast Error (est. sample: 2004m1 – 2021m12)
Table A3-1. Tourist Arrivals to Aruba—Model w/RPCE, GT, CAP, VIX, IC and Monthly Dummies
Table A3-2. Tourist Arrivals to Aruba — Auxiliary Models w/RPCE (Sample: 2004m1-2021m12)
Table A3-3. Contributions of Exogenous Regressors to the Model Fit
Table A3-4. Forecast Evaluation — Contributions of Regressors to Forecast Accuracy
Table A5-1. Tourist Arrivals to Aruba—Model w/RPI, GT, CAP, VIX, IC and Monthly Dummies
Table A5-2. Tourist Arrivals to Aruba—Models with alternative risk measures
Glossary
| ADF test | Augmented Dickey–Fuller test |
| AIC | Akaike Information Criteria |
| ARIMA | Auto Regressive Integrated Moving Average |
| ARIMAX | ARIMA model with exogenous variables |
| ARMA | Auto Regressive Moving Average |
| ATA | Aruba Tourism Authority |
| BEA | Bureau of Economic Analysis |
| BTS | Bureau of Transportation Statistics |
| CBA | Central Bank of Aruba |
| COVID-19 | Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) |
| DFGLS | The modified Dickey–Fuller t test for a unit root in a time series |
| EPU | Economic Policy Uncertainty |
| GDP | Gross Domestic Product |
| GT | Google Trends |
| IC | Intercept Correction |
| MAE | Mean Absolute Error |
| NSA | Not Seasonally Adjusted |
| REER | Real Effective Exchange Rate |
| RGDP | Real Gross Domestic Product |
| RMSE | Root Mean Squared Error |
| RPCE | Real Personal Consumption Expenditures |
| SA | Seasonally Adjusted |
| US | United States |
| Theil U | Theil Inequality Coefficient |
| WHO | World Health Organization |
| WTO | World Tourism Organization |
The work benefited from a collaboration with Dr. Gerald Kock, currently Advisor to Honorary Mr. Geoffrey B. Weaver, the Minister of Economic Affairs, Communication and Sustainable Development of Aruba. Dr. Kock contributed to the motivation of this research project, collected initial Google Trends dataset during 01/08/2021 – 02/06/2021, verified tourism arrivals data; provided background on the tourism industry in Aruba, and commented on the early draft of this research paper during his tenure as Policy Advisor at the Department of Economic Affairs, Commerce, and Industry (DEACI) in Aruba. The author is grateful to S. Cevik for clarifications on using Google Trends in Cevik (2020). This paper incorporated feedback from the internal reviewers, attendees of the presentations at the IMF Caribbean divisions’ meeting (07/18/2022), IMF WHD research seminar (09/07/2022), and 24th Federal Forecasters Conference. The author is particularly grateful for the helpful suggestions to C. Jackson (RES), T. Komatsuzaki, D. Kovtun, N. Laframboise, S. J. Pienknagura Loor, S. Acosta Ormaechea (all – WHD). Any errors are solely of the author.