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200301 ||| eng |
| 020 |
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|a 9781513526348
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| 100 |
1 |
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|a Cevik, Serhan
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| 245 |
0 |
0 |
|a Where Should We Go? Internet Searches and Tourist Arrivals
|c Serhan Cevik
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2020
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| 300 |
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|a 16 pages
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| 651 |
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4 |
|a Bahamas, The
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| 653 |
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|a Recreation
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| 653 |
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|a Prices, Business Fluctuations, and Cycles: Forecasting and Simulation
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| 653 |
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|a Forecasting and Simulation: Models and Applications
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| 653 |
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|a National accounts
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| 653 |
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|a Mobility, Unemployment, and Vacancies: General
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| 653 |
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|a Forecasting and Other Model Applications
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Unemployment: Models, Duration, Incidence, and Job Search
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| 653 |
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|a Foreign exchange
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| 653 |
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|a Gambling
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| 653 |
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|a Forecasting
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| 653 |
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|a Personal Income, Wealth, and Their Distributions
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| 653 |
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|a Sports
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| 653 |
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|a Economic sectors
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| 653 |
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|a Economic Forecasting
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| 653 |
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|a Foreign Exchange
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| 653 |
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|a Personal income
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| 653 |
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|a Income
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| 653 |
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|a Currency
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| 653 |
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|a Time-Series Models
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| 653 |
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|a Hospitality, leisure & tourism industries
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| 653 |
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|a Tourism
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| 653 |
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|a Real effective exchange rates
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| 653 |
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|a Dynamic Treatment Effect Models
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| 653 |
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|a Industries: Hospital,Travel and Tourism
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| 653 |
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|a Economic forecasting
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| 653 |
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|a Restaurants
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Dynamic Quantile Regressions
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a IMF Working Papers
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| 028 |
5 |
0 |
|a 10.5089/9781513526348.001
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| 856 |
4 |
0 |
|u https://elibrary.imf.org/view/journals/001/2020/022/001.2020.issue-022-en.xml?cid=48949-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 330
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| 520 |
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|a The widespread availability of internet search data is a new source of high-frequency information that can potentially improve the precision of macroeconomic forecasting, especially in areas with data constraints. This paper investigates whether travel-related online search queries enhance accuracy in the forecasting of tourist arrivals to The Bahamas from the U.S. The results indicate that the forecast model incorporating internet search data provides additional information about tourist flows over a univariate approach using the traditional autoregressive integrated moving average (ARIMA) model and multivariate models with macroeconomic indicators. The Google Trends-augmented model improves predictability of tourist arrivals by about 30 percent compared to the benchmark ARIMA model and more than 20 percent compared to the model extended only with income and relative prices
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