[go: up one dir, main page]
create a website
A tool to nowcast tourist overnight stays with payment data and complementary indicators. (2023). Mariani, Vincenzo ; Crispino, Marta.
In: Questioni di Economia e Finanza (Occasional Papers).
RePEc:bdi:opques:qef_746_23.

Full description at Econpapers || Download paper

Cited: 0

Citations received by this document

Cites: 39

References cited by this document

Cocites: 20

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

    This document has not been cited yet.

References

References cited by this document

  1. Aastveit, K. A., Fastbø, T. M., Granziera, E., Paulsen, K. S., and Torstensen, K. N. (2020). Nowcasting Norwegian household consumption with debit card transaction data. Norges Bank.

  2. Aladangady, A., Aron-Dine, S., Dunn, W., Feiveson, L., Lengermann, P., and Sahm, C. (2021). From Transactions Data to Economic Statistics: Constructing Real-Time, HighFrequency, Geographic Measures of Consumer Spending. University of Chicago Press.

  3. Antolini, F. and Grassini, L. (2019). Foreign arrivals nowcasting in Italy with Google Trends data. Quality & Quantity, 53.

  4. Aprigliano, V., Ardizzi, G., and Monteforte, L. (2019). Using Payment System Data to Forecast Economic Activity. International Journal of Central Banking, 15(4):55–80.

  5. Ardizzi, G., Nobili, A., and Rocco, G. (2021). A game changer in payment habits: Evidence from daily data during a pandemic. Social Science Research Network.
    Paper not yet in RePEc: Add citation now

  6. Artola, C. and Martı́nez-Galán, E. (2012). Tracking the future on the web: construction of leading indicators using internet searches. Banco de Espana Occasional Paper, (1203).

  7. Askitas, N., Zimmermann, K., (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly (formerly: Konjunkturpolitik), 55:107–120.

  8. Bangwayo-Skeete, P. F. and Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46:454 – 464.

  9. Breiman, L. (2001). Random forests. Machine learning, 45:5–32.
    Paper not yet in RePEc: Add citation now

  10. Camacho, M. and Pacce, M. J. (2018). Forecasting travellers in Spain with Google’s search volume indices. Tourism Economics, 24(4):434–448.

  11. Carboni, A., Doria, C., and Catalano, C. (2022). How big data can improve the quality of tourism statistics? The Bank of Italy experience in compiling the BoP travel item.
    Paper not yet in RePEc: Add citation now

  12. D’Amuri, F. and Marcucci, J. (2017). The predictive power of google searches in forecasting us unemployment. International Journal of Forecasting, 33(4):801–816.

  13. de Kort, R. E. (2017). Forecasting tourism demand through search queries and machine learning. IFC Bulletins, 44.

  14. Della Corte, V., Doria, C., and Oddo, G. (2021). The impact of Covid-19 on international tourism flows to Italy: evidence from mobile phone data.

  15. Della Penna, N. and Huang, H. (2009). Constructing Consumer Sentiment Index for U.S. Using Google Searches. Working Papers 2009-26, University of Alberta, Department of Economics.
    Paper not yet in RePEc: Add citation now
  16. Demma, C. (2021). Il settore turistico e la pandemia di Covid-19. Note Covid-19.
    Paper not yet in RePEc: Add citation now

  17. Di Giacinto, V., Monteforte, L., Filippone, A., Montaruli, F., and Ropele, T. (2019). ITER: a quartely indicator of regional economic activity in Italy. Questioni di Economia e Finanza, (489).

  18. Eurostat (2021). Eurostat statistics explained. https://ec.europa.eu/eurostat/ statistics-explained/index.php?title=Glossary:Nights_spent. Accessed: 202107 -26.
    Paper not yet in RePEc: Add citation now
  19. Feng, Y., Li, G., Sun, X., and Li, J. (2019). Forecasting the number of inbound tourists with google trends. Procedia Computer Science, 162:628 – 633.
    Paper not yet in RePEc: Add citation now

  20. Fonzo, T. D. and Marini, M. (2011). Simultaneous and two-step reconciliation of systems of time series: methodological and practical issues. Journal of the Royal Statistical Society. Series C (Applied Statistics), 60(2):143–164.

  21. Galbraith, J. W. and Tkacz, G. (2018). Nowcasting with payments system data. International Journal of Forecasting, 34(2):366–376.

  22. Giacomini, R. and Rossi, B. (2010). Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25(4):595–620.

  23. Havranek, T. and Zeynalov, A. (2019). Forecasting tourist arrivals: Google trends meets mixed-frequency data. Tourism Economics.

  24. Hyndman, R. J. and Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, Articles, 27(3):1–22.

  25. Istat (2020). Conto satellite del Turismo per l’Italia, year 2017. Statistiche report. https: //www.istat.it/it/files//2020/06/Conto-satellite-turismo.pdf. Accessed: 202201 -05.
    Paper not yet in RePEc: Add citation now
  26. Istat (2021). Occupancy in collective tourist accomodation. https://www.unwto.org/ country-profile-outbound-tourism. Accessed: 2021-07-20.
    Paper not yet in RePEc: Add citation now
  27. Ji-yuan, W., Geng, P., and Shou-yang, W. (2017). Model selection on tourism forecasting: A comparison between Bayesian model averaging and Lasso. African Journal of Business Management, 11:158–167.
    Paper not yet in RePEc: Add citation now

  28. Law, R., Li, G., Fong, D. K. C., and Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75:410 – 423.

  29. Park, S., Lee, J., and Song, W. (2017). Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data. Journal of Travel & Tourism Marketing, 34(3):357– 368.
    Paper not yet in RePEc: Add citation now

  30. Rossi, B. and Sekhposyan, T. (2010). Have economic models’ forecasting performance for US output growth and inflation changed over time, and when? International Journal of Forecasting, 26(4):808–835.

  31. Statcounter (2017). Search Engine Market Share Worldwide. https://gs.statcounter.
    Paper not yet in RePEc: Add citation now
  32. Sun, S., Wei, Y., Tsui, K.-L., and Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70:1 – 10.
    Paper not yet in RePEc: Add citation now
  33. UNWTO (2021). Data on outbound tourism by country. https://www.unwto.org/ country-profile-outbound-tourism. Accessed: 2021-07-20.
    Paper not yet in RePEc: Add citation now
  34. Varian, H. and Choi, H. (2009). Predicting the Present with Google Trends. Economic Record, 88.
    Paper not yet in RePEc: Add citation now

  35. Verbaan, R., Bolt, W., and van der Cruijsen, C. (2017). Using debit card payments data for nowcasting Dutch household consumption. DNB Working Papers 571, Netherlands Central Bank, Research Department.

  36. Webb, G. (2009). Internet search statistics as a source of business intelligence: Searches on foreclosure as an estimate of actual home foreclosures. Issues in Information Systems, 10.
    Paper not yet in RePEc: Add citation now
  37. Wu, L. and Brynjolfsson, E. (2015). The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales. In Economic Analysis of the Digital Economy, pages 89–118. National Bureau of Economic Research, Inc.
    Paper not yet in RePEc: Add citation now

  38. Yang, X., Pan, B., Evans, J. A., and Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46:386 – 397.

  39. Yoon, Y. and Uysal, M. (2005). An examination of the effects of motivation and satisfaction on destination loyalty: a structural model. Tourism Management, 26(1):45–56.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. Monitoring Consumption Switzerland: Data, Background, and Use Cases. (2023). Fengler, Matthias ; Rohrkemper, Robert ; Lalive, Rafael ; Koeniger, Winfried ; Huwyler, Jonas ; Brown, Martin.
    In: Economics Working Paper Series.
    RePEc:usg:econwp:2023:01.

    Full description at Econpapers || Download paper

  2. Monitoring consumption Switzerland: data, background, and use cases. (2023). Koeniger, Winfried ; Fengler, Matthias ; Rohrkemper, Robert ; Lalive, Rafael ; Huwyler, Jonas ; Brown, Martin.
    In: Swiss Journal of Economics and Statistics.
    RePEc:spr:sjecst:v:159:y:2023:i:1:d:10.1186_s41937-023-00108-9.

    Full description at Econpapers || Download paper

  3. Nowcasting economic activity using transaction payments data. (2023). Beyeler, Simon ; Felber, Laura.
    In: Working Papers.
    RePEc:snb:snbwpa:2023-01.

    Full description at Econpapers || Download paper

  4. Measuring tourism demand nowcasting performance using a monotonicity test. (2023). Liu, Ying ; Song, Haiyan ; Wang, Yongjing.
    In: Tourism Economics.
    RePEc:sae:toueco:v:29:y:2023:i:5:p:1302-1327.

    Full description at Econpapers || Download paper

  5. Weekly economic activity: Measurement and informational content. (2023). Guggia, Valentino ; Glocker, Christian ; Wegmuller, Philipp.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:1:p:228-243.

    Full description at Econpapers || Download paper

  6. ifo Konjunkturprognose Sommer 2023: Inflation flaut langsam ab – aber Konjunktur lahmt noch. (2023). Šauer, Radek ; Rathje, Ann-Christin ; Mohrle, Sascha ; Link, Sebastian ; Lehmann, Robert ; Lay, Max ; Fourne, Friederike ; Fell, Maximilian ; Ederer, Stefan ; Zarges, Lara ; Wollmershauser, Timo ; Scheiblecker, Marcus ; Schasching, Moritz.
    In: ifo Schnelldienst.
    RePEc:ces:ifosdt:v:76:y:2023:i:sonderausgabe:p:01-53.

    Full description at Econpapers || Download

  7. A tool to nowcast tourist overnight stays with payment data and complementary indicators. (2023). Mariani, Vincenzo ; Crispino, Marta.
    In: Questioni di Economia e Finanza (Occasional Papers).
    RePEc:bdi:opques:qef_746_23.

    Full description at Econpapers || Download paper

  8. Where should we go? Internet searches and tourist arrivals. (2022). Cevik, Serhan.
    In: International Journal of Finance & Economics.
    RePEc:wly:ijfiec:v:27:y:2022:i:4:p:4048-4057.

    Full description at Econpapers || Download paper

  9. Using a consumer choice model to explain the effect of the newly developed oxford COVID-19 government stringency measure on hotel occupancy rates. (2022). Gani, Azmat.
    In: Quality & Quantity: International Journal of Methodology.
    RePEc:spr:qualqt:v:56:y:2022:i:6:d:10.1007_s11135-022-01323-x.

    Full description at Econpapers || Download paper

  10. A Tale of two narratives: assessing the sociological hypothesis of the appeal of the US dollar in Argentina. (2022). Contiggiani, Federico ; Tohme, Fernando ; Fioriti, Andres ; Delbianco, Fernando.
    In: Quality & Quantity: International Journal of Methodology.
    RePEc:spr:qualqt:v:56:y:2022:i:5:d:10.1007_s11135-021-01280-x.

    Full description at Econpapers || Download paper

  11. The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai. (2022). Rashad, Ahmed.
    In: Forecasting.
    RePEc:gam:jforec:v:4:y:2022:i:3:p:36-684:d:868494.

    Full description at Econpapers || Download paper

  12. Forecasting Regional Industrial Production with High-Frequency Electricity Consumption Data. (2022). Mohrle, Sascha ; Lehmann, Robert.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_9917.

    Full description at Econpapers || Download paper

  13. Measuring the Velocity of Money. (2022). Takes, Frank W ; Luedtke, Allison.
    In: Papers.
    RePEc:arx:papers:2209.01512.

    Full description at Econpapers || Download paper

  14. Weekly Economic Activity: Measurement and Informational Content. (2021). Wegmueller, Philipp ; Guggia, Valentino ; Glocker, Christian ; Wegmuller, Philipp.
    In: WIFO Working Papers.
    RePEc:wfo:wpaper:y:2021:i:627.

    Full description at Econpapers || Download paper

  15. Payment Habits During COVID-19: Evidence from High-Frequency Transaction Data. (2021). Welte, Angelika ; Dahlhaus, Tatjana.
    In: Staff Working Papers.
    RePEc:bca:bocawp:21-43.

    Full description at Econpapers || Download paper

  16. Bank transactions embeddings help to uncover current macroeconomics. (2021). Zaytsev, Alexey ; Begicheva, Maria.
    In: Papers.
    RePEc:arx:papers:2110.12000.

    Full description at Econpapers || Download paper

  17. Machine Learning Time Series Regressions With an Application to Nowcasting. (2021). Striaukas, Jonas ; Ghysels, Eric ; Babii, Andrii.
    In: LIDAM Discussion Papers LFIN.
    RePEc:ajf:louvlf:2021004.

    Full description at Econpapers || Download paper

  18. Economic indicators forecasting in presence of seasonal patterns: time series revision and prediction accuracy. (2020). Mattera, Raffaele ; giacalone, massimiliano ; Nissi, Eugenia.
    In: Quality & Quantity: International Journal of Methodology.
    RePEc:spr:qualqt:v:54:y:2020:i:1:d:10.1007_s11135-019-00935-0.

    Full description at Econpapers || Download paper

  19. How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data. (2020). Stepner, Michael ; Chetty, Raj ; Insights, The Opportunity ; Hendren, Nathaniel ; Friedman, John N.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:27431.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2024-12-19 15:04:06 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated October, 6 2023. Contact: CitEc Team.