[
Batóg, J., Foryś, I., Gaca, R., Głuszak, M., & Konowalczuk, J. (2019). Investigating the impact of airport noise and land use restrictions on house prices: Evidence from selected regional airports in Poland. Sustainability (Basel), 11(2), 412. https://doi.org/10.3390/su11020412
]Search in Google Scholar
[
Bełej, M. (2022). Does Google Trends show the strength of social interest as a predictor of housing price dynamics? Sustainability, 14(9), 5601. https://doi.org/10.3390/su14095601
]Search in Google Scholar
[
Bełej, M., Cellmer, R., Foryś, I., & Głuszak, M. (2023). Airports in the urban landscape: Externalities, stigmatization and housing market. Land Use Policy, 126, 106540. https://doi.org/10.1016/j.landusepol.2023.106540
]Search in Google Scholar
[
Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural timeseries models. https://projecteuclid.org/journals/annals-of-applied-statistics/volume-9/issue-1/Inferring-causal-impact-using-Bayesian-structural-time-series-models/10.1214/14-AOAS788.short https://doi.org/10.1214/14-AOAS788
]Search in Google Scholar
[
Brodersen, K. H., Hauser, A., & Hauser, M. A. (2017). Package CausalImpact. Google LLC., https://mirror.las.iastate.edu/CRAN/web/packages/CausalImpact/CausalImpact.pdf
]Search in Google Scholar
[
BSTS. (2023). Bayesian Structural Time Series | SAP Help Portal. https://help.sap.com/docs/SAP_HANA_PLATFORM/2cfbc5cf2bc14f028cfbe2a2bba60a50/b9972576368640da9831d73a9d749c3b.html
]Search in Google Scholar
[
Carneiro, H. A., & Mylonakis, E. (2009). Google trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49(10), 1557–1564. https://doi.org/10.1086/630200 PMID:19845471
]Search in Google Scholar
[
Castelnuovo, E., & Tran, T. D. (2017). Google it up! A google trends-based uncertainty index for the United States and Australia. Economics Letters, 161, 149–153. https://doi.org/10.1016/j.econlet.2017.09.032
]Search in Google Scholar
[
Chatzakou, D., Vakali, A., & Kafetsios, K. (2017). Detecting variation of emotions in online activities. Expert Systems with Applications, 89, 318–332. https://doi.org/10.1016/j.eswa.2017.07.044
]Search in Google Scholar
[
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. The Economic Record, 88(s1), 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
]Search in Google Scholar
[
Flavián-Blanco, C., Gurrea-Sarasa, R., & Orús-Sanclemente, C. (2011). Analyzing the emotional outcomes of the online search behavior with search engines. Computers in Human Behavior, 27(1), 540–551. https://doi.org/10.1016/j.chb.2010.10.002
]Search in Google Scholar
[
García, C. B., García, J., López Martín, M. M., & Salmerón, R. (2015). Collinearity: Revisiting the variance inflation factor in ridge regression. Journal of Applied Statistics, 42(3), 648–661. https://doi.org/10.1080/02664763.2014.980789
]Search in Google Scholar
[
Göhring, W. (2004). The Memorandum ‘Sustainable Information Society’. In Minier, P. & Susini, A. (Hrsg.), Sh@ring – EnviroInfo 2004. http://enviroinfo.eu/sites/default/files/pdfs/vol110/0278.pdf
]Search in Google Scholar
[
Habdas, M. (2020a). Odszkodowania dla właścicieli nieruchomości zlokalizowanych w obszarach ograniczonego użytkowania dla lotnisk–wyzwania dotyczące prawidłowego ustalenia zakresu odpowiedzialności odszkodowawczej i podlegającej kompensacji szkody–część 1. Przegląd Sądowy, 5, 7–31.
]Search in Google Scholar
[
Habdas, M. (2020b). Polish dilemmas in compensating landowners in the vicinity of airports–black letter law vs. Law in action. Studia Prawnicze KUL, 4, 27–61.
]Search in Google Scholar
[
Huarng, K.-H., Hui-Kuang Yu, T., & Rodriguez-Garcia, M. (2020). Qualitative analysis of housing demand using Google trends data. Ekonomska Istrazivanja, 33(1), 2007–2017. https://doi.org/10.1080/1331677X.2018.1547205
]Search in Google Scholar
[
Khafidli, M. K., & Choiruddin, A. (2022). Forecast of aviation traffic in Indonesia based on Google Trend and macroeconomic data using long short-term memory. 2022 International Conference on Data Science and Its Applications (ICoDSA), 220–225. https://doi.org/10.1109/ICoDSA55874.2022.9862894
]Search in Google Scholar
[
Konowalczuk, J., Habdas, M., Foryś, I., & Drobiec, Ł. (2021). Wartość nieruchomości w sąsiedztwie lotnisk: Metodyka szacowania szkód i ustalania odszkodowań. Wydawnictwo C. H. Beck.
]Search in Google Scholar
[
Li Long, C., Guleria, Y., & Alam, S. (2021). Air passenger forecasting using Neural Granger causal Google Trend queries. Journal of Air Transport Management, 95, 102083. https://doi.org/10.1016/j.jairtraman.2021.102083
]Search in Google Scholar
[
Limnios, A. C., & You, H. (2021). Can Google Trends improve housing market forecasts? Curiosity: Interdisciplinary Journal of Research and Innovation, 1(2), 21987.
]Search in Google Scholar
[
Massicotte, P., Eddelbuettel, D., & Massicotte, M. P. (2016). Package ‘gtrendsR’. R Package. https://cran.curtin.edu.au/web/packages/gtrendsR/gtrendsR.pdf
]Search in Google Scholar
[
Matias, Y. (2013). Nowcasting with Google Trends. International Symposium on String Processing and Information Retrieval, 4. https://doi.org/10.1007/978-3-319-02432-5_4
]Search in Google Scholar
[
Mavragani, A., Ochoa, G., & Tsagarakis, K. P. (2018). Assessing the methods, tools, and statistical approaches in Google Trends research: Systematic review. Journal of Medical Internet Research, 20(11), e270. https://doi.org/10.2196/jmir.9366 PMID:30401664 https://doi.org/10.2196/preprints.9366
]Search in Google Scholar
[
Miles, J. (2014). Tolerance and Variance Inflation Factor. In R. S. Kenett, N. T. Longford, W. W. Piegorsch, & F. Ruggeri (Eds.), Wiley StatsRef: Statistics Reference Online (1st ed.). Wiley., https://doi.org/10.1002/9781118445112.stat06593
]Search in Google Scholar
[
Olszak, C., & Ziemba, E. (2009). The information society development strategy on a regional level. Issues in Informing Science and Information Technology, 6, 213–225. https://doi.org/10.28945/1054
]Search in Google Scholar
[
Rizun, N., & Baj-Rogowska, A. (2021). Can web search queries predict prices change on the real estate market? IEEE Access: Practical Innovations, Open Solutions, 9, 70095–70117. https://doi.org/10.1109/ACCESS.2021.3077860
]Search in Google Scholar
[
Scott, S. L., & Varian, H. R. (2014). Predicting the present with Bayesian structural time series. International Journal of Mathematical Modelling and Numerical Optimisation, 5(1/2), 4. https://doi.org/10.1504/IJMMNO.2014.059942
]Search in Google Scholar
[
Wilcox, R. R. (2003). Least squares regression and Pearson’s correlation. In Applying Contemporary Statistical Techniques, 173–206. Elsevier. https://doi.org/10.1016/B978-012751541-0/50027-4
]Search in Google Scholar
[
Woloszko, N. (2020). Tracking activity in real time with Google Trends. https://www.oecd-ilibrary.org/economics/tracking-activity-in-real-time-with-google-trends_6b9c7518-en
]Search in Google Scholar
[
Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences of the United States of America, 112(47), 14473–14478. https://doi.org/10.1073/pnas.1515373112 PMID:26553980
]Search in Google Scholar