We study the dynamic price competition of hotels in Venice using publicly available data scraped from an online travel agency. This study poses two main challenges. First, the time series of prices recorded for each hotel encompasses a twofold time frame. For every single asking price for an overnight stay on a specific day, there is a corresponding time series of asking prices along the booking window on the online platforms. Second, the competition relations between different hoteliers is clearly unknown and needs to be discovered using a suitable methodology. We address these problems by proposing a novel Network Autoregressive model which is able to handle the peculiar threefold data structure of the data set with time-varying coefficients over the booking window. This approach allows us to uncover the competition network of the market players by employing a quick data-driven algorithm. Independent, mixed, and leader–follower relationships are detected, revealing the competitive dynamics of the destination, useful to managers and stakeholders.
{"title":"Unveiling Venice’s hotels competition networks from dynamic pricing digital market","authors":"Mirko Armillotta, K. Fokianos, A. Guizzardi","doi":"10.1093/jrsssa/qnad085","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad085","url":null,"abstract":"\u0000 We study the dynamic price competition of hotels in Venice using publicly available data scraped from an online travel agency. This study poses two main challenges. First, the time series of prices recorded for each hotel encompasses a twofold time frame. For every single asking price for an overnight stay on a specific day, there is a corresponding time series of asking prices along the booking window on the online platforms. Second, the competition relations between different hoteliers is clearly unknown and needs to be discovered using a suitable methodology. We address these problems by proposing a novel Network Autoregressive model which is able to handle the peculiar threefold data structure of the data set with time-varying coefficients over the booking window. This approach allows us to uncover the competition network of the market players by employing a quick data-driven algorithm. Independent, mixed, and leader–follower relationships are detected, revealing the competitive dynamics of the destination, useful to managers and stakeholders.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"25 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75025021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Natalie Shlomo’s Contribution to the Discussion of 'A system of population estimates compiled from administrative data only' by John Dunne and Li-Chun Zhang","authors":"N. Shlomo","doi":"10.1093/jrsssa/qnad101","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad101","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72822970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Jennet Woolford's contribution to the Discussion of 'A system of population estimates compiled from administrative data only' by John Dunne and Li-Chun Zhang","authors":"Jennet Woolford","doi":"10.1093/jrsssa/qnad094","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad094","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"1 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73877018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Memories for Professor Sir David R. Cox FRS","authors":"S. M. Bird","doi":"10.1093/jrsssa/qnad076","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad076","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"92 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76709609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Regression Analysis in R: A Comprehensive View for the Social Sciences","authors":"V. Kalyani","doi":"10.1093/jrsssa/qnad081","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad081","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"44 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89181336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover.
{"title":"Multilevel longitudinal analysis of social networks.","authors":"Johan Koskinen, Tom A B Snijders","doi":"10.1093/jrsssa/qnac009","DOIUrl":"https://doi.org/10.1093/jrsssa/qnac009","url":null,"abstract":"<p><p>Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover.</p>","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"186 3","pages":"376-400"},"PeriodicalIF":2.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/5b/19/qnac009.PMC10376442.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10286318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Risk Assessment: Theory, Methods, and Applications","authors":"M. Aalabaf-Sabaghi","doi":"10.1093/jrsssa/qnad080","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad080","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"29 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81805820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial version of multivariate Fay–Herriot model is introduced and small area predictor under this model is proposed. The residual maximum likelihood is employed for estimating the parameters of the proposed model. Analytical and bootstrap approaches for estimating the mean squared error (MSE) of the proposed predictor are also developed. The performance of the proposed predictor and the MSE estimators are evaluated through various simulation studies. The results evidently show that the proposed predictor outperforms the existing predictors. An application of the proposed methodology has also been made using the 2011–12 Consumer Expenditure Survey data of India.
{"title":"Small area estimation under a spatially correlated multivariate area-level model","authors":"Saurav Guha, Hukum Chandra","doi":"10.1093/jrsssa/qnad079","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad079","url":null,"abstract":"\u0000 Spatial version of multivariate Fay–Herriot model is introduced and small area predictor under this model is proposed. The residual maximum likelihood is employed for estimating the parameters of the proposed model. Analytical and bootstrap approaches for estimating the mean squared error (MSE) of the proposed predictor are also developed. The performance of the proposed predictor and the MSE estimators are evaluated through various simulation studies. The results evidently show that the proposed predictor outperforms the existing predictors. An application of the proposed methodology has also been made using the 2011–12 Consumer Expenditure Survey data of India.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"C-23 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84422052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we develop a methodology to detect structural breaks in multivariate time series data using the t-distributed stochastic neighbour embedding (t-SNE) technique and non-parametric spectral density estimates. By applying the proposed algorithm to the exchange rates of Indian rupee against four primary currencies, we establish that the coronavirus pandemic (COVID-19) has indeed caused a structural break in the volatility dynamics. Next, to study the effect of the pandemic on the Indian currency market, we provide a compact and efficient way of combining three models, each with a specific objective, to explain and forecast the exchange rate volatility. We find that a forward-looking regime change makes a drop in persistence, while an exogenous shock like COVID-19 makes the market highly persistent. Our analysis shows that although all exchange rates are found to be exposed to common structural breaks, the degrees of impact vary across the four series. Finally, we develop an ensemble approach to combine predictions from multiple models in the context of volatility forecasting. Using model confidence set procedure, we show that the proposed approach improves the accuracy from benchmark models. Relevant economic explanations to our findings are provided as well.
{"title":"New methods of structural break detection and an ensemble approach to analyse exchange rate volatility of Indian rupee during coronavirus pandemic","authors":"M. Mareeswaran, Shubhajit Sen, S. Deb","doi":"10.1093/jrsssa/qnad078","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad078","url":null,"abstract":"\u0000 In this work, we develop a methodology to detect structural breaks in multivariate time series data using the t-distributed stochastic neighbour embedding (t-SNE) technique and non-parametric spectral density estimates. By applying the proposed algorithm to the exchange rates of Indian rupee against four primary currencies, we establish that the coronavirus pandemic (COVID-19) has indeed caused a structural break in the volatility dynamics. Next, to study the effect of the pandemic on the Indian currency market, we provide a compact and efficient way of combining three models, each with a specific objective, to explain and forecast the exchange rate volatility. We find that a forward-looking regime change makes a drop in persistence, while an exogenous shock like COVID-19 makes the market highly persistent. Our analysis shows that although all exchange rates are found to be exposed to common structural breaks, the degrees of impact vary across the four series. Finally, we develop an ensemble approach to combine predictions from multiple models in the context of volatility forecasting. Using model confidence set procedure, we show that the proposed approach improves the accuracy from benchmark models. Relevant economic explanations to our findings are provided as well.","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"140 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80543857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proposer of the vote of thanks and contribution to the Discussion of ‘The Second Discussion Meeting on Statistical aspects of the Covid-19 Pandemic’","authors":"Sylvia Richardson","doi":"10.1093/jrsssa/qnad045","DOIUrl":"https://doi.org/10.1093/jrsssa/qnad045","url":null,"abstract":"","PeriodicalId":49983,"journal":{"name":"Journal of the Royal Statistical Society Series A-Statistics in Society","volume":"52 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85175840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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