Bayesian Model Selection for Beta Autoregressive Processes

We deal with Bayesian inference for Beta autoregressive processes. We restrict our attention to the class of conditionally linear processes. These processes are particularly suitable for forecasting purposes, but are difficult to estimate due to the constraints on the parameter space. We provide a full Bayesian approach to the estimation and include the parameter restrictions in the inference problem by a suitable specification of the prior distributions. Moreover in a Bayesian framework parameter estimation and model choice can be solved simultaneously. In particular we suggest a Markov-Chain Monte Carlo (MCMC) procedure based on a Metropolis-Hastings within Gibbs algorithm and solve the model selection problem following a reversible jump MCMC approach

eWOM and growth strategies for the tourism industry in maritime museum networks. The case of the ARCA Adriatica tourist product

Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented

Hierarchical Species Sampling Models

This paper introduces a general class of hierarchical nonparametric prior distributions. The random probability measures are constructed by a hierarchy of generalized species sampling processes with possibly non-diffuse base measures. The proposed framework provides a general probabilistic foundation for hierarchical random measures with either atomic or mixed base measures and allows for studying their properties, such as the distribution of the marginal and total number of clusters. We show that hierarchical species sampling models have a Chinese Restaurants Franchise representation and can be used as prior distributions to undertake Bayesian nonparametric inference. We provide a method to sample from the posterior distribution together with some numerical illustrations. Our class of priors includes some new hierarchical mixture priors such as the hierarchical Gnedin measures, and other well-known prior distributions such as the hierarchical Pitman-Yor and the hierarchical normalized random measures

eWom and sentiment analysis to support decision processes within maritime heritage museum networks. The case of Arca Adriatica

Museum networks are proliferating in the Mediterranean area showing new forms of collaboration between public and private institutions. Museums and heritage conservation play a fundamental role in tourism development. The purpose of the present working paper is to provide an analysis of the museum network experience in order to define a set of useful and viable marketing strategies to be adopted by the museum management with respect to the relative tourist context. The case of the Arca Adriatica maritime museum network - a network of eight maritime museums representing the core asset of an elaborated tourist product - has been analyzed and considered particularly relevant and of peculiar interest. After the analysis of the museum network and its most important related points of interest, managerial recommendations within strategic and tactical perspectives are hence presented

Dynamic identification of the Qutb Minar, New Delhi, India

Eu-India Economic Cross Cultural Programme “Improving the Seismic Resistance of Cultural Heritage Buildings” - Contract ALA-95-23-2003-077-122.Central Building Research Institute, Roorkee, India.Technical University of Catalonia, Barcelona, Spain.Archaeological Survey of India

Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model

News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. Our model contributes to the literature in several directions: 1) we provide a model-embedded data-driven interpretation for the latent leaning of news outlets in terms of media bias; 2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered

Relating Group Size and Posting Activity of an Online Community of Financial Investors: Regularities and Seasonal Patterns

Group size can potentially affect collective activity and individual propensity to contribute to collective goods. Mancur Olson, in his Logic of Collective Action, argued that individual contribution to a collective good tends to be lower in groups of large size. Today, online communication platforms represent an interesting ground to study such collaborative dynamics under possibly different conditions (e.g., lower costs related to gather and share information). This paper examines the relationship between group size and activity in an online financial forum, where users invest time in sharing news, analysis and comments with other investors. We looked at about 24 million messages shared in more than ten years in the finanzaonline.com online forum. We found that the relationship between the number of active users and the number of posts shared by those users is of the power type (with exponent α\u3e1) and is subject to periodic fluctuations, mostly driven by hour-of-the-day and day-of-the-week effects. The daily patterns of the exponent showed a divergence between working week and weekend days. In general, the exponent was lower before noon, where investors are typically interested in market news, higher in the late afternoon, where markets are closing and investors need better understanding of the situation. Further research is needed, especially at the micro level, to dissect the mechanisms behind these regularities

Fiscal Policy Regimes in Resource-Rich Economies

We analyse fiscal policy in resource-rich economies using a novel Bayesian regime-switching panel model. The identified regimes capture pro- or countercyclical fiscal behaviour, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to sixteen oil-producing economies, we show that fiscal policy has alternated between a procyclical and countercyclical regime multiple times over the sample. Furthermore, we find fiscal policy to be the most volatile in the procyclical regime and that the probability of being in the procyclical regime is higher for OPEC countries rather than non OPEC countries. We also show that following either an increase or decrease in oil revenues, the growth in government expenditure mostly increases, suggesting there is an upward bias in expenditures in oil-producing countries. These are new findings in the literature