1,720,989 research outputs found
The wild bootstrap for multilevel models
No full textIn this paper we study the performance of the most popular bootstrap schemes for multilevel data. Also, we propose a modied version of the wild bootstrap procedure for hierarchical data structures. The wild bootstrap does not require homoscedasticity or assumptions on the distribution of the error processes. Hence, it is a valuable tool for robust inference in a multilevel framework. We assess the nite size performances of the schemes through a
Monte Carlo study. The results show that for big sample sizes it always pays o to adopt an agnostic approach as the wild bootstrap outperforms other techniques
A Multilevel Model with Time Series Components for the Analysis of Tribal Art Prices
No full textIn the present work, we extend the classic multilevel model to include time series components at the second level. In order to show its potentials, we perform an econometric analysis of the Tribal art market. In literature, art prices are modelled through the hedonic regression model, a classic fixed-effect model. We use, instead, a multilevel model for the analysis of Tribal art prices that takes into account the influence of time on artwork prices. Since we do not have repeated measurements of the same items over time, we propose to treat the data as two-level structured in that items are grouped in time points. Hence, the proposed model copes with the time dependence of random effects
A multilevel model for repeated cross-sectional data with stochastic volatility
No full textIn this paper we propose a multilevel approach for the analysis of repeated
cross-sectional data that exhibit volatility effects. We treat individuals as clustered
within time-points so that the dynamics over time is modelled at the second level.
Items sold in auction present a structure like that of repeated cross-sectional surveys
since different goods are sold at different time-points. For prices of artworks, as well
as for other assets (financial, insurance, etc.) the hypothesis of constant volatility appears
unreasonable. In this work we combine a multilevel model with autoregressive
random effects and a stochastic volatility model in order to account for the kurtosis
and the volatility pattern of prices. We apply the model to Tribal art auction prices
and show improvement over existing proposals both in terms of fit and forecastin
Multilevel models with stochastic volatility for repeated cross-sections: an application to tribal art prices
Full text linkIn this paper we introduce a multilevel specification with stochastic volatility for repeated cross-sectional data. Modelling the time dynamics in repeated cross sections requires a suitable adaptation of the multilevel framework where the individuals/items are modelled at the first level whereas the time component appears at thesecond level. We perform maximum likelihood estimation by means of a nonlinear state space approach combined with Gauss-Legendre quadrature methods to approximate the likelihood function. We apply the model to the first database of tribal art items sold in the most important auction houses worldwide. The model allows to account properly for the heteroscedastic and autocorrelated volatility observed and has superior forecasting performance. Also, it provides valuable information on market trends and on predictability of prices that can be used by art markets stakeholders
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Tribal Art Price (TAP) http://www.tribalartprice.it/
No full textTAP
http://www.tribalartprice.it/
sito dedicato all'analisi economica e statistica on demand delle aggiudicazioni delle aste di arte etnica
A multilevel model with autoregressive components for the analysis of tribal art prices
No full textIn this paper, we introduce a multilevel model specification with time-series components for the analysis of prices of artworks sold at auctions. Since auction data do not constitute a panel or a time series but are composed of repeated cross-sections, they require a specification with items at the first level nested in time-points. Our approach combines the flexibility of mixed effect models together with the predicting performance of time series as it allows to model the time dynamics directly. Model estimation is obtained by means of maximum likelihood through the expectation–maximization algorithm. The model is motivated by the analysis of the first database ethnic artworks sold in the most important auctions worldwide. The results show that the proposed specification improves considerably over classical proposals both in terms of fit and prediction
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