Chen, C., J. Wakefield, and T. Lumely. 2014. “The Use of Sampling Weights in Bayesian Hierarchical Models for Small Area Estimation.” Spatial and Spatio-Temporal Epidemiology 11: 33–43. Doi: https://doi.org/10.1016/j.sste.2014.07.002 .
Citro, C.F. 2014. “From Multiple Modes for Surveys to Multiple Data Sources for Estimates.” Survey Methodology 40(2): 137–161.
Daas, P.J., M.J. Puts, B. Buelens, and P.A. van den Hurk. 2015. “Big Data as a Source for Official Statistics.” Journal of Official Statistics 31(2): 249
Abad, A. and E.M. Quilis. 2005. “Software to Perform Temporal Disaggregation of Economic Time Series.” Eurostat, Working Papers and Series. Available at: http://ec.europa.eu/eurostat/documents/4187653/5774917/LN-SR012007-EN.PDF/c83eb69ee3a9-4fdd-923d-76c09fea6f7b (accessed October 2015).
Abad, A., A. Cuevas, and E.M. Quilis. 2007. “Chain-Linked Volume Indexes: a Practical Guide.” Universidad Carlos III de Madrid, Instituto Flores de Lemus, Boletín de Inflación y Análisis Macroeconómico 157: 72–85. Available at http://e-archivo.uc3m
MoonJung Cho, John L. Eltinge, Julie Gershunskaya and Larry Huff
Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic tools for the evaluation of these patterns of variability. These tools are for (1) identification of predictor variables that explain some of the observed temporal and cross-sectional variability in published standard errors; (2) evaluation of the proportion of variability attributable to the abovementioned predictors, equation error and estimation error, respectively; and (3) comparison of equation error variances across groups defined by observable predictor variables. The primary ideas are motivated and illustrated by an application to the U.S. Current Employment Statistics program.
Interpreting changes between point estimates at different waves may be misleading if we do not take the sampling variation into account. It is therefore necessary to estimate the standard error of these changes in order to judge whether or not the observed changes are statistically significant. This involves the estimation of temporal correlations between cross-sectional estimates, because correlations play an important role in estimating the variance of a change in the cross-sectional estimates. Standard estimators for correlations cannot be used because of the rotation used in most panel surveys, such as the European Union Statistics on Income and Living Conditions (EU-SILC) surveys. Furthermore, as poverty indicators are complex functions of the data, they require special treatment when estimating their variance. For example, poverty rates depend on poverty thresholds which are estimated from medians. We propose using a multivariate linear regression approach to estimate correlations by taking into account the variability of the poverty threshold. We apply the approach proposed to the Turkish EU-SILC survey data.
Stefano Marchetti, Caterina Giusti, Monica Pratesi, Nicola Salvati, Fosca Giannotti, Dino Pedreschi, Salvatore Rinzivillo, Luca Pappalardo and Lorenzo Gabrielli
The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.
This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements.
Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance.
We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.
_FR_TechnicalReports.zip (accessed 8 November 2017).
Ruiz, F. and F.J. Goerlich. 2014. “Taxonomía y representacio´n de los cambios en los municipios espan˜oles.” Working Paper No. WP-EC 2014-01. Valencia: Instituto Valenciano de Investigaciones Econo´micas. Doi: http://dx.medra.org/10.12842/WPEC_201401.
Sindoni, G., S. De Francisci, M. Paolucci, and L. Tininini. 2002. “Experiences in Developing a Spatio-Temporal Information System.” Research in Official Statistics 1: 45-57. Available at: http://ec.europa.eu/eurostat/documents/3217494/5644045/KS-CS-02-001-EN.PDF/69d
: 815 – 826. Doi: http://dx.doi.org/10.1080/01621459.1997.10474037 .
Marhuenda, Y., I. Molina, and D. Morales. 2013. “Small Area Estimation with Spatio-Temporal Fay-Herriot Models.” Computational Statistics and Data Analysis 58: 308–325. Doi: http://dx.doi.org/10.1016/j.csda.2012.09.002 .
Molina, I., B. Nandram, and J.N.K. Rao. 2014. “Small Area Estimation of General Parameters with Application to Poverty Indicators: A Hierarchical Bayes Approach.” The Annals of Applied Statistics 8: 852–885. Doi: http://dx.doi.org/10.1214/13-AOAS702 .
Price Indexes . Queensland: University of Queensland (CEPA Working Paper Series No: WP03/2013).
Sun, H., Y. Tu, and S.-M. Yu. 2005. “A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis.” The Journal of Real Estate Finance and Economics 31: 155–187. DOI: https://doi.org/10.1007/s11146-005-1370-0 .
Tu, Y., S.-M. Yu, and H. Sun. 2004. “Transaction-Based Office Price Indexes: A Spatiotemporal Modeling Approach.” Real Estate Economics 32: 297–328. DOI: http://dx.doi.org/10.1111/j.1080-8620.2004.00093.x .
van de Minne, A
Modelling with Cointegrated Trends.” Computational Statistics and Data Analysis 56(10): 2918-2933. Doi: https://doi.org/10.1016/j.csda.2012.02.008.
Moauro, F. and G. Savio. 2005. “Temporal Disaggregation Using Multivariate Structural Time Series Models.” The Econometrics Journal 8(2): 214-234. Doi: http://dx.doi.org/10.1111/j.1368-423X.2005.00161.x.
Morley, J.C., C.R. Nelson, and E. Zivot. 2003. “Why Are the Beveridge-Nelson and Unobserved Components Decompositions of GDP so Different?” Review of Economics and Statistics 85(2): 235