Abstract Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers. The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes. The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.
{"title":"Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes","authors":"Fabiana Rocci, R. Varriale, Orietta Luzi","doi":"10.2478/jos-2022-0025","DOIUrl":"https://doi.org/10.2478/jos-2022-0025","url":null,"abstract":"Abstract Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers. The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes. The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"533 - 556"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69223216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Elleouet, P. Graham, N. Kondratev, Abby Morgan, R. Green
Abstract Many countries conduct a full census survey to report official population statistics. As no census survey ever achieves 100% response rate, a post-enumeration survey (PES) is usually conducted and analysed to assess census coverage and produce official population estimates by geographic area and demographic attributes. Considering the usually small size of PES, direct estimation at the desired level of disaggregation is not feasible. Design-based estimation with sampling weight adjustment is a commonly used method but is difficult to implement when survey nonresponse patterns cannot be fully documented and population benchmarks are not available. We overcome these limitations with a fully model-based Bayesian approach applied to the New Zealand PES. Although theory for the Bayesian treatment of complex surveys has been described, published applications of individual level Bayesian models for complex survey data remain scarce. We provide such an application through a case study of the 2018 census and PES surveys. We implement a multilevel model that accounts for the complex design of PES. We then illustrate how mixed posterior predictive checking and cross-validation can assist with model building and model selection. Finally, we discuss potential methodological improvements to the model and potential solutions to mitigate dependence between the two surveys.
{"title":"Small Domain Estimation of Census Coverage – A Case Study in Bayesian Analysis of Complex Survey Data","authors":"J. Elleouet, P. Graham, N. Kondratev, Abby Morgan, R. Green","doi":"10.2478/jos-2022-0034","DOIUrl":"https://doi.org/10.2478/jos-2022-0034","url":null,"abstract":"Abstract Many countries conduct a full census survey to report official population statistics. As no census survey ever achieves 100% response rate, a post-enumeration survey (PES) is usually conducted and analysed to assess census coverage and produce official population estimates by geographic area and demographic attributes. Considering the usually small size of PES, direct estimation at the desired level of disaggregation is not feasible. Design-based estimation with sampling weight adjustment is a commonly used method but is difficult to implement when survey nonresponse patterns cannot be fully documented and population benchmarks are not available. We overcome these limitations with a fully model-based Bayesian approach applied to the New Zealand PES. Although theory for the Bayesian treatment of complex surveys has been described, published applications of individual level Bayesian models for complex survey data remain scarce. We provide such an application through a case study of the 2018 census and PES surveys. We implement a multilevel model that accounts for the complex design of PES. We then illustrate how mixed posterior predictive checking and cross-validation can assist with model building and model selection. Finally, we discuss potential methodological improvements to the model and potential solutions to mitigate dependence between the two surveys.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"767 - 792"},"PeriodicalIF":1.1,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48106183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract National statistical offices have faced unprecedented circumstances in the modern history of economic measurement. There were dramatically changing consumer expenditure patterns due to pandemic conditions, with lockdowns and fear of infection making many goods and services unavailable. We examine the implications of changing relative expenditures for the construction of Consumer Price Indexes, with special reference to the treatment of prices for unavailable products. We conclude that for many purposes, it would be useful for statistical agencies to establish a continuous consumer expenditure survey. We also examine various other practical pandemic induced CPI measurement problems.
{"title":"Measuring Inflation under Pandemic Conditions","authors":"W. Diewert, Kevin J. Fox","doi":"10.2478/jos-2022-0012","DOIUrl":"https://doi.org/10.2478/jos-2022-0012","url":null,"abstract":"Abstract National statistical offices have faced unprecedented circumstances in the modern history of economic measurement. There were dramatically changing consumer expenditure patterns due to pandemic conditions, with lockdowns and fear of infection making many goods and services unavailable. We examine the implications of changing relative expenditures for the construction of Consumer Price Indexes, with special reference to the treatment of prices for unavailable products. We conclude that for many purposes, it would be useful for statistical agencies to establish a continuous consumer expenditure survey. We also examine various other practical pandemic induced CPI measurement problems.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"255 - 285"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46752676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Diewert and Fox (2022) examine various implications of the 2020 COVID-19 pandemic for constructing consumer price indexes. The authors state that the pandemic caused major changes in consumption expenditures and shares which makes fixed basket index number formulae inapplicable. They emphasize the need for more frequent surveys of consumer expenditure which will enable compilation of the Fisher index which is considered superior to the traditional Laspeyres or Young indexes. In addition, Diewert and Fox discuss the use of various “new” technologies such as web scraping, scanner data, and information from transactions through credit cards to estimate consumption expenditure.
{"title":"“Measuring Inflation under Pandemic Conditions”: A Comment","authors":"Naohito Abe","doi":"10.2478/jos-2022-0015","DOIUrl":"https://doi.org/10.2478/jos-2022-0015","url":null,"abstract":"Abstract Diewert and Fox (2022) examine various implications of the 2020 COVID-19 pandemic for constructing consumer price indexes. The authors state that the pandemic caused major changes in consumption expenditures and shares which makes fixed basket index number formulae inapplicable. They emphasize the need for more frequent surveys of consumer expenditure which will enable compilation of the Fisher index which is considered superior to the traditional Laspeyres or Young indexes. In addition, Diewert and Fox discuss the use of various “new” technologies such as web scraping, scanner data, and information from transactions through credit cards to estimate consumption expenditure.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"295 - 300"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41536925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Hill, Michael Scholz, C. Shimizu, Miriam Steurer
Abstract Rolling-time-dummy (RTD) is a hedonic method used by a number of countries to compute their official house price indexes (HPIs). The RTD method requires less data and is more adaptable than other hedonic methods, which makes it well suited for computing higher frequency HPIs (e.g., monthly or weekly). In this article, we address three key issues relating to RTD. First, we develop a method for determining the optimal length of the rolling window. Second, we consider variants on the standard way of linking the current period with earlier periods, and show how the optimal linking method can be determined. Third, we propose three ways of modifying the RTD method to make it more robust to periods of low transaction volume. These modifications could prove useful for countries using the RTD method in their official HPIs.
{"title":"Rolling-Time-Dummy House Price Indexes: Window Length, Linking and Options for Dealing with Low Transaction Volume","authors":"R. Hill, Michael Scholz, C. Shimizu, Miriam Steurer","doi":"10.2478/jos-2022-0007","DOIUrl":"https://doi.org/10.2478/jos-2022-0007","url":null,"abstract":"Abstract Rolling-time-dummy (RTD) is a hedonic method used by a number of countries to compute their official house price indexes (HPIs). The RTD method requires less data and is more adaptable than other hedonic methods, which makes it well suited for computing higher frequency HPIs (e.g., monthly or weekly). In this article, we address three key issues relating to RTD. First, we develop a method for determining the optimal length of the rolling window. Second, we consider variants on the standard way of linking the current period with earlier periods, and show how the optimal linking method can be determined. Third, we propose three ways of modifying the RTD method to make it more robust to periods of low transaction volume. These modifications could prove useful for countries using the RTD method in their official HPIs.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"127 - 151"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42421621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robert S. Martin, A. Sadler, Sara Stanley, W. Thompson, Jonathan C. Weinhagen
Abstract We re-estimate historical U.S. Producer Price Indexes (PPI) using the geometric Young formula at the elementary level. The geometric Young has better axiomatic properties than the modified Laspeyres, and may better approximate a feasible economic target. We find in most cases, indexes that use the geometric Young escalate between 0.1 and 0.3 percentage points less each year than those that use the modified Laspeyres. However, for wholesale and retail trade, as well as some other services, the differences are much larger. As a result, using the geometric Young at the elementary level lowers the U.S. PPI for Final Demand by 0.55 percentage points per year during the study period, a magnitude larger than what has been previously found for the U.S. Consumer Price Index.
{"title":"The Geometric Young Formula for Elementary Aggregate Producer Price Indexes","authors":"Robert S. Martin, A. Sadler, Sara Stanley, W. Thompson, Jonathan C. Weinhagen","doi":"10.2478/jos-2022-0011","DOIUrl":"https://doi.org/10.2478/jos-2022-0011","url":null,"abstract":"Abstract We re-estimate historical U.S. Producer Price Indexes (PPI) using the geometric Young formula at the elementary level. The geometric Young has better axiomatic properties than the modified Laspeyres, and may better approximate a feasible economic target. We find in most cases, indexes that use the geometric Young escalate between 0.1 and 0.3 percentage points less each year than those that use the modified Laspeyres. However, for wholesale and retail trade, as well as some other services, the differences are much larger. As a result, using the geometric Young at the elementary level lowers the U.S. PPI for Final Demand by 0.55 percentage points per year during the study period, a magnitude larger than what has been previously found for the U.S. Consumer Price Index.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"239 - 253"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44139524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comment on the Article by W. Erwin Diewert and Kevin J. Fox","authors":"Carsten Boldsen","doi":"10.2478/jos-2022-0013","DOIUrl":"https://doi.org/10.2478/jos-2022-0013","url":null,"abstract":"","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"287 - 289"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49292946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Dawber, Nora Würz, Paul A. Smith, Tanya Flower, Heledd Thomas, T. Schmid, N. Tzavidis
Abstract Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.
{"title":"Experimental UK Regional Consumer Price Inflation with Model-Based Expenditure Weights","authors":"J. Dawber, Nora Würz, Paul A. Smith, Tanya Flower, Heledd Thomas, T. Schmid, N. Tzavidis","doi":"10.2478/jos-2022-0010","DOIUrl":"https://doi.org/10.2478/jos-2022-0010","url":null,"abstract":"Abstract Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"213 - 237"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42748707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.
{"title":"Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy","authors":"Ilaria Benedetti, L. Biggeri, T. Laureti","doi":"10.2478/jos-2022-0004","DOIUrl":"https://doi.org/10.2478/jos-2022-0004","url":null,"abstract":"Abstract It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"57 - 82"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41602626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Hedonic regressions are widely used and recommended for property price index (PPI) measurement. Hedonic PPIs control for changes in the quality-mix of properties transacted that can confound measures of change in average property prices. The widespread adoption of the hedonic approach is primarily due to the increasing availability, in this digital age, of electronic data on advertised and transaction prices of properties and their price-determining characteristics. Yet hedonic PPIs are only as good as the underlying estimated hedonic regressions. Regression-based measures are unusual in official economic statistics. There is little technical support in the international Handbooks and Guides for diagnostic measures and graphical plots for estimated regression equations as applied to PPIs. These diagnostics are essential to the transparency and credibility of hedonic PPI measurement. This article seeks to remedy this.
{"title":"Econometric Issues in Hedonic Property Price Indices: Some Practical Help","authors":"M. Silver","doi":"10.2478/jos-2022-0008","DOIUrl":"https://doi.org/10.2478/jos-2022-0008","url":null,"abstract":"Abstract Hedonic regressions are widely used and recommended for property price index (PPI) measurement. Hedonic PPIs control for changes in the quality-mix of properties transacted that can confound measures of change in average property prices. The widespread adoption of the hedonic approach is primarily due to the increasing availability, in this digital age, of electronic data on advertised and transaction prices of properties and their price-determining characteristics. Yet hedonic PPIs are only as good as the underlying estimated hedonic regressions. Regression-based measures are unusual in official economic statistics. There is little technical support in the international Handbooks and Guides for diagnostic measures and graphical plots for estimated regression equations as applied to PPIs. These diagnostics are essential to the transparency and credibility of hedonic PPI measurement. This article seeks to remedy this.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"153 - 186"},"PeriodicalIF":1.1,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49618497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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