Abstract In the production of US agricultural official statistics, certain inequality and benchmarking constraints must be satisfied. For example, available administrative data provide an accurate lower bound for the county-level estimates of planted acres, produced by the U.S. Department of Agriculture’s (USDA) National Agricultural statistics Services (NASS). In addition, the county-level estimates within a state need to add to the state-level estimates. A sub-area hierarchical Bayesian model with inequality constraints to produce county-level estimates that satisfy these important relationships is discussed, along with associated measures of uncertainty. This model combines the County Agricultural Production Survey (CAPS) data with administrative data. Inequality constraints add complexity to fitting the model and present a computational challenge to a full Bayesian approach. To evaluate the inclusion of these constraints, the models with and without inequality constraints were compared using 2014 corn planted acres estimates for three states. The performance of the model with inequality constraints illustrates the improvement of county-level estimates in accuracy and precision while preserving required relationships.
{"title":"Hierarchical Bayesian Model with Inequality Constraints for US County Estimates","authors":"Lu Chen, B. Nandram, Nathan B. Cruze","doi":"10.2478/jos-2022-0032","DOIUrl":"https://doi.org/10.2478/jos-2022-0032","url":null,"abstract":"Abstract In the production of US agricultural official statistics, certain inequality and benchmarking constraints must be satisfied. For example, available administrative data provide an accurate lower bound for the county-level estimates of planted acres, produced by the U.S. Department of Agriculture’s (USDA) National Agricultural statistics Services (NASS). In addition, the county-level estimates within a state need to add to the state-level estimates. A sub-area hierarchical Bayesian model with inequality constraints to produce county-level estimates that satisfy these important relationships is discussed, along with associated measures of uncertainty. This model combines the County Agricultural Production Survey (CAPS) data with administrative data. Inequality constraints add complexity to fitting the model and present a computational challenge to a full Bayesian approach. To evaluate the inclusion of these constraints, the models with and without inequality constraints were compared using 2014 corn planted acres estimates for three states. The performance of the model with inequality constraints illustrates the improvement of county-level estimates in accuracy and precision while preserving required relationships.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"709 - 732"},"PeriodicalIF":1.1,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49176756","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":"In Memory of Dr. Lars Lyberg Remembering a Giant in Survey Research 1944–2021","authors":"","doi":"10.2478/jos-2022-0018","DOIUrl":"https://doi.org/10.2478/jos-2022-0018","url":null,"abstract":"","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"1 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46047412","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}
G. Alleva, G. Arbia, P. D. Falorsi, V. Nardelli, A. Zuliani
Abstract Given the urgent informational needs connected with the diffusion of infection with regard to the COVID-19 pandemic, in this article, we propose a sampling design for building a continuous-time surveillance system. Compared with other observational strategies, the proposed method has three important elements of strength and originality: (1) it aims to provide a snapshot of the phenomenon at a single moment in time, and it is designed to be a continuous survey that is repeated in several waves over time, taking different target variables during different stages of the development of the epidemic into account; (2) the statistical optimality properties of the proposed estimators are formally derived and tested with a Monte Carlo experiment; and (3) it is rapidly operational as this property is required by the emergency connected with the diffusion of the virus. The sampling design is thought to be designed with the diffusion of SAR-CoV-2 in Italy during the spring of 2020 in mind. However, it is very general, and we are confident that it can be easily extended to other geographical areas and to possible future epidemic outbreaks. Formal proofs and a Monte Carlo exercise highlight that the estimators are unbiased and have higher efficiency than the simple random sampling scheme.
{"title":"Spatial Sampling Design to Improve the Efficiency of the Estimation of the Critical Parameters of the SARS-CoV-2 Epidemic","authors":"G. Alleva, G. Arbia, P. D. Falorsi, V. Nardelli, A. Zuliani","doi":"10.2478/jos-2022-0019","DOIUrl":"https://doi.org/10.2478/jos-2022-0019","url":null,"abstract":"Abstract Given the urgent informational needs connected with the diffusion of infection with regard to the COVID-19 pandemic, in this article, we propose a sampling design for building a continuous-time surveillance system. Compared with other observational strategies, the proposed method has three important elements of strength and originality: (1) it aims to provide a snapshot of the phenomenon at a single moment in time, and it is designed to be a continuous survey that is repeated in several waves over time, taking different target variables during different stages of the development of the epidemic into account; (2) the statistical optimality properties of the proposed estimators are formally derived and tested with a Monte Carlo experiment; and (3) it is rapidly operational as this property is required by the emergency connected with the diffusion of the virus. The sampling design is thought to be designed with the diffusion of SAR-CoV-2 in Italy during the spring of 2020 in mind. However, it is very general, and we are confident that it can be easily extended to other geographical areas and to possible future epidemic outbreaks. Formal proofs and a Monte Carlo exercise highlight that the estimators are unbiased and have higher efficiency than the simple random sampling scheme.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"367 - 398"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41712016","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":"Rejoinder: Measuring Inflation under Pandemic Conditions","authors":"W. Diewert, Kevin J. Fox","doi":"10.2478/jos-2022-0029","DOIUrl":"https://doi.org/10.2478/jos-2022-0029","url":null,"abstract":"","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"663 - 668"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44661399","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}
ISCUSI may be the most prominent academic critic of current public health approaches to smoking, often serving as an expert witness for cigarette companies. He is perhaps best known for his conclusion that smokers provide governments with net economic benefits because they pay more in taxes than do nonsmokers and, thanks to their smoking-shortened lives, consume fewer government benefits. Viscusi’s new book, Smoke-Filled Rooms, presents itself as a critical analysis of the states’ settlements of their lawsuits against the cigarette companies. This framework serves Viscusi well, because it supports the narrow, dollars-and-cents approach he favors and excludes important public health considerations. As he writes, “the focus of the litigation is solely on whether the government incurred financial costs as a result of the cigarettes.” Not only are private costs ignored, but so are the suffering and loss caused by smoking and other undesirable effects that are not reflected in government expenditures. “Framing the question in this manner may seem narrow, which it is,” he writes, and he blames the “anti-smoking forces and the governmental lawsuits” for creating such a framework. But Viscusi does not go beyond this kind of sterile and limited economic view. This narrow approach might make sense if Viscusi’s book discussed only the state tobacco settlements, but it clearly does much more. Besides criticizing all other litigation against the tobacco companies (and similar litigation against other businesses), Viscusi evaluates current government and public health initiatives for reducing tobacco use, finds them lacking, and offers a controversial alternative approach. Viscusi’s analysis is often superficial and incomplete, even within the narrow framework he has chosen. In his accounting of smoking-related costs and savings, for example, Viscusi states that “this comprehensive review reflects all cost components that have been recognized in the professional economics literature.” But he later, without explanation, indicates that he “will omit influences such as costs associated with low-birthweight babies” — despite estimates that the costs resulting from smoking-affected pregnancies are as high as $2 billion per year. Other overlooked costs include Social Security survivors’ insurance payments to spouses and children of adults who die early because of smoking, cleaning and maintenance costs related to smoking, and costs related to secondhand smoke. Although Viscusi considers the costs of secondhand smoke in a separate chapter, he does not provide any estimate or substantial discussion of the costs of treating ailments caused or exacerbated by secondhand smoke, nor does he cite the published research that does so. It is also impossible to evaluate the subtotals of costs and savings that Viscusi does present, because he reveals very little about his underlying calculations, data, and assumptions. V Viscusi’s conclusion that smoking has a net positive effec
{"title":"Book Review","authors":"Ann-Marie Flygare, Ingegerd Jansson","doi":"10.2478/jos-2022-0030","DOIUrl":"https://doi.org/10.2478/jos-2022-0030","url":null,"abstract":"ISCUSI may be the most prominent academic critic of current public health approaches to smoking, often serving as an expert witness for cigarette companies. He is perhaps best known for his conclusion that smokers provide governments with net economic benefits because they pay more in taxes than do nonsmokers and, thanks to their smoking-shortened lives, consume fewer government benefits. Viscusi’s new book, Smoke-Filled Rooms, presents itself as a critical analysis of the states’ settlements of their lawsuits against the cigarette companies. This framework serves Viscusi well, because it supports the narrow, dollars-and-cents approach he favors and excludes important public health considerations. As he writes, “the focus of the litigation is solely on whether the government incurred financial costs as a result of the cigarettes.” Not only are private costs ignored, but so are the suffering and loss caused by smoking and other undesirable effects that are not reflected in government expenditures. “Framing the question in this manner may seem narrow, which it is,” he writes, and he blames the “anti-smoking forces and the governmental lawsuits” for creating such a framework. But Viscusi does not go beyond this kind of sterile and limited economic view. This narrow approach might make sense if Viscusi’s book discussed only the state tobacco settlements, but it clearly does much more. Besides criticizing all other litigation against the tobacco companies (and similar litigation against other businesses), Viscusi evaluates current government and public health initiatives for reducing tobacco use, finds them lacking, and offers a controversial alternative approach. Viscusi’s analysis is often superficial and incomplete, even within the narrow framework he has chosen. In his accounting of smoking-related costs and savings, for example, Viscusi states that “this comprehensive review reflects all cost components that have been recognized in the professional economics literature.” But he later, without explanation, indicates that he “will omit influences such as costs associated with low-birthweight babies” — despite estimates that the costs resulting from smoking-affected pregnancies are as high as $2 billion per year. Other overlooked costs include Social Security survivors’ insurance payments to spouses and children of adults who die early because of smoking, cleaning and maintenance costs related to smoking, and costs related to secondhand smoke. Although Viscusi considers the costs of secondhand smoke in a separate chapter, he does not provide any estimate or substantial discussion of the costs of treating ailments caused or exacerbated by secondhand smoke, nor does he cite the published research that does so. It is also impossible to evaluate the subtotals of costs and savings that Viscusi does present, because he reveals very little about his underlying calculations, data, and assumptions. V Viscusi’s conclusion that smoking has a net positive effec","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"669 - 671"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48746281","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 The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany.
{"title":"Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus","authors":"Paul Walter, Marcus Gross, T. Schmid, K. Weimer","doi":"10.2478/jos-2022-0027","DOIUrl":"https://doi.org/10.2478/jos-2022-0027","url":null,"abstract":"Abstract The estimation of poverty and inequality indicators based on survey data is trivial as long as the variable of interest (e.g., income or consumption) is measured on a metric scale. However, estimation is not directly possible, using standard formulas, when the income variable is grouped due to confidentiality constraints or in order to decrease item nonresponse. We propose an iterative kernel density algorithm that generates metric pseudo samples from the grouped variable for the estimation of indicators. The corresponding standard errors are estimated by a non-parametric bootstrap that accounts for the additional uncertainty due to the grouping. The algorithm enables the use of survey weights and household equivalence scales. The proposed method is applied to the German Microcensus for estimating the regional distribution of poverty and inequality in Germany.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"599 - 635"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49405441","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}
Yves Tillé, M. Debusschere, Henri Luomaranta, Martin Axelson, E. Elvers, A. Holmberg, R. Valliant
Abstract In this article, we share some reflections on the state of statistical science and its evolution in the production systems of official statistics. We first try to make a synthesis of the evolution of statistical thinking. We then examine the evolution of practices in official statistics, which had to face very early on a diversification of sou rces: first with the use of censuses, then sample surveys and finally administrative files. At each stage, a profound revision of methods was necessary. We show that since the middle of the 20th century, one of the major challenges of statistics has been to produce estimates from a variety of sources. To do this, a large number of methods have been proposed which are based on very different f oundations. The term “big data” encompasses a set of sources and new statistical methods. We first examine the potential of valorization of big data in official statistics. Some applications such as image analysis for agricultural prediction are very old and will be further developed. However, we report our skepticism towards web-scrapping methods. Then we examine the use of new deep learning methods. With access to more and more sources, the great challenge will remain the valorization and harmonization of these sources.
{"title":"Some Thoughts on Official Statistics and its Future (with discussion)","authors":"Yves Tillé, M. Debusschere, Henri Luomaranta, Martin Axelson, E. Elvers, A. Holmberg, R. Valliant","doi":"10.2478/jos-2022-0026","DOIUrl":"https://doi.org/10.2478/jos-2022-0026","url":null,"abstract":"Abstract In this article, we share some reflections on the state of statistical science and its evolution in the production systems of official statistics. We first try to make a synthesis of the evolution of statistical thinking. We then examine the evolution of practices in official statistics, which had to face very early on a diversification of sou rces: first with the use of censuses, then sample surveys and finally administrative files. At each stage, a profound revision of methods was necessary. We show that since the middle of the 20th century, one of the major challenges of statistics has been to produce estimates from a variety of sources. To do this, a large number of methods have been proposed which are based on very different f oundations. The term “big data” encompasses a set of sources and new statistical methods. We first examine the potential of valorization of big data in official statistics. Some applications such as image analysis for agricultural prediction are very old and will be further developed. However, we report our skepticism towards web-scrapping methods. Then we examine the use of new deep learning methods. With access to more and more sources, the great challenge will remain the valorization and harmonization of these sources.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"557 - 598"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41856379","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 Data collection staff involved in sampling designs, monitoring and analysis of surveys often have a good sense of the response rate that can be expected in a survey, even when this survey is new or done at a relatively low frequency. They make expectations of response rates, and, subsequently, costs on an almost continuous basis. Rarely, however, are these expectations formally structured. Furthermore, the expectations usually are point estimates without any assessment of precision or uncertainty. In recent years, the interest in adaptive survey designs has increased. These designs lean heavily on accurate estimates of response rates and costs. In order to account for inaccurate estimates, a Bayesian analysis of survey design parameters is very sensible. The combination of strong intrinsic knowledge of data collection staff and a Bayesian analysis is a natural next step. In this article, prior elicitation is developed for design parameters with the help of data collection staff. The elicitation is applied to two case studies in which surveys underwent a major redesign and direct historic survey data was unavailable.
{"title":"Data Collection Expert Prior Elicitation in Survey Design: Two Case Studies","authors":"Shiya Wu, B. Schouten, R. Meijers, M. Moerbeek","doi":"10.2478/jos-2022-0028","DOIUrl":"https://doi.org/10.2478/jos-2022-0028","url":null,"abstract":"Abstract Data collection staff involved in sampling designs, monitoring and analysis of surveys often have a good sense of the response rate that can be expected in a survey, even when this survey is new or done at a relatively low frequency. They make expectations of response rates, and, subsequently, costs on an almost continuous basis. Rarely, however, are these expectations formally structured. Furthermore, the expectations usually are point estimates without any assessment of precision or uncertainty. In recent years, the interest in adaptive survey designs has increased. These designs lean heavily on accurate estimates of response rates and costs. In order to account for inaccurate estimates, a Bayesian analysis of survey design parameters is very sensible. The combination of strong intrinsic knowledge of data collection staff and a Bayesian analysis is a natural next step. In this article, prior elicitation is developed for design parameters with the help of data collection staff. The elicitation is applied to two case studies in which surveys underwent a major redesign and direct historic survey data was unavailable.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"637 - 662"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42770038","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 The provision of translated field instruments is a crucial aspect to reduce response burden and subsequently increase data quality in surveys with a multi-linguistic target population such as surveys on recent immigrants. Failure to address this can result in a mismatch between the survey language and the respondent’s mother tongue. By using a survey on refugees in Germany, this article explores the correlation of the absence of the respondents’ mother tongue on item nonresponse – a crucial aspect of data quality. Further, this article investigates whether supplementary audio recordings in the same language as the written questions can reduce item nonresponse when the mother tongue is not available. To answer the research questions, all missing answers per individual are counted and analyzed by means of poisson regression analyses. In a second step, the likelihood of item-nonresponse for single items is estimated as well. Results show that a language mismatch as well as the usage of audio recordings increase item nonresponse.
{"title":"If They Don’t Understand the Question, They Don’t answer. Language Mismatch in Face-to-Face Interviews","authors":"Jannes Jacobsen","doi":"10.2478/jos-2022-0022","DOIUrl":"https://doi.org/10.2478/jos-2022-0022","url":null,"abstract":"Abstract The provision of translated field instruments is a crucial aspect to reduce response burden and subsequently increase data quality in surveys with a multi-linguistic target population such as surveys on recent immigrants. Failure to address this can result in a mismatch between the survey language and the respondent’s mother tongue. By using a survey on refugees in Germany, this article explores the correlation of the absence of the respondents’ mother tongue on item nonresponse – a crucial aspect of data quality. Further, this article investigates whether supplementary audio recordings in the same language as the written questions can reduce item nonresponse when the mother tongue is not available. To answer the research questions, all missing answers per individual are counted and analyzed by means of poisson regression analyses. In a second step, the likelihood of item-nonresponse for single items is estimated as well. Results show that a language mismatch as well as the usage of audio recordings increase item nonresponse.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"453 - 484"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48733121","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 There is an ongoing debate on whether residual seasonality is present in the estimates of real Gross Domestic Product (GDP) in U.S. national accounts and whether it explains the slower quarter-one GDP growth rate in the recent years. This article aims to bring clarity to this topic by (1) summarizing the techniques and methodologies used in these studies; (2) arguing for a sound methodological framework for evaluating claims of residual seasonality; and (3) proposing three diagnostic tests for detecting residual seasonality, applying them to different vintages and different sample spans of data on real GDP and its major components from the U.S. national accounts and making comparisons with results from the previous studies.
{"title":"Assessing Residual Seasonality in the U.S. National Income and Product Accounts Aggregates","authors":"Baoline Chen, T. McElroy, Osbert Pang","doi":"10.2478/jos-2022-0020","DOIUrl":"https://doi.org/10.2478/jos-2022-0020","url":null,"abstract":"Abstract There is an ongoing debate on whether residual seasonality is present in the estimates of real Gross Domestic Product (GDP) in U.S. national accounts and whether it explains the slower quarter-one GDP growth rate in the recent years. This article aims to bring clarity to this topic by (1) summarizing the techniques and methodologies used in these studies; (2) arguing for a sound methodological framework for evaluating claims of residual seasonality; and (3) proposing three diagnostic tests for detecting residual seasonality, applying them to different vintages and different sample spans of data on real GDP and its major components from the U.S. national accounts and making comparisons with results from the previous studies.","PeriodicalId":51092,"journal":{"name":"Journal of Official Statistics","volume":"38 1","pages":"399 - 428"},"PeriodicalIF":1.1,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43805858","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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