Maria Garcia US Census Bureau UNECE/SDE, Oslo, Norway, 24-26 September 2012 An Application of Selective Editing to the US Census Bureau Trade Data.

Slides:



Advertisements
Similar presentations
Katherine Jenny Thompson
Advertisements

Data Imputation United Nations Statistics Division (UNSD) 16 March 2011 Santiago, Chile.
OptiShip ® Multi-carrier Shipping System. OptiShip ® customers save on average 13.6% of parcel shipping costs… OptiShip ® is a comprehensive system that.
Editing and Imputing VAT Data for the Purpose of Producing Mixed- Source Turnover Estimates Hannah Finselbach and Daniel Lewis Office for National Statistics,
United Nations Workshop on the 2010 World Programme on Population and Housing Censuses: Census Evaluation and Post Enumeration Surveys, Amman, Jordan,
Quality assurance -Population and Housing Census Alma Kondi, INSTAT, Albania.
1 Editing Administrative Data and Combined Data Sources Introduction.
Editing of mixed source data for turnover statistics Jeffrey Hoogland (SN) Work Session on Statistical Data Editing (Ljubljana, Slovenia, 9-11 May 2011)
Data Editing United Nations Statistics Division (UNSD) 16 March 2011 Santiago, Chile.
SW318 Social Work Statistics Slide 1 Estimation Practice Problem – 1 This question asks about the best estimate of the mean for the population. Recall.
Eurostat Statistical Data Editing and Imputation.
France : Improving checks in customs data OCDE – 7 November 2011.
Joint UNECE/Eurostat Meeting on Population and Housing Censuses (28-30 October 2009) Accuracy evaluation of Nuts level 2 hypercubes with the adoption of.
12th Meeting of the Group of Experts on Business Registers
The Adoption of METIS GSBPM in Statistics Denmark.
Update on Selective Editing and Implications for Staff Skills International Trade Conference September 2008 Ken Smart.
Topic (ii): New and Emerging Methods Maria Garcia (USA) Jeroen Pannekoek (Netherlands) UNECE Work Session on Statistical Data Editing Paris, France,
Current and Future Applications of the Generic Statistical Business Process Model at Statistics Canada Laurie Reedman and Claude Julien May 5, 2010.
Jeroen Pannekoek - Statistics Netherlands Work Session on Statistical Data Editing Oslo, Norway, 24 September 2012 Topic (I) Selective and macro editing.
1 An Evaluation of Changes to the Universe Extraction for Current Business Surveys at the U.S. Census Bureau Author: Carol S. King Presenter: Ruth E. Detlefsen.
Some ACS Data Issues and Statistical Significance (MOEs) Table Release Rules Statistical Filtering & Collapsing Disclosure Review Board Statistical Significance.
CBS-SSB STATISTICS NETHERLANDS – STATISTICS NORWAY Work Session on Statistical Data Editing Oslo, Norway, September 2012 Jeroen Pannekoek and Li-Chun.
Statistical Expertise for Sound Decision Making Quality Assurance for Census Data Processing Jean-Michel Durr 28/1/20111Fourth meeting of the TCG - Lubjana.
Implicit Linear Inequality Edits Generation and Error Localization in the SPEER Edit System Maria Garcia U.S. Census Bureau UNECE Work Session on Statistical.
1 C. ARRIBAS, D. LORCA, A. SALINERO & A. COLMENERO Measuring statistical quality at the Spanish National Statistical Institute.
Workshop on Price Index Compilation Issues February 23-27, 2015 Data Collection Issues Gefinor Rotana Hotel, Beirut, Lebanon.
A Quality Driven Approach to Managing Collection and Analysis
Topic (iii): Macro Editing Methods Paula Mason and Maria Garcia (USA) UNECE Work Session on Statistical Data Editing Ljubljana, Slovenia, 9-11 May 2011.
Outlining a Process Model for Editing With Quality Indicators Pauli Ollila (part 1) Outi Ahti-Miettinen (part 2) Statistics Finland.
Copyright 2010, The World Bank Group. All Rights Reserved. Managing Data Processing Section B.
1 Towards a common statistical enterprise architecture Ongoing process reengineering at Statistics Sweden Service Oriented Architecture – SOA Sharing of.
Selective Editing Strategies for the U.S. Census Bureau Trade Statistics Programs María García, Alison Gajcowski, and Andrew Jennings U.S. Census Bureau.
Heather Wagstaff and Thomas Burg Topic (vi) Methodologies for Editing Census Data INTRODUCTION UNECE Work Session on Statistical Data Editing:Vienna
A selective editing method considering both suspicion and potential impact, developed and applied to the Swedish foreign trade statistics Topic (ii), WP.
ITS Meeting Sept 2006 New Developments at the OECD: The Common Processing Application (COPRA) ITS Experts Meeting – September 2006 Trevor Fletcher ITN/CBS.
1 Probability and Statistics Confidence Intervals.
Study of Editing and Imputation Practices at Statistics Finland Janika Konnu and Pauli Ollila Statistics Finland Q2010: Editing session Wednesday 5 th.
5.8 Finalise data files 5.6 Calculate weights Price index for legal services Quality Management / Metadata Management Specify Needs Design Build CollectProcessAnalyse.
DETECTION OF OUTLIERS IN THE CANADIAN CONSUMER PRICE INDEX (CPI) DETECTION OF OUTLIERS IN THE CANADIAN CONSUMER PRICE INDEX (CPI) ABDELNASSER SAÏDI AND.
1 IT system and data validation process in Latvian CPI/HICP Prepared by Oskars Alksnis, Central Statistical Bureau of Latvia EU Twinning Project Forwarding.
1 Handbook on Population and Housing Census Editing Department of Economic and Social Development United Nations Statistics Division Studies in Methods,
USE OF ADMINISTRATIVE DATA
Methods for Data-Integration
New York State Attorney General’s Office Antitrust Bureau
Software Trade Data Tutorial
Standardized and modernized data editing in Statistics Denmark
Census of Population & Housing 2001 Sri Lanka
Anna Długosz Central Statistical Office of Poland
اختبار الفرضيات اختبارالفرضيات المتعلقة بالوسط
Confidentiality in data dissemination
Estimation methods for the integration of administrative sources
Survey phases, survey errors and quality control system
Working Group on Population and Housing Censuses
Recent developments in Israel Foreign Trade Statistics Methodology
Survey phases, survey errors and quality control system
Tomaž Špeh, Rudi Seljak Statistical Office of the Republic of Slovenia
WinTIM, Indices methodology and tool Wiking Althoff, CESD Communautaire External trade experts meeting on the CARDS Programme, Luxembourg, May.
Validation in International Trade in Goods Statistics Lídia Bassó
The Norwegian CPI Data Validation and Editing
Foreign Trade Statistics in the Republic of Kazakhstan
Validation process and the IT tools used at KAS
Validation of WStatR-Data
Evaluation of Content Error Pres. 10
Chapter 5: Classification of Industries and Products and Size of SUTs
Data processing German foreign trade statistics
Exploring Clustering Applications in Outlier Detection for Administrative Data Elizabeth Ayres Sunday, July 29, 2018.
Treatment of Missing Data Pres. 8
Automatic Editing with Soft Edits
Adjusting Census Figures Pres. 11
The Challenge in Creating a Stock of Emigrants From Israel
Presentation transcript:

Maria Garcia US Census Bureau UNECE/SDE, Oslo, Norway, September 2012 An Application of Selective Editing to the US Census Bureau Trade Data

Foreign Trade Statistics Programs Official source of US international merchandise trade statistics Electronic data collection Complete enumeration Pre – editing: check for fatal errors Micro editing –Range and ratio edits –Automatic imputation –“Rejects” – imputation not successful

Foreign Trade Data Processing Rejects –Distribute among analysts for manual correction. –Analysts review large number of records under tight time constraints Goal: Use selective editing to identify highly suspicious errors having a high potential effect on the estimates –Value (V) –Quantity (Q) –Shipping weight (SW)

Hidiroglou-Berthelot Method (HB)

HB Method for Our Trade Data

HB for Our Trade Data (Cont’d)

Effect on Publication Totals

Simulation and Evaluation

Evaluation Results Examining results at lowest level of aggregation: –Data users may closely scrutinize the statistics for particular types of products –Ex: import/export of rough diamonds –Kimberley Process - joint governments, industry and civil society initiative to stem the flow of conflict diamonds

Evaluation Results

Customer’s Feedback Subject matter experts questioned: –High ranking given to records that by experience they consider insignificant to final cell estimates –Low ranking given to records that would have been flagged for manual correction

Total Value (V) Total Quantity (Q) Unit Price (V/Q) Ratio Bounds Reported cell total $102,1907,217$ Reported suspicious Record $3,024 7,144$ Final suspicious record $3,02410$ Final cell total $102,19083$1, Commodity XXXXXXXXXX Customer’s Feedback

Total Value (V) Total Quantity (Q) Unit Price (V/Q) Ratio Bounds Lower Bound Upper Bound 128 records, 87 records imputed, three rejects $3,142,622129,973,502$ Final cell total All three rejects corrected $3,142,6221,230,629$ Selective editing cell total Two highest ranked records corrected $3,142,6221,804,699$ Commodity YYYYYYYYYY Customer’s Feedback

Concluding Remarks

Thank you!