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Output Quality of Multisource Statistics Output Quality of Multisource Statistics

Output Quality of Multisource Statistics - PowerPoint Presentation

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Output Quality of Multisource Statistics - PPT Presentation

Ton de Waal 15 March 2017 Overview Komuso ESSnet on quality of multisource statistics Basic data configurations BDCs and some work done For all BDCs literature reviews have been carried out ID: 599678

quality data statistics bdc data quality bdc statistics sources measures variables aggregated work input units komuso essnet results framework

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Slide1

Output Quality of Multisource Statistics

Ton de Waal

15 March, 2017Slide2

Overview

Komuso

:

ESSnet

on

quality of multisource statistics

Basic data configurations (BDCs) and some work done

For

all BDCs literature reviews have

been carried out

For most BDCs suitability tests have been done

ConclusionsSlide3

Komuso: ESSnet

on

quality of multisource statistics

Komuso

is

part of

ESS.VIP

Admin

Project

During

first

Specific Grant Agreement

(January

2016 until April

2017)

four Work Packages (WPs) have been defined:

WP 1: Evaluating

the quality of input

data

WP 2: Methodology

for the assessment of the quality of frames for social

statistics

WP 3: Framework

for the quality evaluation of statistical output based on multiple

sources

WP 4: Communication

with respect to the

ESSnetSlide4

Komuso: ESSnet

on

quality of multisource statistics

Komuso

is

part of

ESS.VIP

Admin

Project

During

first

Specific Grant Agreement

(January

2016 until April

2017)

four Work Packages (WPs) have been defined:

WP 1: Evaluating

the quality of input

data

WP 2: Methodology

for the assessment of the quality of frames for social

statistics

WP 3: Framework

for the quality evaluation of statistical output based on multiple

sources

WP 4: Communication

with respect to the

ESSnetSlide5

BDC 1: The baseline

Several data sources with non-overlapping units that together cover complete population

Estimates from data sources can simply be added

Even in this “simple” case important problems occur, such as

Progressiveness of data

Unit problems, e.g. classification into domainsSlide6

Some results for BDC 1

Statistics

Netherlands

has examined the effect of errors in the NACE code classification

on

growth rates of enterprise statistics broken down by NACE

codeSlide7

BDC 2: Partly overlapping units/variables

Part of variables and units in data sources overlap

Observed value in one data source may differ from observed value in other data source

Options:

Micro-integration

Latent class models

Structural equation modelsSlide8

Some results for BDC 2

ISTAT

has examined multiple administrative and survey sources that provide the value of the same variable of interest

A

Latent Class model can be used to estimate the true

values

E

stimates

of the probabilities

, where

is the observed value in data source

and

is the true (latent) value, can be used to evaluate the accuracy of data source

 Slide9

Some results for BDC 2

Statistics

Austria

has analysed a quality framework

that

can be used when several data sources with possibly conflicting values for common variables are available.

The

quality framework models errors in variables in these data sources as well as systematically uses expert knowledge. Slide10

BDC 3: Partly overlapping units/variables with under-coverage

Part of variables and units in data sources may overlap

Under-coverage occurs

Options:

Capture-recapture

methodsSlide11

BDC 4: Microdata and aggregated data

Microdata are combined with aggregated data

Inconsistencies between microdata and aggregated data should be avoided

Especially complicated if aggregated data are estimates

Example: Dutch virtual Population Census

Options:

Repeated weighting

Calibrated imputation

Macro-integrationSlide12

Some results for BDC 4

Statistics Netherlands

and

Statistics Norway

have

been working on quality measures that can be applied to

BDC 4

Many

macro-economic figures are connected by constraints (“accounting equations”)

Input estimates usually do not automatically satisfy accounting equations due to measurement and sampling errorsEstimation involves a

reconciliation step by which the input estimates are

modified

A

n

accounting equation is considered as a single entity and scalar quality measures

have been defined

These

measures capture the adjustment effect as well as the relative contribution of the various input estimates to the final estimated

accountSlide13

BDC 5: Only aggregated data

Sometimes only aggregated data are combined with each other

Example: National Accounts

Options:

Macro-integrationSlide14

Some results for BDC 5

Same method that has been developed by Statistics Netherlands and Statistics Norway for BDC 4 can be applied to BDC 5Slide15

BDC 6: Longitudinal data

Combining longitudinal data with different frequencies

Example: combining turnover data from monthly survey with (more accurate) quarterly data from Tax Office

Problem: calibrate monthly data on quarterly data while preserve month-to-month growth

Option:

Benchmarking techniquesSlide16

Conclusions

Much

work has been

done

More work is needed

simplifying

some of the quality measures, methods to compute them, and the use of these measures/methods in

practice

extending

the range of situations in which the quality measures and methods to compute them can be appliedexamining

quality measures relating to “coherence” in more detail