Upload
nancy-grant
View
213
Download
0
Embed Size (px)
Citation preview
1
Enhancing Small Area Estimation Methods Applications to Istat’s Survey Data
Ranalli M.G. ~ Università di Perugia
D’Alo’ M., Di Consiglio L., Falorsi S., Solari F. ~ Istat
Pratesi M., Salvati N. ~ Università di Pisa
Q2008 ~ Rome, July 11th
2
OUTLINE OUTLINE
Italian Labour Force Survey
Standard small area estimators for LFS
Small area estimators that incorporate spatial information
Model based direct estimator (MBDE)
Semi-parametric models (based on p-splines)
Experimental study
Analysis of results
Final remarks
3
Labour Force Survey description
Labour Force Survey (LFS) is a quarterly two stage survey with partial overlap of sampling units according to a rotation scheme of type (2-2-2).
In each province the municipalities are classified as Self-Representing Areas (SRAs) and the Non Self-Representing Areas (NSRAs).
From each SRAs a sample of households is selected.
In NSRAs the sample is based on a stratified two stage sampling design. The municipalities are the primary sampling units (PSUs), while the households are the Secondary Sampling Units (SSUs).
For each quarterly sample about 1350 municipalities and 200,000 individuals are involved.
4
■Since 2000, ISTAT disseminates yearly LFS estimates of employed and unemployed counts related to the 784 Local Labour Market Areas (LLMAs).
■LLMAs are unplanned domains obtained as clusters of municipalities cutting across provinces which are the LFS finest planned domains.
■The direct estimates are unstable due to very small LLMA sample sizes (more than 100 LLMAs have zero sample size). SAE methods are necessary.
■Until 2003, a design based composite type estimator was adopted.
■Starting from 2004, after the redesign of LFS sampling strategy, a unit-level EBLUP estimator with spatially autocorrelated random area effects has been introduced.
Small area estimation on LFS
5
Standard small area estimators – design based
The GREG estimator is based on the standard linear model:
id
T
ididy βx 2)( var 0, )E( idid
wTD
dddd YY βXX ˆˆˆˆ DGREG
Direct and GREG estimator
and can be expressed as an adjustment of the direct estimator
for differences between the sample and population area means of covariates
The direct estimator is given by
dsi
iid NywYd
Dˆ
6
Unit level Synthetic and EBLUP
Standard small area estimators – model based
The Synthetic estimator assumes a standard linear mixed model with unit-specific auxiliary variables, random area-specific effects and errors independently normally distributed
iddTidid euy βx
),0( ~ ),,0( ~ 22
eidud NiideNiidu
βX ˆˆ SI TddY
and is given by
The EBLUP estimator assumes the same model but is given by
dTdd uY ˆˆˆ EB βX d
Uii Ny
d
ˆ
7
Enhanced small area estimators
1. Unit level EBLUP with spatial correlation of area effects
The matrix A depends on the distances among the areas and on an unknown parameter connected to the spatial correlation coefficient among the areas.
),0(~ ),,0(~ 22
Neu MNMN IeAu
1'
'',
exp1dddist
a ddddA
otherwise1
if0 '
'dd
dd
The EBLUP-S estimator is based on the following unit level linear mixed model:
iddTidid euy βx
8
Enhanced small area estimators
2. Model Based Direct Estimator (Chambers & Chandra, 2006)
dd si
mi
sii
mid wywY MBDˆ
si
imi ywY where the weights are such that is the (E)BLUP of
The MBD estimator is based on a unit level linear mixed model and is given by
Ui
iyY under the model (Royall, 1976).
Calibrated with respect to the total of x.
Reduces bias vs EBLUP
Does not allow estimation for non-sampled areas
Less efficient than EBLUP
9
In the literature there are many nonparametric regression methods (kernel, local polynomial, wavelets…) BUT difficult to incorporate in a Small area model
Methods based on penalized splines (Eilers e Marx, 1996; Ruppert et al., 2003) can be estimated by means of mixed models -> promising candidate for SAE methods
Enhanced small area estimators
3. Nonparametric EBLUP (Opsomer et al., 2008)
iddidididTidid euzzfzfy ),()( 321βx
),0( ~ ),,0( ~ 22
eidud NiideNiidu
Great Flexibility in definition of model Estimable with existing software using REML
Hard to estimate efficiency and test for terms significance (via bootstrap?)
10
LFS empirical study
The simulation study on LFS has been carried out to estimate the unemployment rate at LLMA level
500 two-stage LFS sample have been drawn from 2001 census data set.
The performances of the methods have been evaluated for the estimation of the unemployment rate in the 127 LLMAs belonging to the geographical area “Center of Italy ”.
GREG, Synthetic, EBLUP small area estimators have been applied considering two different sets of auxiliary variables
Case A - LFS real covariates = sex by 14 age classes + employment indicator at previous census;
Case B – LFS real covariates + geographic coordinates (latitude and longitude of the municipality the sampling unit belongs to).
11
■ Spatial EBLUP: A spatial correlation in the variance matrix of the random effects has been considered (EBLUP SP) + Case A covariates
■ MBD: Model based direct estimation is performed on sampled LLMAs, while synthetic estimators based on unit level linear mixed model is considered for non sampled LLMAs (Case A covariates)
■ Nonparametric EBLUP: two semiparametric representations based on penalized splines have been applied (fitted as additional random effects):
geographical coordinates of the municipality (EBLUP-SPLINE SP): this allows for a finer representation of the spatial component vs EBLUP SP (at municipality level instead of LLMA).
age (EBLUP-SPLINE AGE & EBLUP SP-SPLINE AGE)
Enhanced Small area estimators
12
D
1ddRB
D
1AARB
D
1ddRRMSE
D
1ARRMSE
d RBmaxd
MARB
Average Absolute RB:
Average RRMSE:
Maximum Absolute RB:
Maximum RRMSE: dRRMSEmaxd
MRRMSE
Evaluation Criteria
% Relative Bias:
% Relative Root Mean Squared Error:
100 ˆ1
RB1
d
R
r d
drd
Y
YY
R
100 ˆ1
RRMSE1
2
d
R
r d
drd
YYY
R
13
ESTIMATOR AARB
ARRMSE MARB
MRRMSE
DIRECT 2.9 51.7 20.4 90.7
GREG A 7.2 40.2 83.3 93.8
GREG B 6.9 40.0 71.5 82.8
SYNTH A 14.0 15.8 93.0 93.5
SYNTH B 12.4 16.4 79.7 81.0
EBLUP A 13.2 16.2 92.5 93.1
EBLUP B 11.9 16.7 79.5 80.7
EBLUP SP 12.7 16.3 90.9 91.6
MBD 8.8 35.3 86.3 92.6
EBLUP-SPLINE SP 12.1 16.5 91.1 92.2
EBLUP-SPLINE AGE 13.2 16.5 89.8 90.5
EBLUP SP-SPLINE AGE 12.2 17.3 90.3 90.9
Results – A: LFS covariates; B = A + geog. coord. mun.
14
Analysis of results
Area level estimators (not shown here) perform a little better in terms of Bias but much worse in terms of MSE.
The results of GREG, SYNTH and EBLUB in case B, when geographical information is considered in the fixed term, display better performances in terms of bias.
In terms of MSE standard estimators in case A outperform standard estimators in case B if the ARRMSE is considered as overall evaluation criteria, while better results are obtained in case B if MRRMSE is considered
15
EBLUP SP can be compared with the unit level EBLUP with geographical information included as covariates and the EBLUP-SPLINE SP.
o EBLUP SP show better performances in terms of MSE, while the unit level EBLUP outperform the other estimators in terms of bias.
o The EBLUP-SPLINE SP displays performances in between the other estimators.
Analysis of results
EBLUP-SPLINE AGE performs similarly to the unit level EBLUP in Case A
o The use of the age in a nonparametric way is an alternative use of auxiliary information. With respect to case A the model is more parsimonious.
As it was expected MBDE shows better results in term of bias and performs poorly in term of MSE than other SAE methods
The use of autocorrelation structure together with the spline on the variable age doesn’t improve the performances
16
Final remarks
Sensitivity to smoothing parameters’ choice in the splines approach has to be investigated.
The introduction of the sampling weighs should be considered to try to achieve benchmarking with direct estimates produced at regional level
The response in a 0-1 variable: a logistic mixed model is currently being investigated
The model group is a small portion of Italy (center); hence the area specific effects are smaller than they could be if an overall model was considered for all the country: the introduction of geographical information should be analyzed considering a larger model level group