Single-month unemployment rate estimates for the Brazilian Labour Force Survey using state-space models

C. Goncalves; L. Hidalgo; D. Silva; J. van den Brakel

doi:10.1111/rssa.12914

Single-month unemployment rate estimates for the Brazilian Labour Force Survey using state-space models

C. Goncalves^*, L. Hidalgo, D. Silva, J. van den Brakel

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

Original language	English
Pages (from-to)	1707-1732
Number of pages	26
Journal	Journal of the Royal Statistical Society Series A-Statistics in Society
Volume	185
Issue number	4
DOIs	https://doi.org/10.1111/rssa.12914
Publication status	Published - Oct 2022

Keywords

official statistics
sampling error
state-space model
time series
unemployment rate
SMALL-AREA ESTIMATION
TIME-SERIES
DISCONTINUITIES
LEVEL

Access to Document

10.1111/rssa.12914

Cite this

@article{0936506ab2434fc6b7811e0fcfe7cac0,

title = "Single-month unemployment rate estimates for the Brazilian Labour Force Survey using state-space models",

abstract = "The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.",

keywords = "official statistics, sampling error, state-space model, time series, unemployment rate, SMALL-AREA ESTIMATION, TIME-SERIES, DISCONTINUITIES, LEVEL",

author = "C. Goncalves and L. Hidalgo and D. Silva and {van den Brakel}, J.",

note = "data source: Brazilian Labour Force Survey",

year = "2022",

month = oct,

doi = "10.1111/rssa.12914",

language = "English",

volume = "185",

pages = "1707--1732",

journal = "Journal of the Royal Statistical Society Series A-Statistics in Society",

issn = "0964-1998",

publisher = "Wiley-Blackwell",

number = "4",

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T1 - Single-month unemployment rate estimates for the Brazilian Labour Force Survey using state-space models

AU - Goncalves, C.

AU - Hidalgo, L.

AU - Silva, D.

AU - van den Brakel, J.

N1 - data source: Brazilian Labour Force Survey

PY - 2022/10

Y1 - 2022/10

N2 - The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

AB - The Brazilian Labour Force Survey publishes monthly national indicators based on 3-month rolling data. This paper presents state-space models to produce state-level single-month unemployment rate estimates. The models account for sampling errors and the increased dynamics in the labour force series due to the unforeseen SARS-COV-2 pandemic. Bivariate time series models with claimant count auxiliary data and multivariate models combining survey data of several states are investigated. The results demonstrated the benefits of the univariate state-space approach to produce unemployment official statistics for Brazil. Additionally, the regional multivariate model shows promising results but requires further investigation.

KW - official statistics

KW - sampling error

KW - state-space model

KW - time series

KW - unemployment rate

KW - SMALL-AREA ESTIMATION

KW - TIME-SERIES

KW - DISCONTINUITIES

KW - LEVEL

U2 - 10.1111/rssa.12914

DO - 10.1111/rssa.12914

M3 - Article

SN - 0964-1998

VL - 185

SP - 1707

EP - 1732

JO - Journal of the Royal Statistical Society Series A-Statistics in Society

JF - Journal of the Royal Statistical Society Series A-Statistics in Society

IS - 4

ER -