Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends

Krieg, Sabine; Brakel, Jan A. van den

doi:10.1016/j.csda.2012.02.008

Cited by 9 publications

(19 citation statements)

References 17 publications

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“…This implies that trends of the domains develop in the same direction and that there is no substitution or replacement in the Dutch labour market between the domains. See Krieg and Van den Brakel (2012) for a more detailed interpretation based on an analysis of the underlying common trends.…”

Section: Results For Common Factor Models For Six Domainsmentioning

confidence: 99%

“…Earlier references, e.g. Pfeffermann and Burck (1990), Pfeffermann and Bleuer (1993) and Krieg and Van den Brakel (2012), modelled the correlation between trends of cross-sectional domain series in a multivariate structural time series model. This paper combines the wave series of the rotating panel of all domains in a multivariate model to take advantage of the correlation between the trend component, seasonal component and RGB of the domains.…”

Section: Introductionmentioning

confidence: 99%

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Small Area Estimation with State Space Common Factor Models for Rotating Panels

Brakel¹,

Krieg

2016

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Macroeconomic indicators about the labour force, published by national statistical institutes, are predominantly based on rotating panels. Sample sizes of most labour force surveys in combination with the design‐based or model‐assisted mode of inference obstruct the publication of such indicators on a monthly frequency. Previous research proposed a multivariate structural time series model to obtain more precise model‐based estimates by taking advantage of sample information observed in previous periods. In the paper this model is extended to use sample information from other domains or strongly correlated auxiliary series. A relatively parsimonious version of these models is currently used by Statistics Netherlands to produce official monthly figures about the labour force.

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Section: Results For Common Factor Models For Six Domainsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Small Area Estimation with State Space Common Factor Models for Rotating Panels

Brakel¹,

Krieg

2016

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…In the case of cross‐sectional surveys, these components are confounded and can be estimated as one term,

v_{k, t} = I_{t} + e_{k, t}

. To account for heteroscedasticity due to changing sample sizes or design over time, the variances of the direct estimates can be used as a priori information in the variance of the measurement equation of the state space model, for example, by making the variance of the measurement disturbance term,

v_{k, t}

, proportional to the variances of the direct estimates; see Binder & Dick () & Krieg & van den Brakel ().…”

Section: Estimating Discontinuities Using State‐space Intervention Momentioning

confidence: 99%

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Brakel

Zhang

Tam

2020

Int Statistical Rev

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Summary A key requirement of repeated surveys conducted by national statistical institutes is the comparability of estimates over time, resulting in uninterrupted time series describing the evolution of finite population parameters. This is often an argument to keep survey processes unchanged as long as possible. It is nevertheless inevitable that a survey process will need to be redesigned from time to time, for example, to improve or update methods or implement more cost‐effective data collection procedures. It is important to quantify the systematic effects or discontinuities of a new survey process on the estimates of a repeated survey to avoid a disturbance in the comparability of estimates over time. This paper reviews different statistical methods that can be used to measure discontinuities and manage the risk due to a survey process redesign.

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“…This way, the model accounts for heteroscedasticity caused by varying sample sizes. This approach was proposed by Krieg and Van den Brakel (2012) and comes down to the measurement error model that was proposed by Binder and Dick (1990). For the variance of the above-mentioned product to be close to the design-based variance estimate var.Y m,t /, σ 2 e m should be close to 1.…”

Section: Structural Time Series Model Specificationmentioning

confidence: 99%

“…Time series extensions of the Fay-Herriot model can be found in Rao and Yu (1994), Datta et al (1999) and You (2008). For small area applications modelled with structural time series (STS) techniques, see Pfeffermann (1991), Pfeffermann and Bleuer (1993), Pfeffermann and Tiller (2006), Pfeffermann et al (2014), Krieg and Van den Brakel (2012), Van den Brakel and Krieg (2016) and Bollineni-Balabay et al (2016). Furthermore, time series models can help to compare figures before and after survey redesigns by estimating level breaks.…”

Section: Introductionmentioning

confidence: 99%

Multilevel Hierarchical BayesianVersusState Space Approach in time Series Small Area Estimation: the Dutch Travel Survey

Bollineni‐Balabay

Brakel

Palm

et al. 2017

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary This study compares state space models (estimated with the Kalman filter with a frequentist approach to hyperparameter estimation) with multilevel time series models (based on the hierarchical Bayesian framework). The application chosen is the Dutch Travel Survey featuring small sample sizes and discontinuities caused by the survey redesigns. Both modelling approaches deliver similar point and variance estimates. Slight differences in model‐based variance estimates appear mostly in small‐scaled domains and are due to neglecting uncertainty around the hyperparameter estimates in the state space models, and to a lesser extent to skewness in the posterior distributions of the parameters of interest. The results suggest that the reduction in design‐based standard errors with the hierarchical Bayesian approach is over 50% at the provincial level, and over 30% at the national level.

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Estimation of the monthly unemployment rate for six domains through structural time series modelling with cointegrated trends

Cited by 9 publications

References 17 publications

Small Area Estimation with State Space Common Factor Models for Rotating Panels

Small Area Estimation with State Space Common Factor Models for Rotating Panels

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Multilevel Hierarchical BayesianVersusState Space Approach in time Series Small Area Estimation: the Dutch Travel Survey

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