Estimating the underlying change in unemployment in the UK

By setting up a suitable time series model in state space form, the latest estimate of the underlying current change in a series may be computed by the Kalman filter. This may be done even if the observations are only available in a time-aggregated form subject to survey sampling error. A related series, possibly observed more frequently, may be used to improve the estimate of change further. The paper applies these techniques to the important problem of estimating the underlying monthly change in unemployment in the UK measured according to the definition of the International Labour Organisation by the Labour Force Survey. The fitted models suggest a reduction in root-mean-squared error of around 10% over a simple estimate based on differences if a univariate model is used and a further reduction of 50% if information on claimant counts is taken into account. With seasonally unadjusted data, the bivariate model offers a gain of roughly 40% over the use of annual differences. For both adjusted and unadjusted data, there is a further gain of around 10% if the next month's figure on claimant counts is used. The method preferred is based on a bivariate model with unadjusted data. If the next month's claimant count is known, the root-mean-squared error for the estimate of change is just over 10000.

[1]  Siem Jan Koopman,et al.  Signal extraction and the formulation of unobserved components models , 2000 .

[2]  Alastair Scott,et al.  A Stochastic Model for Repeated Surveys , 1973 .

[3]  G. Tunnicliffe Wilson,et al.  Parameterization of continuous time autoregressive models for irregularly sampled time series data , 1994 .

[4]  David F. Findley,et al.  New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program , 1998 .

[5]  A. Harvey,et al.  Detrending, stylized facts and the business cycle , 1993 .

[6]  Andrew Harvey,et al.  The econometric analysis of time series , 1991 .

[7]  T.M.F. Smith,et al.  13 – Principles and Problems in the Analysis of Repeated Surveys1 , 1978 .

[8]  Siem Jan Koopman,et al.  Computing Observation Weights for Signal Extraction and Filtering , 2003 .

[9]  David G Steel,et al.  Producing Monthly Estimates of Unemployment and Employment According to the International Labour Office Definition , 1997 .

[10]  Andrew Harvey,et al.  A unified view of statistical forecasting procedures , 1984 .

[11]  F. Diebold,et al.  Forecast Evaluation and Combination , 1996 .

[12]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[13]  Jerry A. Hausman,et al.  Errors in Variables and Seasonal Adjustment Procedures , 1985 .

[14]  Jurgen A. Doornik,et al.  Statistical algorithms for models in state space using SsfPack 2.2 , 1999 .

[15]  G. C. Tiao,et al.  Journal of the American Statistical Association Forecasting the U.s. Unemployment Rate Forecasting the U.s. Unemployment Rate , 2022 .

[16]  Agustín Maravall,et al.  Unobserved Components in Economic Time Series , 1993 .

[17]  Steven C. Hillmer,et al.  Some consideration of decomposition of a time series , 1978 .

[18]  Danny Pfeffermann,et al.  Estimation and Seasonal Adjustment of Population Means Using Data from Repeated Surveys , 1991 .

[19]  Danny Pfeffermann,et al.  Estimation of Autocorrelations of Survey Errors with Application to Trend Estimation in Small Areas , 1998 .

[20]  S. Frühwirth-Schnatter.  Data Augmentation and Dynamic Linear Models , 1994 .

[21]  Nicholas T. Longford,et al.  Multivariate shrinkage estimation of small area means and proportions , 1999 .

[22]  T.M.F. Smith,et al.  Modelling Compositional Time Series from Repeated Surveys , 2001 .

[23]  Estela Bee Dagum,et al.  Dynamic linear models for time series components , 1993 .

[24]  Ray Thomas.  ILO Unemployment and Registered Unemployment: a Case Study , 1998 .