A one-factor multivariate time series model of metropolitan wage rates

Robert Engle, Mark Watson

Research output: Contribution to journalArticlepeer-review

196 Scopus citations

Abstract

The paper formulates and estimates a single-factor multivariate time series model. The model is a dynamic generalization of the multiple indicator (or factor analysis) model. It is shown to be a special case of the general state space model and can be estimated by maximum likelihood methods using the Kalman filter algorithm. The model is used to obtain estimates of the unobserved metropolitan wage rate for Los Angeles, based on observations of sectoral wages within the Standard Metropolitan Statistical Area. Hypothesis tests, model diagnostics, and out-of-sample forecasts are used to evaluate the model.

Original languageEnglish (US)
Pages (from-to)774-781
Number of pages8
JournalJournal of the American Statistical Association
Volume76
Issue number376
DOIs
StatePublished - Dec 1981
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Dynamic factor analysis
  • Kalman filter
  • Method of scoring
  • State space model
  • Unobserved component estimation

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