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

Robert Engle; Mark Watson

doi:10.1080/01621459.1981.10477720

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

Robert Engle, Mark Watson

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

225 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 language	English (US)
Pages (from-to)	774-781
Number of pages	8
Journal	Journal of the American Statistical Association
Volume	76
Issue number	376
DOIs	https://doi.org/10.1080/01621459.1981.10477720
State	Published - Dec 1981
Externally published	Yes

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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10.1080/01621459.1981.10477720

Cite this

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title = "A one-factor multivariate time series model of metropolitan wage rates",

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.",

keywords = "Dynamic factor analysis, Kalman filter, Method of scoring, State space model, Unobserved component estimation",

author = "Robert Engle and Mark Watson",

note = "Funding Information: * Kohcrt Engle is Professor. Ikpartment of Economics. University of California at San Diego. La Jolla. CA 91093. Mark Watson is Assistant Profe.;sjor. Department of Economics. Harvard University. Cambridge. MA 02138. This research was supported by NSF grant SOC 77-071hh. The authors are indebted to Clive W. J. Granger. David I.ilien. Adrian Pagan. and Andrew Harvey for u.;eful comments. suggestions. and encouragement at various stage\ of the reseal-ch. The authurs alone take credit for any remaining ~ri-cm.",

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doi = "10.1080/01621459.1981.10477720",

language = "English (US)",

volume = "76",

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journal = "Journal of the American Statistical Association",

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AU - Watson, Mark

N1 - Funding Information: * Kohcrt Engle is Professor. Ikpartment of Economics. University of California at San Diego. La Jolla. CA 91093. Mark Watson is Assistant Profe.;sjor. Department of Economics. Harvard University. Cambridge. MA 02138. This research was supported by NSF grant SOC 77-071hh. The authors are indebted to Clive W. J. Granger. David I.ilien. Adrian Pagan. and Andrew Harvey for u.;eful comments. suggestions. and encouragement at various stage\ of the reseal-ch. The authurs alone take credit for any remaining ~ri-cm.

PY - 1981/12

Y1 - 1981/12

N2 - 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.

AB - 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.

KW - Dynamic factor analysis

KW - Kalman filter

KW - Method of scoring

KW - State space model

KW - Unobserved component estimation

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ER -

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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