This is the third of four papers which will be published in close sequence in this journal. For a general distributed-lag transfer-function model of regional economic structure, the paper examines the nature of patterns of parameter variation that may be present. Methods of estimating such adaptive or innovative changes in parameter variation are then discussed, and one method based upon assuming a random-walk model of parameter behaviour is applied to estimating dynamic parameter trends, shifts, and stochastic variation for a five-equation model of the North-west region. Changes in this model structure are found to characterise, mainly, parameters associated with employment linkage, and migration and industrial-movement responses. In most cases, the employment-exchange areas in the North-west are shown to be increasingly amplifying, rather than damping, national economic fluctuations. An attempt is also made to link the observed shifts in parameter values to changes in policy instruments.
Get full access to this article
View all access options for this article.
References
1.
AndeenR. E.ShipleyP. P., 1963, “Digital adaptive flight control system for aerospace vehicles”, American Institute of Aeronautics and Aerospace, 1, 1105–1109.
2.
BennettR. J., 1974, Methods of Dynamic Modelling of Spatial Systems: The Case of North West England, Ph. D. Thesis, University of Cambridge, Cambridge, England.
3.
BennettR. J., 1975a, “The representation and identification of spatio—temporal systems: An example of population diffusion in North West England”, Transactions of the Institute of British Geographers, 66, forthcoming.
4.
BennettR. J., 1975b, “Dynamic systems modelling of the North-west region: 2. Estimation of the spatio—temporal policy model”, Environment and Planning A, 7, 539–566.
5.
BennettR. J., 1976a, “Nonstationary parameter estimation for small situations: A comparison of methods”, International Journal of Systems Science, forthcoming.
6.
BennettR. J., 1976b, “Consistent estimation of nonstationary parameters for small sample situations”, International Economic Review, forthcoming.
7.
BoxG. E. P.JenkinsG. M., 1970, Time Series Analysis, Forecasting and Control (Holden-Day, San Francisco).
8.
Hastings-JamesR.SageM. W., 1969, “Recursive generalised least squares procedure for on-line identification of process parameters”, Proceedings of the Institution of Electrical Engineers, 116, 2057–2062.
9.
JazwinskiA. H., 1970, Stochastic Processes and Filtering Theory (Academic Press, New York).
10.
KalaithT., 1968, “An innovations approach to least squares estimation. Part 1”, Transactions of the IEEE, Automatic Control, AC-13, 646–655.
11.
KalmanR. E., 1960, “A new approach to linear filtering and prediction problems”, Transactions of the ASME, Journal of Basic Engineering, D, 82, 35–45.
12.
KalmanR. E., 1966, “On structural properties of linear, constant, multivariable systems”, paper 6A, Proceedings of the International Federation of Automatic Control, Third World Congress, Prague.
13.
KalmanR. E.BucyR. S., 1961, “New results in linear filtering and prediction theory”, Transactions of the ASME, Journal of Basic Engineering D, 83, 95–108.
14.
LeeR. C. K., 1964, Optimal Estimation, Identification and Control, research monograph number 28 (MIT Press, Cambridge, Mass.).
15.
MannH. B.WaldA., 1943, “On the statistical treatment of linear stochastic difference equations”, Econometrica, 11, 173–220.
16.
MayneD. Q., 1967, “A method for estimating discrete time transfer functions”, paper C2, in Advances in Computer Control, Second UKAC Control Convention, Bristol, Institution of Electrical Engineers, London.
17.
MilesC., 1968, Lancashire Textiles: A case study of Industrial Change, occasional paper number 23, National Institute of Economic and Social Research (Cambridge University Press, Cambridge, England).
18.
MooreB.RhodesJ., 1973, “Evaluating the effects of British Regional Policy”, Economic Journal, 83, 87–110.
19.
PlackettR. L., 1950, “Some theorems in least-squares”, Biometrika, 37, 149–157.
20.
ReiersølO., 1941, “Confluence analysis by means of lag moments and other methods of confluence analysis”, Econometrica, 9, 1–24.
21.
SageA. P.HusaG. W., 1969, “Algorithms for sequential adaptive estimation of prior statistics”, paper 6a, Proceedings of the 8th IEEE Symposium on Adaptive Processes.
22.
ShellenbargerJ. C., 1966, “Estimation of covariance parameters for an adaptive Kalman filter”, Proceedings of the National Electronics Conference, 22, 698–702.
23.
SteiglitzK.McBrideL. E., 1965, “A technique for the identification of linear systems”, Transactions of the IEEE, Automatic Control, AC-10, 461–464.
24.
TzafestasS. G.NightingaleJ.M., 1968, “Optimal filtering, smoothing and prediction in linear distributed-parameter systems”, Proceedings of the Institution of Electrical Engineers, 115, 1207–1212.
25.
WilsonA. G., 1970, Entropy in Urban and Regional Modelling (Pion, London).
26.
YaglomA. M., 1955, “Correlation theory of processes with random stationary nth increments”, Matematicheskii Sbornik, 37(79), 141–196, American Mathematical Society Translations, 2(8), 87–141.
27.
YoungP. C., 1970, “An instrumental variable method for the real-time identification of a noisy process”, Automatica, 6, 271–297.
