Titlebook: Statistical Analysis and Forecasting of Economic Structural Change; Peter Hackl Book 1989 Springer-Verlag Berlin Heidelberg 1989 data anal

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The Use of Graphical Displays in the Analysis of Structural Changels; (b) progressive change in the variability of the residuals; (c) a curved regression of residuals on fitted values or the number of cases; and (d) the subsets of successive residuals with significantly different configuration. In this chapter we concentrate on (d) which indicates possible structu

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Adaptive Estimation and Structural Change in Regression and Time Series Models introduces time-varying coefficients explicitly and assumes that the coefficients follow certain autoregressive integrated moving average time series processes. We show how these time-varying coefficient models can be written in state space form, we illustrate how the Kalman filter approach can be

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An Adaptive Method of Regression Analysisally weighted moving averages (EWMA). The coefficients’ trajectories imply possible improvements of the model specification. Aspects of suitable preparation of the time series, such as the elimination of time trends and parameter estimation, are also considered. The method is illustrated on the basi

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Changing and Random Coefficient Models. A Surveyccepted parameter variation structure one may classify such models into two main groups: models with variable but nonstochastic parameters and models with randomly varying coefficients. The latter group consists of two types — models where coefficients are generated from stationary and models in whi

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Nonparametric Estimation of Time-Varying Parametersle random variable with zero mean and unit variance, .. is an observable .-vector-valued variable, and .. and .. are, respectively, unobservable scalar and .-vector-valued parameters. No model (stochastic or nonstochastic) is assumed for the .. or ..; instead they are assumed to be smoothly varying

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Adaptive Estimation and Structural Change in Regression and Time Series Modelse the coefficient estimates. It is shown how these parameters can be estimated from historic observations. These parameters determine how adaptive the resulting coefficient estimates are to changes in the coefficients.

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What Can Statistics Contribute to the Analysis of Economic Structural Change?c models. In addition, it is noted that major changes in the sample correlations between variables, rather than being a nuisance for econometric model builders, is in fact an important stimulus to model evaluation and improvement.