Geweke, J 1984, 'Inference and Causality in Economic Time Series Models' in Griliches, Z & Intriligator, MD (eds), Handbook of Econometrics, Volume 2, North-Holland, Amsterdam, pp. 1101-1144.
Geweke, J 1984, 'Forecasting and Conditional Projection Using Realistic Prior Distributions', Econometric Reviews, vol. 5, no. 1, pp. 105-112.
Abstract: Measures of linear dependence and feedback for two multiple time series conditional on a third are defined. The measure of conditional linear dependence is the sum of linear feedback from the first to the second conditional on the third, linear feedback from the second to the first conditional on the third, and instantaneous linear feedback between the first and second series conditional on the third. The measures are non-negative and may be expressed in terms of measures of unconditional feedback between various combinations of the three series. The measures of conditional linear feedback can be additively decomposed by frequency. Estimates of these measures are straightforward to compute, and their distribution can be routinely approximated by bootstrap methods. An empirical example involving real output, money, and interest rates is presented.
Geweke, J 1984, 'The Indispensable Art of Econometrics', Journal of the American Statistical Association, vol. 79, pp. 25-26.
GEWEKE, J 1984, 'THE INDISPENSABLE ART OF ECONOMETRICS - COMMENT', JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, vol. 79, no. 385, pp. 25-26.
Geweke, J & Meese, R 1984, 'A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series', Journal Of Business & Economic Statistics, vol. 2, pp. 187-202.
Abstract: The actual performance of several automated univariate autoregressive forecasting procedures, applied to 150 macroeconomic time series, are compared. The procedures are the random walk model as a basis for comparison; long autoregressions, with three alternative rules for lag length selection; and a long autoregression estimated by minimizing the sum of absolute deviations. The sensitivity of each procedure to preliminary transformations, data, periodicity, forecast horizon, loss function employed in parameter estimation, and seasonal adjustment procedures is examined. The more important conclusions are that Akaike's lag-length selection criterion works well in a wide variety of situations, the modeling of long memory components becomes important for forecast horizons of three or more periods, and linear combinations of forecasts do not improve forecast quality appreciably.
Abstract: The definitions of fractional Gaussian noise and integrated (or fractionally differenced) series are generalized, and it is shown that the two concepts are equivalent. A new estimator of the long memory parameter in these models is proposed, based on the simple linear regression of the log periodogram on a deterministic regressor. The estimator is the ordinary least squares estimator of the slope parameter in this regression, formed using only the lowest frequency ordinates of the log periodogram. Its asymptotic distribution is derived, from which it is evident that the conventional interpretation of these least squares statistics is justified in large samples. Using synthetic data the asymptotic theory proves to be reliable in samples of 50 observations or more. For three postwar monthly economic time series, the estimated integrated series model provides more reliable out-of-sample forecasts than do more conventional procedures.
Geweke, J & Weisbrod, B 1984, 'How Does Technological Change Affect Health Care Expenditures? The Case of a New Drug', Evaluation Review, vol. 8, no. 1, pp. 75-92.
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Abstract: The expenditure consequences of the drug cimetidine for the period 1977-1979 are investigated. Using Medicaid data for the State of Michigan, it is found that expenditures for the first year of treatment of duodenal ulcers are reduced between 26% and 70% The methodology employed can be applied to the assessment of other medical technologies.
Meese, R & Geweke, J 1984, 'A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series.', Journal of Business and Economic Statistics, vol. 2, no. 3, pp. 191-200.