Geoffrey Moore Research Articles

The Use of Composite Indicators

THE ARTICLE A of Activity (by Adam Gostomski and Bohdan J. Kekish) in the March-April 1965 issue of the Financial Analysts Journal prompts me to raise a few questions and make a few comments based on several years of experience in dealing with leading indicators. The use of a single composite indicator of leading is not new. In the absence of any really scholarly research, we can immediately point to the Statistical Indicator Associates composite index of NBER leading indicators, which has been publicly available since 1955, and to similar composites devised by Dr. Geoffrey Moore of the NBER and by Edgar Fiedler of Bankers Trust Company. The SIA composite is an extremely simple statistical concept based on a simple averaging of individual indexes for 10 of the 12 NBER leading indicators. Dr. Moore's composite is refined by weighting the according to their relative cyclical amplitude. And Mr. Fiedler has experimented with composites based on long-lead and short-lead indicators. Nevertheless, with a minimum of statistical manipulation, the simplest of these composites, the SIA composite, can be made to provide a graphic comparison with the FRB index of industrial production that, I submit, is quite comparable in its results with the Business Criterion of Gostomski and Kekish as the answer to the busy analyst, or one frankly unable to reach a decision from many indicators (to use the words of the two authors mentioned).' My experience is, however, that neither composite provides any such answer. On the contrary, without an accompanying interpretation, the busy analyst is apt to be greatly confused and/or seriously misled.

Geoffrey Moore (ed:) American Literature, A Representative Anthology of American Writing from Colonial Times to the Present (London, Faber and Faber, 1964,) 25s.; pp. 1328.

Geoffrey Moore (ed:) American Literature, A Representative Anthology of American Writing from Colonial Times to the Present (London, Faber and Faber, 1964,) 25s.; pp. 1328.

On the Appraisal of Cyclical Turning-Point Predictors

IN ORDER to review past economic forecasts and to investigate methods of improving economic predictions in the future, economists require criteria for appraising the predictive accuracy of their forecasts. For quantitative predictions of national product and its components, such criteria have been advanced and discussed. Forecast errors are naturally measurable as dollar deviations between predicted and actual magnitudes. The errors in a number of different forecasts can therefore be readily compared. In addition, naive models which imply persistence of either the rate of change or the level of national product have been accepted as benchmarks for comparison with economists' predictions. There has been little discussion, however, of criteria for appraising the accuracy of cyclical turning-point forecasts, which project reversals in the direction of change of economic activity. This paper advances a method for evaluating turningpoint predictions and illustrates the operation of the proposed system on historical data collected by the National Bureau of Economic Research. The assumptions underlying the scoring system and the characteristics of the method are discussed below. Few claims are made for the system; it is far from ideal. It is offered with the conviction that some objective standard of appraisal is required for turning-point forecasts and that the specification of one possible system may evoke fruitful discussion of the problem. If existing methods of forecasting are to be evaluated and if new methods are to be sought, it must be possible to recognize a good forecast and to distinguish degrees of accuracy. Even if a particular forecasting tool or method is used in combination with other evidence and judgment, it is desirable to determine the predictive contribution of that element of the forecast. It may well be that informed judgment can produce successful forecasting. Still, it is reasonable to inquire whether a particular tool is an aid or a hindrance to the exercise of good judgment. The possibility of employing a method subjectively does not prevent the objective appraisal of that method. Only if predictive techniques can be evaluated objectively can the accuracy of economic forecasting be viewed as a test of methods rather than of men. In principle, the value of a prediction should be gauged in terms of the uses for which the forecast is desired. In order to obtain guidance for their actions, decision-makers can exercise a choice among * This paper was written as part of the project in Research on Short-Term Economic Forecasting conducted at the Cowles Foundation for Research in Economics at Yale University and financed by the Rockefeller Foundation. I am indebted to Mrs. Wilma W. Heston for her valuable assistance in processing the data presented below. Dr. Geoffrey Moore, of the National Bureau of Economic Research, kindly provided me with data and also offered helpful criticisms. The F. W. Dodge Corporation authorized the release of its data on construction contracts. I am also grateful to Harry Eisenpress, William Fellner, Todd May, Richard Porter, Robert Summers, and James Tobin for their constructive suggestions.

XVI. AMERICAN LITERATURE

XVI. AMERICAN LITERATURE Get access GEOFFREY MOORE GEOFFREY MOORE Search for other works by this author on: Oxford Academic Google Scholar The Year's Work in English Studies, Volume XXXIX, Issue 1, 1960, Pages 283–303, https://doi.org/10.1093/ywes/XXXIX.1.283 Published: 01 September 1960

Correlations among Time Series and Moore's Statistical Indicators

A recent exchange between Stefan Valavanis and Geoffrey Moore invites attention to one of the most important unresolved issues in statistical analysis, namely, the proper procedure for assessing the significance of relationships among time series.' The particular subject of discussion in that exchange was the statistical significance that can be attributed to the behavior of those time series selected by Moore to forecast cyclical movements in busines sactivity. It is apparent that an answer to this question of significance is invaluable not only for prediction in the present instance but also in the whole field of time series analysis. It is to this general question and to this particular application that the following discussion is addressed.

CHANGES IN QUALITY OF BANK CREDIT

GEOFFREY MOORE'S THOUGHTFUL discussion of Quality of Credit in Booms and Depressions reveals clearly the need for research on this general subject. He has pointed out how little is available in the way of quantitative data for measuring changes in the quality of bank credit, particularly business loans of banks. The program of research proposed by the National Bureau should contribute significantly to knowledge in this field and deserves the wholehearted support of bankers everywhere. In dealing with the question of changes in quality of bank credit, three types of problems emerge: the first deals with the concepts or definition of quality; the second deals with measurement of quality and changes therein; and the third with the nature and validity of the inferences and conclusions to be drawn from the analysis of changes.