Aggregation and Econometric Models

Fromm, Gary; Schink, George R.

doi:10.2307/2526041

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…The work at the Brookings Institution in the 1960s incorporated several related themes, ranging from the creation of databases by James Craig [79]-based upon programming by Mark Eisner and later partially utilizing the NBER database created by Charlotte Boschan and others [31,276], to the creation of software to condense Input-Output tables by McCarthy and Renfro [212], to the early development of econometric modeling packages such as MODLER [274,281,283] and PLANETS [32,33]. The first task to which MODLER was applied, and which initially caused its creation, was the estimation of the approximately 200 equation "condensed" Brookings model [115,116]. 13 PLANETS, which became generally available at Brookings in 1971, was used subsequently at Wharton Econometric Forecasting Associates, under the name DAMSEL; it was used until 1987, just before the merger of that organization with Chase Econometric Forecasting Associates.…”

Section: The Beginningsmentioning

confidence: 99%

“…13 Prior to 1969, the several versions of the Brookings model not only were the "large" version, but as systems of equations also conceptually represented an after-the-fact attempt to merge into a coherent model the independently developed sectors described in the first two Brookings volumes [79,80] and elsewhere [241]. In contrast, the "condensed" model represented at its creation an attempt to evaluate how the degree of aggregation affected the properties of a model, but estimated by Fromm and Renfro at Brookings, in consultation with Klein, Kuh, and others, as a coherent model by initial design, albeit taking into account the sector specifications of the "large" model [113,115]. This model was to a degree subsequently reestimated in the very early 1970s by Schink during the process of his coding it into a Fortran language simulation package; two variants were created in this process.…”

Section: The Beginningsmentioning

confidence: 99%

“…Modern nonlinear model solution techniques, which is to say in this case the Gauss-Seidel method, were not used to solve the Wharton Model until 1968-69. The Brookings Model was not solved in this way until the early 1970s [114][115][116]300]. In the light of these computational circumstances, it is interesting to read many years later [44], an assertion that the predictive performance of the Brookings Model was poor, notwithstanding that no version of that model was ever used to make an actual ex ante prediction.…”

Section: Conclusion and Prospectivementioning

confidence: 99%

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Econometric software: The first fifty years in perspective

Renfro

2004

JEM

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This paper introduces a special issue of the Journal of Economic and Social Measurement on the development and evaluation of computer software for econometric applications. It describes at least certain aspects of the history of the development of this software during the past approximately 50 years, beginning with the first use of the programmable electronic computer by economists in the early 1950s. It considers the various types of software developed, ranging from packages that permit the user to select from a particular set of options, to those that form essentially an econometric modeling language, to those that offer econometric programming capabilities, potentially allowing the individual specification of the characteristics and properties of the operations performed. This paper provides a relatively extensive list of references and is supplemented by a separate compendium of existing econometric software packages.

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Section: The Beginningsmentioning

confidence: 99%

Section: The Beginningsmentioning

confidence: 99%

Section: Conclusion and Prospectivementioning

confidence: 99%

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Econometric software: The first fifty years in perspective

Renfro

2004

JEM

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“…Over longer projection horizons, it is commonly thought that the projections of a monthly model will deteriorate at a faster rate than those of a quarterly model (Fromm and Hwa, 1980), although this conjecture is subject to empirical testing Effect of linkage in post-linkage periods. Regardless of whether multi-step forecasting is performed in the nalve fashion or via repeated linkage, there is interest in knowing the effect of improvements to the first (few) forecast quarter(s) on forecasts of subsequent quarters.…”

Section: Multi-step Linkagementioning

confidence: 99%

Reducing uncertainty in short‐term projections: Linkage of monthly and quarterly models

Corrado

Greene

1988

Journal of Forecasting

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This paper shows how monthly data and forecasts can be used in a systematic way to improve the predictive accuracy of a quarterly macroeconometric model. The problem is formulated as a model pooling procedure (equivalent to non-recursive Kalman filtering) where a baseline quarterly model forecast is modified through 'add-factors' or 'constant adjustments'. The procedure 'automatically' constructs these adjustments in a covariance-minimizing fashion to reflect the revised expectation of the quarterly model's forecast errors, conditional on the monthly information set. Results obtained using Federal Reserve Board models indicate the potential for significant reduction in forecast error variance through application of these procedures. K E Y \\ORUSForecasting Forecast pooling Macro-econometric models Kalman filteringThe typical process of generating a model-based forecast of economic activity involves more than mechanical simulation of a macro-econometric model forward in time. A model-based forecast is conditioned on values supplied for exogenous variables and on assumptions concerning the time paths of policy instruments. In addition, forecasters typically use information outside the model's data base-often from myriad and diverse sources-in formulating their final projection. This information may consist of timely high frequency measurements or data revisions, anticipated events not captured by the model structure, and e\en the predictions of other forecasters. The use of such information to alter a model's 5olution is often termed 'judgemental modification'.In surveying published descriptions of how private forecasters use their models, McNees reported that they attribute anywhere from 20 to 50 percent of the final forecast to the process of judgemental adjustments (1981, pp. 6-7). At the Federal Reserve Board (FRB), the staff outlook is anchored about a consensus projection of the most probable outcome associated with given settings of the policy instruments. As described in Kalchbrenner and Tinsley (1977, pp. 40-44), the consensus projection involves a considerable judgemental component.'' The opinions express'ed are rhose of the authors and do nor necessai-ily rellecr the view, of the Board of Governois of the Federal Reserve System or other mernherc of iis $!aft'.

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Modeling Economic-Demographic Linkages: A Study of National and Regional Models

Ahlburg

1987

Forecasting in the Social and Natural Sciences

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Aggregation and Econometric Models

Cited by 15 publications

References 3 publications

Econometric software: The first fifty years in perspective

Econometric software: The first fifty years in perspective

Reducing uncertainty in short‐term projections: Linkage of monthly and quarterly models

Modeling Economic-Demographic Linkages: A Study of National and Regional Models

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