Last edited by Zololmaran
Friday, July 10, 2020 | History

3 edition of A study of aggregation bias in estimating the market for home heating and cooling equipment found in the catalog.

A study of aggregation bias in estimating the market for home heating and cooling equipment

Denis John Wood

A study of aggregation bias in estimating the market for home heating and cooling equipment

by Denis John Wood

  • 319 Want to read
  • 1 Currently reading

Published by Lawrence Berkeley Laboratory .
Written in English


Edition Notes

StatementWood, D. J.
ContributionsRuderman, Henry., McMahon, James E.
The Physical Object
Pagination33p. $0.00 C.1.
Number of Pages33
ID Numbers
Open LibraryOL17587814M

aggregation bias using the within estimates.4 3. Empirical results Empirical results in Table 2 include CT’s original within estimates in column 1 and our fixed effects estimates, which decrease in the level of aggregation from left to right. In columns 2 and 3 we aggregate crime types into violent and property crime groups and estimate Eq. Downloadable! This study revisits the consistent aggregation (over households) property of almost ideal demand system (AIDS) models and presents a method to explicitly account for expenditure aggregation bias when estimating the aggregate AIDS model with time-series data. Ignoring aggregation bias can lead to biased and inconsistent parameter estimates and can cause aggregate demand functions.

market demand and supply, total tax revenues, aggregate wages, unemployment, and so forth. The valuation and allocation of scarce resources requires that attention be paid to large groups of individuals. It is important to study relationships among economic aggregates, and to bring individual economic behavior to bear on those relationships. The Gage Bias table shows that the average bias increases as the amount of liquid increases. And even though this was a small sample, the bias was statistically significant (P bias wasn't consistent at each reference value—there is a considerable range of bias among the estimates at each reference value.

  Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires . Start studying ML Measurement, Aggregation, & Analysis. Learn vocabulary, terms, and more with flashcards, games, and other study tools.


Share this book
You might also like
Vaux, 1649

Vaux, 1649

Windscale

Windscale

South Carolina Rules of Court

South Carolina Rules of Court

A teachers and textbook writers handbook on Japan.

A teachers and textbook writers handbook on Japan.

Foundation Grants Index 26/E

Foundation Grants Index 26/E

Four County Metro Street Atlas of Atlantic, Cape May, Cumberland, Salem Counties

Four County Metro Street Atlas of Atlantic, Cape May, Cumberland, Salem Counties

Lead-zinc deposits of the Boquira district, State of Bahia, Brazil

Lead-zinc deposits of the Boquira district, State of Bahia, Brazil

Introduction to analysis in the large.

Introduction to analysis in the large.

Indian handicraft

Indian handicraft

Colloque sur les institutions coloniales dans les Ame riques au XVIIIe sie cle.

Colloque sur les institutions coloniales dans les Ame riques au XVIIIe sie cle.

A presentation and evaluation of the hydrologic information available for the major Federal coal lands in seven eastern states

A presentation and evaluation of the hydrologic information available for the major Federal coal lands in seven eastern states

A study of aggregation bias in estimating the market for home heating and cooling equipment by Denis John Wood Download PDF EPUB FB2

A STUDY OF AGGREGATION BIAS IN ESTIMATING THE MARKET FOR HOME HEATING AND COOLING EQUIPMENT. Author(s): Wood, D.J. et al. Main Content Metrics Author & Article Info. Main Content. Download PDF to View View Larger.

Thumbnails Document Outline Author: D.J. Wood, H. Ruderman, J.E. McMahon. Bias is when a person prefers an idea and he or she does not give an equal chance to another idea.

By not giving the opposing idea a chance, the topic is being clouded. Bias can occur when certain language or stereotyping or one sided opinions are used to convey a message to the reader.

The reader would get influenced by those words and he or. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription. Please subscribe or login to access full text content. If you have purchased a print title that contains an access token, please see the token for information about how to register your code.

Aggregation bias is, generally, the incorrect assumption that "what is true about the group is true about the individual" (also known as the ecological fallacy). For example, children from poor.

This study revisits the consistent aggregation (over households) property of almost ideal demand system (AIDS) models and presents a method to explicitly account for expenditure aggregation bias when estimating the aggregate AIDS model with time- series data.

Ignoring aggregation bias can lead to biased and inconsistent parameter. Quantifying Bias. If the true value or an accepted reference value is available the bias is the difference between the average of all test results and the reference value.

Variations in precision and bias. Changes in the process due to material, operators, equipment, or environment change both precision and bias. / Geographical Analysis TABLE 1 CORRELATIOS AND SLOPE COEFFICIENTS DERIVED FROM LAMAS ASD CESSUS Tlucr DATA Data Set Method of Data Generalization r r2 b units: LAMAS Not applicable 1, units: Census tracts Tract mean units: Welfare Planning Croup mean 35 units: Regional Planning Croup mean.

to reveal aggregation bias. Aggregation bias is also observed in econometric estimations (e.g., Agostino et al., ; Hillberry, ). With the decreasing level of tariffs due to the increasing number of economic unions or free trade agreements (FTAs), the importance of non-tariff barriers (NTBs) in trade has risen.

Especially. A aggregation (econometrics) The econometrics of aggregation is about modelling the relationship between individual (micro) behaviour and aggregate (macro) statistics, so that data from both levels can be used for estimation and inference about economic parameters.

Practical models must address three types of individual heter. is a platform for academics to share research papers. Aggregation bias To motivate the issue of aggregation bias, it is informative to consider the classic examples offered by Theil 1 () and Gor-man2 ().

1 H. Theil, Linear Aggregation of Economic Relations (Amster-dam: North-Holland, ). 2 W.M. (Terence) Gorman, “Community preference fields,” Econometrica 21 (): 63– 3 0 Aggregation Bias and Ecological Fallacy Researchers need to design their studies at the appropriate unit of analysis.

This is especially important to understand so that one can avoid making improper inferences about units of analysis that were not actually analyzed. Confusing the units of analysis can lead to an 'ecological fallacy', where one. Kelejian [] provides a methodology for testing for the presence of this form of bias.

This study uses Monte Carlo analysis to evaluate the usefulness of Kelejian’s test. Using 50 units, populated by,individuals, we find that aggregation bias is almost universally present.

To me, the term bias indicates that by aggregation I should be systematically pushing the results to either overestimate or underestimate the size of relationships.

However, it's not clear to me that that actually happens. James, L. Aggregation bias in estimates of perceptual agreement. Journal of Applied Psychology, 67(2),   The average bias is an estimate of the true unknown average bias in a single study.

If the study were repeated, the estimate would be expected to vary from study to study. Therefore, if a single estimate is compared directly to 0 or compared to the allowable bias the statement is only applicable to the single study.

I cannot discuss forecasting bias without mentioning MAPE, but since I have written about those topics in the past, in this post, I will concentrate on Forecast Bias and the Forecast Bias Formula.

[bar group=”content”] What Is Forecast Bias. Forecast Bias can be described as a tendency to either over-forecast (forecast is more than the actual), or under-forecast (forecast is less than the.

This example appears in the Experiment Design and Analysis Reference book. If a baby is lbs and the reading of a scale is lbs, then the bias is lb. If an adult is 85 lbs and the reading from the same scale is lbs, then the bias is still lb. This scale does not seem to have a linearity issue.

Example of a gage linearity and bias study. An engineer works for a company that manufactures several types of screws that have different diameters. The engineer wants to know whether bias is present in the measurement system, and whether this bias is constant, independent of the outer diameter of the screw.

Abstract. It is common practice to aggregate inputs prior to estimating the structure of production technology. It is of interest, therefore to have some idea of the impact of such aggregation on the resulting inferences concerning the structure of production technology.

A study of aggregation bias in estimating the market for home heating and cooling equipment. Article. Using data at some other level of aggregation introduces bias into the inferences made.

Article Citation: Donald Jud and Terry Seaks () Sample Selection Bias in Estimating Housing Sales Prices. Journal of Real Estate Research:Vol. 9, No. 3, pp. Downloadable (with restrictions)! This paper documents a study about the influence of the aggregation effect on the estimates of models based on the original Basu model – specifically the Ball, Kothari and Nikolaev model (Ball et al., b).

We provide an analytical study of the effect, showing that it can produce two biases: an omitted-variable bias and a truncated-sample bias. Step 2: Collect data for bias study The published data in figure 1 are from the same gauge mentioned above.

It is an automated test device that uses a dial gauge. Figure 1: Data for bias study. Step 3: Analyze bias Figure 2 shows the bias analysis as presented in the stability module of GAGEtrak software.