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technique whatsoever in which the true demand (or supply) curve can be estimated. Nor, indeed, is the problem here one of statistical inference—of separating out the effects of random disturbance. There is no disturbance in this model It is the logic of the supply-demand equilibrium itself which
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In the graph shown here, the supply curve (red line, upward sloping) shows the quantity supplied depending positively on the price, while the demand curve (black lines, downward sloping) shows quantity depending negatively on the price and also on some additional variable
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of some specific good. The quantity demanded varies negatively with the price: a higher price decreases the quantity demanded. The quantity supplied varies directly with the price: a higher price increases the quantity supplied.
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is the matrix of coefficients of the equations. This is the generalization in matrix algebra of the requirement "while it does enter the other equation" mentioned above (in the line above the formulas).
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equation. The (negative) slope parameter of the demand equation cannot be identified in this case. In other words, the parameters of an equation can be identified if it is known that some variable does
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Assume that, say for several years, we have data on both the price and the traded quantity of this good. Unfortunately this is not enough to identify the two equations (demand and supply) using
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for identification. (The general form of the order condition deals also with restrictions other than exclusions.) The order condition is necessary but not sufficient for identification.
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With the quantities supplied and demanded being equal, the observations on quantity and price are the three white points in the graph: they reveal the supply curve. Hence the effect of
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It is important to note that the problem is not one of the appropriateness of a particular estimation technique. In the situation described , there clearly exists
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an upward slope with one linear regression line involving only two variables. Additional variables can make it possible to identify the individual relations.
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condition for identification. In the case of only exclusion restrictions, it must "be possible to form at least one nonvanishing determinant of order
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might be consumers' income, with a rise in income shifting the demand curve outwards. This is symbolically indicated with the values 1, 2 and 3 for
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For example, this problem can occur in the estimation of multiple-equation econometric models where the equations have variables in common.
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has more than one set of parameters that generate the same distribution of observations, meaning that multiple parameterizations are
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A situation in which both the supply and the demand equation are identified arises if there is not only a variable
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Fisher points out that this problem is fundamental to the model, and not a matter of statistical estimation:
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corresponding to the variables excluded a priori from that equation" (Fisher 1966, p. 40), where
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668:(2019-12-01). "The Identification Zoo: Meanings of Identification in Econometrics".
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Greenberg, Edward; Webster, Charles E. Jr. (1983). "The
Identification Problem".
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entering the demand equation but not the supply equation, but also a variable
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cannot be determined from observable variables. It is closely related to
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Parameter estimation technique in statistics, particularly econometrics
702:(2013). "Nonparametric Identification in Structural Economic Models".
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Rothenberg, Thomas J. (1971). "Identification in
Parametric Models".
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82:
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622:(1949). "Identification problems in economic model construction".
556:(Fifth ed.). New York: McGraw-Hill Irwin. pp. 692–698.
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enter into the equation, while it does enter the other equation.
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An equation cannot be identified from the data if less than
604:(Second ed.). New York: Macmillan. pp. 660–672.
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entering the supply equation but not the demand equation:
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makes it possible to identify the (positive) slope of the
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650:("A classic and masterful exposition of the subject",
530:. New York: John Wiley & Sons. pp. 221–241.
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414:leads to the difficulty. (Fisher 1966, p. 5)
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580:. Princeton University Press. pp. 200–203.
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386:of the model, showing the relations between the
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678:(4). American Economic Association: 835–903.
423:More generally, consider a linear system of
766:, Handbook of Econometrics, Vol. 1, Ch.4,
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509:The Identification Problem in Econometrics
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69:Learn how and when to remove this message
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371:. Here both equations are identified if
32:This article includes a list of general
718:10.1146/annurev-economics-082912-110231
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163:: one cannot estimate a downward slope
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482:Errors-in-variables model#Linear model
109:and econometrics, which occurs when a
782:Lecture on the identification problem
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93:arises when the value of one or more
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139:Standard example, with two equations
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478:, the related problem in statistics
347:{\displaystyle Q=a_{D}+b_{D}P+dZ\,}
281:{\displaystyle Q=a_{S}+b_{S}P+cX\,}
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398:however can be identified easily.
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38:it lacks sufficient corresponding
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768:North-Holland Publishing Company
143:Consider a linear model for the
124:In simultaneous equations models
91:parameter identification problem
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671:Journal of Economic Literature
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130:Simultaneous equations model
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705:Annual Review of Economics
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115:observationally equivalent
601:Elements of Econometrics
451:necessary and sufficient
53:more precise citations.
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382:Note that this is the
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762:Hsiao, Cheng (1983),
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128:Further information:
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546:Gujarati, Damodar N.
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155:on observations of
153:regression analysis
103:non-identifiability
554:Basic Econometrics
492:Set identification
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806:Estimation theory
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174:Supply and demand
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51:introducing
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498:References
409:way using
107:statistics
95:parameters
34:references
747:0012-9682
692:0022-0515
83:economics
800:Category
598:(1986).
574:(2000).
552:(2009).
507:(1966).
470:See also
787:YouTube
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463:A
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