This paper examines the identifiability of the coefficients of a single equation in a simultaneous equation model which is nonlinear only in the variables. The concept of identifiability in this model is motivated and developed using the closely related concept of observational equivalence. This framework is then utilized to develop necessary and sufficient conditions for identifiability when the disturbances are required to be independent of the exogenous variables. The approach recommended by Fisher is shown to yield sufficient but not necessary conditions for identifiability. For several relatively common special cases the necessary and sufficient conditions are found to simplify to the familiar rank condition for identifiability in the linear model. THE SIMULTANEOUS EQUATION MODEL that is nonlinear only in the variables has enjoyed widespread application in economics. Such models are linear in the parameters and typically seem to be linear in the variables as well, when viewing a single equation. In many models the nonlinearity in the variables arises due to endogenous variables entering in different forms in different equations (logged and unlogged form, for example). In macroeconometric models nonlinearity in the variables arises when the model includes endogenous real, nominal, and price variables, which are nonlinearly related.2 Whatever the reason for the nonlinearity in the variables, it is important to determine the conditions under which the equations of such models can be identified.

For more than three decades, economic columnist Joseph A. Livingston has canvassed a panel of economists twice a year, eliciting their six-month and twelve-month forecasts for more than a dozen key variables. This study analyzes whether experts' predictions are unbiased, and whether complete use was made of all relevant, known information (unbiasedness and completeness being necessary conditions for fully rational expectations). Little bias was found in either half-year or full-year predictions, but extensive underutilization of information-particularly data on monetary growth-occurred. To prophecy is extremely difficult-especially with respect to future. Chinese proverb Do ECONOMISTS' EXPECTATIONS regarding key price and nonprice variables utilize all known, relevant information, in an unbiased, efficient manner? This is a worthy subject for research, for several reasons. Properties of experts' predictions likely form an upper bound for those of laymen. Further, as John Muth [14] has noted, the character of dynamic processes is typically very sensitive to way expectations are influenced by actual course of (p. 316); hence, we need to know precisely how events do affect expectations. Finally, common practice of replacing a variable's (generally unobserved) expectation with a proxy based on its past values will be unbiased (and will not cause bias in other

In this paper, the authors examine the consequences of adopting the random utility hypothesis as an approach for randomizing a system of demand equations. Random utility models are appealing since they allow the usual assumption of deterministic utility-maximizing behavior by each consumer to coexist with the apparent randomness across individuals that is exhibited by data. Their results show that the use of random utility models implies that the disturbances of the demand equations may not be homoskedastic, but must be functions of prices and/or income.