Abstract
which the relationships are not exact, so that a set of ideal economic variables is assumed to be generated by a set of dynamic stochastic relationships, as in Koopmans [12], and the actual economic time series are assumed to differ from the ideal economic variables because of random disturbances or measurement errors. The asymptotic error variance matrix for the coefficients of one of the relationships is obtained in the case in which these relationships are estimated using instrumental variables. With this variance matrix we are able to discuss the problem of choice that arises when there are more instrumental variables available than the minimum number required to enable the method to be used. A method of estimation is derived which involves a characteristic equation already considered by Hotelling in defining the canonical correlation [10]. This method was previously suggested by Durbin [7]. The same estimates would be obtained by the maximum-likelihood limited
Keywords
Related Publications
Approximate Inference in Generalized Linear Mixed Models
Statistical approaches to overdispersion, correlated errors, shrinkage estimation, and smoothing of regression relationships may be encompassed within the framework of the gener...
A general method for analysis of covariance structures
It is assumed that observations on a set of variables have a multivariate normal distribution with a general parametric form of the mean vector and the variance-covariance matri...
External Single-Set Components Analysis Of Multiple Criterion/Multiple Predictor Variables
Although much progress has been made in clarifying the properties of canonical correlation analysis in order to enhance its applicability, there are several remaining problems. ...
Co-Integration and Error Correction: Representation, Estimation, and Testing
The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and em...
Panel Data and Unobservable Individual Effects
Abstract An important purpose in pooling time-series and cross-section data is to control for individual-specific unobservable effects which may be correlated with other expla...
Publication Info
- Year
- 1958
- Type
- article
- Volume
- 26
- Issue
- 3
- Pages
- 393-393
- Citations
- 3292
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.2307/1907619