Contents
What is the SUR model?
In econometrics, the seemingly unrelated regressions (SUR) or seemingly unrelated regression equations (SURE) model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially different sets …
What can I use instead of OLS?
Least absolute deviation (LAD) (also known as least absolute residuals, least absolute errors and least absolute value) is an alternative method to the conventional ordinary least squares (OLS), for building regression models.
What does a OLS mean?
Ordinary least-squares
Ordinary least-squares (OLS) models assume that the analysis is fitting a model of a relationship between one or more explanatory variables and a continuous or at least interval outcome variable that minimizes the sum of square errors, where an error is the difference between the actual and the predicted value of the …
What is cross section Sur?
The Cross-section SUR setting allows for contemporaneous correlation between cross-sections (clustering by period), while the Period SUR allows for general correlation of residuals across periods for a specific cross-section (clustering by individual).
What is 3SLS?
The term three-stage least squares (3SLS) refers to a method of estimation that combines system equation, sometimes known as seemingly unrelated regression (SUR), with two-stage least squares estimation.
What are alternatives to regression analysis?
Alternative procedures include: Different linear model: fitting a linear model with additional X variable(s) Nonlinear model: fitting a nonlinear model when the linear model is inappropriate. Transformations: correcting nonnormality, nonlinearity, or unequal variances by transforming all the data values for X and/or Y.
What is cross-sectional data in statistics?
Cross-sectional data are the result of a data collection, carried out at a single point in time on a statistical unit. With cross-sectional data, we are not interested in the change of data over time, but in the current, valid opinion of the respondents about a question in a survey.
How is the SUR method similar to OLS?
Like OLS, the SUR method assumes that all the regressors are independent variables, but SUR uses the correlations among the errors in different equations to improve the regression estimates. The SUR method requires an initial OLS regression to compute residuals.
When do SUR estimates turn out to be equivalent to OLS?
There are two important cases when the SUR estimates turn out to be equivalent to the equation-by-equation OLS. These cases are: When the matrix Σ is known to be diagonal, that is, there are no cross-equation correlations between the error terms. In this case the system becomes not seemingly but truly unrelated.
How is the SUR model estimated using FGLS?
The SUR model is usually estimated using the feasible generalized least squares (FGLS) method. This is a two-step method where in the first step we run ordinary least squares regression for (1). The residuals from this regression are used to estimate the elements of matrix
Which is more efficient Sur or SUR method?
Such estimates are consistent, however generally not as efficient as the SUR method, which amounts to feasible generalized least squares with a specific form of the variance-covariance matrix.