Simultaneous equation models are a form of statistical model in the form of a set of linear simultaneous equations. They are often used in econometrics.
Contents

Structural and reduced form 1

Estimation 2

Twostages least squares (2SLS) 2.1

Indirect least squares 2.2

Limited information maximum likelihood (LIML) 2.3

Threestage least squares (3SLS) 2.4

See also 3

Notes 4

References 5

External links 6
Structural and reduced form
Suppose there are m regression equations of the form

y_{it} = y_{i,t}'\gamma_i + x_{it}'\;\!\beta_i + u_{it}, \quad i=1,\ldots,m,
where i is the equation number, and t = 1, ..., T is the observation index. In these equations x_{it} is the k_{i}×1 vector of exogenous variables, y_{it} is the dependent variable, y_{−i,t} is the n_{i}×1 vector of all other endogenous variables which enter the i^{th} equation on the righthand side, and u_{it} are the error terms. The “−i” notation indicates that the vector y_{−i,t} may contain any of the y’s except for y_{it} (since it is already present on the lefthand side). The regression coefficients β_{i} and γ_{i} are of dimensions k_{i}×1 and n_{i}×1 correspondingly. Vertically stacking the T observations corresponding to the i^{th} equation, we can write each equation in vector form as

y_i = Y_{i}\gamma_i + X_i\beta_i + u_i, \quad i=1,\ldots,m,
where y_{i} and u_{i} are T×1 vectors, X_{i} is a T×k_{i} matrix of exogenous regressors, and Y_{−i} is a T×n_{i} matrix of endogenous regressors on the righthand side of the i^{th} equation. Finally, we can move all endogenous variables to the lefthand side and write the m equations jointly in vector form as

Y\Gamma = X\Beta + U.\,
This representation is known as the structural form. In this equation Y = [y_{1} y_{2} ... y_{m}] is the T×m matrix of dependent variables. Each of the matrices Y_{−i} is in fact an n_{i}columned submatrix of this Y. The m×m matrix Γ, which describes the relation between the dependent variables, has a complicated structure. It has ones on the diagonal, and all other elements of each column i are either the components of the vector −γ_{i} or zeros, depending on which columns of Y were included in the matrix Y_{−i}. The T×k matrix X contains all exogenous regressors from all equations, but without repetitions (that is, matrix X should be of full rank). Thus, each X_{i} is a k_{i}columned submatrix of X. Matrix Β has size k×m, and each of its columns consists of the components of vectors β_{i} and zeros, depending on which of the regressors from X were included or excluded from X_{i}. Finally, U = [u_{1} u_{2} ... u_{m}] is a T×m matrix of the error terms.
Postmultiplying the structural equation by Γ^{ −1}, the system can be written in the reduced form as

Y = X\Beta\Gamma^{1} + U\Gamma^{1} = X\Pi + V.\,
This is already a simple general linear model, and it can be estimated for example by ordinary least squares. Unfortunately, the task of decomposing the estimated matrix \scriptstyle\hat\Pi into the individual factors Β and Γ^{ −1} is quite complicated, and therefore the reduced form is more suitable for prediction but not inference.
Assumptions
Firstly, the rank of the matrix X of exogenous regressors must be equal to k, both in finite samples and in the limit as T → ∞ (this later requirement means that in the limit the expression \scriptstyle \frac1TX'\!X should converge to a nondegenerate k×k matrix). Matrix Γ is also assumed to be nondegenerate.
Secondly, error terms are assumed to be serially independent and identically distributed. That is, if the t^{th} row of matrix U is denoted by u_{(t)}, then the sequence of vectors {u_{(t)}} should be iid, with zero mean and some covariance matrix Σ (which is unknown). In particular, this implies that E[U] = 0, and E[U′U] = T Σ.
Lastly, the identification conditions require that the number of unknowns in this system of equations should not exceed the number of equations. More specifically, the order condition requires that for each equation k_{i} + n_{i} ≤ k, which can be phrased as “the number of excluded exogenous variables is greater or equal to the number of included endogenous variables”. The rank condition of identifiability is that rank(Π_{i0}) = n_{i}, where Π_{i0} is a (k − k_{i})×n_{i} matrix which is obtained from Π by crossing out those columns which correspond to the excluded endogenous variables, and those rows which correspond to the included exogenous variables.
Estimation
Twostages least squares (2SLS)
The simplest and the most common^{[1]} estimation method for the simultaneous equations model is the socalled twostage least squares method, developed independently by Theil (1953) and Basmann (1957). It is an equationbyequation technique, where the endogenous regressors on the righthand side of each equation are being instrumented with the regressors X from all other equations. The method is called “twostage” because it conducts estimation in two steps:^{[2]}

Step 1: Regress Y_{−i} on X and obtain the predicted values \scriptstyle\hat{Y}_{\!i};

Step 2: Estimate γ_{i}, β_{i} by the ordinary least squares regression of y_{i} on \scriptstyle\hat{Y}_{\!i} and X_{i}.
If the i^{th} equation in the model is written as

y_i = \begin{pmatrix}Y_{i} & X_i\end{pmatrix}\begin{pmatrix}\gamma_i\\\beta_i\end{pmatrix} + u_i \equiv Z_i \delta_i + u_i,
where Z_{i} is a T×(n_{i} + k_{i}) matrix of both endogenous and exogenous regressors in the i^{th} equation, and δ_{i} is an (n_{i} + k_{i})dimensional vector of regression coefficients, then the 2SLS estimator of δ_{i} will be given by^{[3]}

\hat\delta_i = \big(\hat{Z}'_i\hat{Z}_i\big)^{1}\hat{Z}'_i y_i = \big( Z'_iPZ_i \big)^{1} Z'_iPy_i,
where P = X (X ′X)^{−1}X ′ is the projection matrix onto the linear space spanned by the exogenous regressors X.
Indirect least squares
Indirect least squares is an approach in econometrics where the coefficients in a simultaneous equations model are estimated from the reduced form model using ordinary least squares.^{[4]}^{[5]} For this, the structural system of equations is transformed into the reduced form first. Once the coefficients are estimated the model is put back into the structural form.
Limited information maximum likelihood (LIML)
The “limited information” maximum likelihood method was suggested by Anderson & Rubin (1949). It is used when one is interested in estimating a single structural equation at a time (hence its name of limited information), say for observation i:

y_i = Y_{i}\gamma_i +X_i\beta_i+ u_i \equiv Z_i \delta_i + u_i
The structural equations for the remaining endogeneous variables Y_{−1} are not specified, and they are given in their reduced form:

Y_{i} = X \Pi + U_{1}
Notation in this context is different than for the simple IV case. One has:

Y_{i}: The endogeneous variable(s).

X_{i}: The exogeneous variable(s)

X: The instrument(s) (often denoted Z)
The explicit formula for the LIML is:^{[6]}

\hat\delta_i = \Big(Z'_i(I\lambda M)Z_i\Big)^{\!1}Z'_i(I\lambda M)y_i,
where M = I − X (X ′X)^{−1}X ′, and λ is the smallest characteristic root of the matrix:

\Big(\begin{bmatrix}y_i\\Y_{i}\end{bmatrix} M_i \begin{bmatrix}y_i&Y_{i}\end{bmatrix} \Big) \Big(\begin{bmatrix}y_i\\Y_{i}\end{bmatrix} M \begin{bmatrix}y_i&Y_{i}\end{bmatrix} \Big)^{\!1}
where, in a similar way, M_{i} = I − X_{i} (X_{i}′X_{i})^{−1}X_{i}′.
In other words, λ is the smallest solution of the generalized eigenvalue problem, see Theil (1971, p. 503):

\Big\begin{bmatrix}y_i&Y_{i}\end{bmatrix}' M_i \begin{bmatrix}y_i&Y_{i}\end{bmatrix} \lambda \begin{bmatrix}y_i&Y_{i}\end{bmatrix}' M \begin{bmatrix}y_i&Y_{i}\end{bmatrix} \Big=0
K class estimators
The LIML is a special case of the Kclass estimators:^{[7]}

\hat\delta = \Big(Z'(I\kappa M)Z\Big)^{\!1}Z'(I\kappa M)y,
with:

\delta = \begin{bmatrix} \beta_i & \gamma_i\end{bmatrix}

Z = \begin{bmatrix} X_i & Y_{i}\end{bmatrix}
Several estimators belong to this class:

κ=0: OLS

κ=1: 2SLS. Note indeed that in this case, I\kappa M = IM= P the usual projection matrix of the 2SLS

κ=λ: LIML

κ=λ  α (nK): Fuller (1977) estimator. Here K represents the number of instruments, n the sample size, and α a positive constant to specify. A value of α=1 will yield an estimator that is approximately unbiased.^{[8]}
Threestage least squares (3SLS)
The threestage least squares estimator was introduced by Zellner & Theil (1962). It combines twostage least squares (2SLS) with seemingly unrelated regressions (SUR).
See also
Notes

^ Greene (2003, p. 398)

^ Greene (2003, p. 399)

^ Greene (2003, p. 399)

^ Park, SB. (1974) "On Indirect Least Squares Estimation of a Simultaneous Equation System", The Canadian Journal of Statistics / La Revue Canadienne de Statistique, 2 (1), 75–82 JSTOR 3314964

^ Vajda, S.; Valko, P.; Godfrey, K.R. (1987). "Direct and indirect least squares methods in continuoustime parameter estimation". Automatica 23 (6): 707–718.

^ Amemiya (1985, p. 235)

^ Davidson & Mackinnon (1993, p. 649)

^ Davidson & Mackinnon (1993, p. 649)
References

Amemiya, Takeshi (1985). Advanced econometrics. Cambridge, Massachusetts: Harvard University Press.

Anderson, T.W.; Rubin, H. (1949). "Estimator of the parameters of a single equation in a complete system of stochastic equations".

Basmann, R.L. (1957). "A generalized classical method of linear estimation of coefficients in a structural equation".

Davidson, Russell; MacKinnon, James G. (1993). Estimation and inference in econometrics. Oxford University Press.


Greene, William H. (2002). Econometric analysis (5th ed.). Prentice Hall.



External links

About.com:economics Online dictionary of economics, entry for ILS

Lecture on the Identification Problem in 2SLS, and Estimation on YouTube by Mark Thoma
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