Simultaneous Equation Models (let's call them SIM to separate the two types of models), are models where you have some simultaneity. For example,

$$ y=\alpha+\beta x + u_y\\ x=\gamma+\delta y + u_x $$

As you can see, the two equations form a system of equations. These are widely used in econometrics and applied economics, but it is not guaranteed that they have a reasonable (economic) interpretation.

Furthermore, to make things even more complicated, SIMs can be written in both structural and reduced form. So you can speak of a simultaneous equation model in a structural form, without referring to what is traditionally known as structural equation modeling (SEM)! If you want a reference, Econometric Analysis of Cross Section and Panel Data by Wooldridge is pretty good.

In the SEM universe you try to estimate causal relationships and things you cannot observe. For example, IQ is impossible to observe, but you may exploit relationships between related (observable) variables to study it. Factor analysis is a common SEM method.

For applications of SEM on time series, you may want to have a look at dynamic factor analysis.

Simultaneous equation models (let's call them SIM to separate the two types of models), are models where you have some simultaneity. For example,

$$ y=\alpha+\beta x + u_y\\ x=\gamma+\delta y + u_x $$

As you can see, the two equations form a system of equations. These are widely used in econometrics and applied economics, but it is not guaranteed that they have a reasonable (economic) interpretation.

Furthermore, to make things even more complicated, SIMs can be written in both structural and reduced form. So you can speak of a simultaneous equation model in a structural form, without referring to what is traditionally known as structural equation modeling (SEM)! If you want a reference, Econometric Analysis of Cross Section and Panel Data by Wooldridge is pretty good.

In the SEM universe you try to estimate causal relationships and things you cannot observe. For example, IQ is impossible to observe, but you may exploit relationships between related (observable) variables to study it. Factor analysis is a common SEM method.

For applications of SEM on time series, you may want to have a look at dynamic factor analysis.

Simultaneous Equation Models (let's call them SIM to separate the two types of models), are models where you have some simultaneity. For example,

$$ y=\alpha+\beta x + u_y\\ x=\gamma+\delta y + u_x $$

As you can see, the two equations form a system of equations. These are widely used in econometrics and applied economics, but it is not guaranteed that they have a reasonable (economic) interpretation.

Furthermore, to make things even more complicated, SIMs can be written in both structural and reduced form. So you can speak of a simultaneous equation model in a structural form, without referring to what is traditionally known as structural equation modeling (SEM)! If you want a reference, Econometric Analysis of Cross Section and Panel Data by Wooldridge is pretty good.

In the SEM universe you try to estimate causal relationships and things you cannot observe. For example, IQ is impossible to observe, but you may exploit relationships between related (observable) variables to study it. Factor analysis is a common SEM method.

For applications of SEM on time series, you may want to have a look at dynamic factor analysis.

Simultaneous Equation Models (let's call them SIM to separate the two types of models), are models where you have some simultaneity. For example,

$$ y=\alpha+\beta x + u_y\\ x=\gamma+\delta y + u_x $$

As you can see, the two equations form a system of equations. These are widely used in econometrics and applied economics, but it is not guaranteed that they have a reasonable (economic) interpretation.

Furthermore, to make things even more complicated, SIMs can be written in both structural and reduced form. So you can speak of a simultaneous equation model in a structural form, without referring to what is traditionally known as structural equation modeling (SEM)! If you want a reference, Econometric Analysis of Cross Section and Panel Data by Wooldridge is pretty good.

In the SEM universe you try to estimate causal relationships and things you cannot observe. For example, IQ is impossible to observe, but you may exploit relationships between related (observable) variables to study it. Factor analysis is a common SEM method.

For applications of SEM on time series, you may want to have a look at dynamic factor analysis.