Hi Laura,
In MATLAB, there is no specific toolbox or function dedicated to implementing cross-lagged panel models (CLPMs) with random intercepts. However, you can use MATLAB's statistical modeling capabilities and optimization functions to build and estimate such models. What you can do is :
- Start by defining the structural equations that represent the cross-lagged relationships between variables in your model.
- Convert the model equations into an optimization problem that can be solved using MATLAB's optimization functions. This typically involves formulating the problem as a maximum likelihood estimation (MLE) problem.
- Write a MATLAB function that calculates the likelihood of the observed data given the model parameters.
- Use MATLAB's optimization functions, such as 'fmincon' or 'fminunc', to estimate the model parameters by maximizing the likelihood function.
- Assess model fit and interpret results.
Hope it helps!
