Questions on SMM Estimation

[jpfeifer]

1. @yw3864a

   How do I generally decide which parameters to include in the estimated_params
   for estimation?

   See

How to select parameters for estimation?

There is no hard rule. But it’s quite common to calibrate parameters that i) are not well-identified from the data and ii) where we have a good idea what the parameter value is. An example are the discount factor and the depreciation rate. They are often not well identified from cyclical moments but can often be set to to sensible values based on long-run averages for the investment to capital ratio and the long-run interest rate. The risk aversion parameter is another example. There is good mic…

2. Usually, you pick the moments that you are most interested in. That’s the difference to full information estimation.

3. It depends on whether nonlinearities are important. If higher order does not work, there is often a problem with the scaling of shocks (scaling by 100). Also, often pruning helps.

4. To compute the standard deviations, you need a positive definite Hessian at the mode. Looking at the mode_check pictures, that is not the case in your results, most likely due to a corner solution.

5. If your model is overidentified, the J test ist for testing whether the model is rejected by the data.

6. Enable options_.debug=true; to see where the dots comes from. But given that the autocorrelation coefficients go to 1 (unit root), you most likely have a problem with the observation equations. The mean of the data and the matched model variables most likely differ.