My question(s) may sound rather naïve for seasoned DSGE practitioners. If I have a set of parameters [p1,p2,p3,…pk] How do I know which parameters can be estimated and which can be calibrated (Model Implicit way)? Given, for example, that all parameters can be calibrated implicitly (You could also assume that some might not like Elasticities). If you can, can you give me an example when you decide which parameters are to be estimated? Is data part of the solution to the decision rule? How is data taken into account for estimation?
Best,
Rodrigo
Usually in DSGE estimation researchers do not estimate total parameters and calibrate some parameters such as discount factor , depreciation rate, share of capital in production function and other similar parameters and estimate other parameters.
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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 micro evidence to set its value.
In contrast, we have no good idea how the TFP process looks like. That’s a good reason to estimate it.
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Thank you, I did not think of it in terms of identification.