# Misunderstandings

Hi,

1. First of all, supposing I want to estimate a model through bayesian techniques (using the metropolis-hastings algorithm), with observable variables like GDP, consumption, investment, interest rate, inflation, hours worked, wages. If I introduce the equation in Dynare for consumption for example:
C = h/(1+h)*C(-1) + 1/(1+h)C(+1)+(sigma_c - 1)/((1+lambda_w)(1+h)*sigma_c) * (L - L(+1)) - (1-h)/((1+h)*sigma_c) * Rauxil(-1) + (1-h)/((1+h)*sigma_c) *eps_B;

how should my actual data look like? I am confused as the data which I have extracted (quarterly data, seasonally adjusted) is not stationary, presenting an increasing trend and I don’t know if I should stationarize it and afterwards modify also the code in Dynare somehow. And if I should stationarize it, detrending it, how should I do that? And also, what about the interest rate and inflation which are presented as %? Shouldn’t I express all the variables in the model in the same way?

1. About the calibrated parameters: in general, in many articles that I have read, these appear to be standard values. For example, for beta (the discount factor) almost in every cases the calibrated value is 0.99 or for capital depreciation rate is 0.025. It is ok to use the same calibration for different type of economies? And if you state other values, based on what should you do that?

2. I understood that the initval for the observable values should be as close to the ones at the steady state. How can I better aproximate that? Which way do I know that my values are correctly posted in the initval subsection?

3.) Put the `resid(1);` command before steady to see how good your starting values are. All residuals should be as close to 0 as possible. Even better for estimation: use a steady state file. See the respective remark in the linked document.