I want to calibrate some parameters of my DSGE model so that in the steady state some variable ratios, that are present in data, are met. My question is, how do I get such ratios from time series correctly?
For example, say I want to target the general government expenditure ($g_t$) to GDP ($y_t$) ratio, I get both time series, and then, how do I correctly get a ratio that is valid for targeting from these data? I've thought in three alternatives (not sure if any of them are correct): 1) Just compute the average of the ratio as it is, no matter how the resulting time series ratio behaves (i.e. $mean(g_t/y_t)$). 2) If the ratio is stationary, compute best ARMA (e.g. $(g/y)_t=c+\phi_1 (g/y)_{t-1}$) model and from that estimation use the resulting mean. 3) Test for cointegration between both variables without constant (coint. eq. would be $g_t=\beta_1 y_t$), and if there's cointegration my steady state target would be $\hat \beta_1$.
Is any of that the right approach? Which particular things should I care of?
Thanks!
PD: I've actually tried which results the three approaches yield and all three are very similar, nevertheless I'd like to know which is a "correct" way.