Under the estimation block, there are a ton of options and I have questions on a couple.

My first question relates to the “loglinear” command. The default is for Dynare to linearize the model and the data, but what happens if your model and data are already log-linearized? Does Dynare’s linearization affect the model/data specification at all, and if so is there a way to turn this off?

mh_replic, mh_nblocks and mh_drop: In my model, if I set mh_replic to 60,000, mh_nblocks to 15, and mh_drop to .3, Dynare accepts 13 of the blocks, and then uses blocks 4 through 13, before accepting 42,000 replications. How does this work? That is, what are the conditions under which it accepts blocks, and then does it draw only the best replications from each block to get the (60,000 - (60,000*.3))=42,000 best replications? Or does it use each block that it accepted as a unique, separate set of data to increase the “observational” size of the estimation?

As a side-note, should I be worried if the model says there are not enough replications to compute the posterior densities (I think that’s what it said? I’ll double check later, running another estimation right now and it takes about four and a half hours given the above MH conditions). I’m considering increasing the number of replications to 100,000 and leaving mh_drop at .3, or is this a situation in which I would need more actual observations? I have 52 data points across five variables for estimation right now.

Finally, can high volatility or structural breaks in a supposedly log-linearized data series cause problems with the estimation? One of my series is oil prices (pr_O) in domestic currency, and the (deviations from steady state - steady state calculated as the average over the time period) series causes all kinds of problems once I try to incorporate it into and otherwise-working estimation. The error it produces if oil prices are included (given the above estimation conditions) is “Estimation::marginal density: There’s probably a problem with the modified harmonic mean estimator.”

Relevant files are attached.

varobs2.m (5.49 KB)

EstimationTest2.mod (6.84 KB)