How is this plot called? The three plots on the bottom are regular line plots but how are three top subplots called? The picture is taken from p. 45 here: https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp769.pdf?9570e6e4c82cc1b22c891b0d412af1bc

 A: As far as I can tell, all six of these plots are line plots; they show a measure of the efficiency of the MCMC sampler for different subsets of the parameters. The first four (counting right-to-left, top-to-bottom) represent subsets with much larger numbers of parameters, and in some cases (I guess) some kind of periodicity in the meaning of the parameters (e.g. the $\theta_t$s in the top plot form a time series, and probably have a seasonal component), which makes for an interesting visual appearance - but they're not qualitatively different from the last two.
At least in my experience with general MCMCs, this is not a very widespread graphical convention for assessing MCMC chains - maybe more common in econometrics (e.g. also mentioned here). It shows the efficiency of the MCMC - in my opinion (contradicting what the reference says!) this is different from convergence, which would typically be measured by a set of trace plots or a display of the Gelman-Rubin statistics.
Here's what the reference says about the plot:

Following Primiceri (2005), we assess the convergence of the Markov chain by inspecting the autocorrelation properties of the ergodic distribution’s draws. Specifically, in what follows we consider the draws’ inefficiency factors (henceforth, IFs), defined as
  the inverse of the relative numerical efficiency measure of Geweke (1992),
  $$
RNE = (2 \pi)^{-1} \frac{1}{S(0)} \int_{-\pi}^{\pi} S(\omega) \, d\omega
$$
  where S(ω) is the spectral density of the sequence of draws from the Gibbs sampler
  for the quantity of interest at the frequency ω. ...
  Figure 10 shows the draws’ IFs for the models’ hyperparameters–i.e., the free
  elements of the matrices $Q$, $Z$, and $S$ - and for the states, i.e. the time-varying coefficients of the VAR (the $\theta_t$), the volatilities (the $h_{i,t}$ ’s), and the non-zero elements of the matrix $A_t$. As the figure clearly shows, the autocorrelation of the draws is uniformly very low, being in the vast majority of cases around or below 3–as stressed by
  Primiceri (2005, Appendix B), values of the IFs below or around twenty are generally
  regarded as satisfactory.

