find sensible priors for each of them that would be conceptually similar and could reasonably be expected to apply to Zambia.

Third, there is _little guidance by theory_ in the choice of priors. The theoretical literature is generally not parameterized in a way that suggests any particular amplitudes for the coefficients in our empirical model. To illustrate this, consider Baldini et al. (2012), who develop a DSGE model to analyze monetary policy in Zambia. While conceptually and contextually close to our study, and very transparent about the parameters applied and derived, none of the macroparameters of interest here correspond to those employed in their model (which by construction roots the dynamics in deep parameters at the micro level).

In order to obtain sensible priors, we take the following approach. Instead of collecting or deriving coefficients from earlier literature, we generate our own priors from a more traditional approach ( _as if_ there was previous literature sufficiently consistent with our study). We start by estimating the model using a conventional Cholesky decomposition, thereby obtaining results based on more restrictive assumptions as typically made in the earlier literature. We then feed the resulting estimates as the prior modes into the system described earlier. Thus, we take traditional estimates with all their shortcomings as a _first guess_ , and then use algorithms to improve on them.

Note that – given our structure of choice described above – it is not possible to estimate �� and �� simultaneously. In order to obtain prior modes for both, we estimate the system twice, once as described above, and then reversing the order of the last two variables (exchange rate and inflation). All other parameters are those obtained from the estimation with the order as described throughout the article (exchange rate before inflation). Beyond the mode, in order to complete the specification of our priors, we need to specify the nature of our prior distribution (as the parameters are inherently treated as random variables). In line with Baumeister and Hamilton (2015), we formulate the priors on the parameters in � as student _t_ distributions with three degrees of freedom, and a scale parameter of 0.6 reflecting moderate confidence in our prior modes. Appendix A explicitly reports the prior modes.

# _4. Data_

We use quarterly data from 1995:2 to 2014:4. This corresponds to a period of relative stability in the Zambian economy, and a regime of flexible exchange rates since 1994. Furthermore, we only have data on the output gap starting from 1995, the first quarter of which drops out as we take first differences. We deliberately exclude the tumultuous year 2015 from the main analysis as it exhibits extreme developments that are likely to dominate and distort our baseline estimations.

We obtain the price of copper from the IMF’s Primary Commodity Price database; the Federal funds rate, the nominal USD/ZMW exchange rate (expressed such that a decrease corresponds to devaluation) and the Zambian CPI from the IMF’s International Financial Database; Broad Money (M2) from the Bank of Zambia’s fortnightly statistics; and an internal estimate of the output gap kindly provided to us by the Bank of Zambia. All series are originally obtained at a monthly frequency and averaged over quarters, except for the output gap which was provided to us at quarterly frequency.

Figure 2 plots all variables in levels (solid lines) and in log differences (dashed lines). All series, except for the output gap (especially in early periods) resemble non-stationary series; the differenced series however appear stationary. This is confirmed by Augmented Dickey9