Monetary policy through the lens of historyThis week Roger is trying to make a penance for the ill-doings of previous blogs on monetary policy. But will he really succeed in walking the straight and narrow? - Read on!
In this blog, and elsewhere, there has been an enormous amount of focus on the foremost monetary policy tool; the Phillips curve, and its shortcomings. Truth be told, I am no great fan of the “calibration contest” surrounding the Phillips curve relationship that has been going on over the past decades. The models being used still put some very strong assumptions of rationality on the agents populating their secluded worlds. From that perspective I am pleased to see that behavioral ideas are finally finding their way into mainstream macro models as well1.
That said, I still think there is something to be had in the interlinkages between a credible inflation targeting regime (IT), labor markets and inflation. And after last week’s feeble effort to explore (or even find for that matter) a long-run Phillips curve I thought we could take a step back and perform a more straightforward exercise in comparing stabilization policy regimes. Hopefully, our conclusions will make us see that despite not yet being as quantifiable and clean without nth-order effects as we would have liked, IT has nonetheless been a boon to the real performance of our economies.
Arguably, this in-the-greater-scheme-of-things view of IT, is also what is currently fought over at and around many central banks. Adherents to a rigidly quantitative, “narrow”2 IT predating the crisis by a few years are battling it out at monetary policy meetings with proponents of a more qualitative, rediscovered, Bayoumi-Bundesbank type of IT, and those wanting to see more universal changes to the stabilization policy framework.
All inflation targeters have improved on previous regimes
First, let’s have a look on how eight major IT-economies have performed in relation to typical stabilization policy objectives; stable inflation and output. Here, I use OECD:s Economic Outlook series of CPI-inflation and unemployment rates (UNR) and I also use the Hodrick-Prescott filter on the unemployment rate to extract possible time-varying trends in UNR:s3. Furthermore, I will use a lambda of 6400 instead of the customary 1600 (for quarterly data) as I am more interested in finding shifts in NAIRU than any high/medium frequency movements in UNR. Current inflation targets are set as definition of price stability throughout the period.
1As an aside, if decision making becomes more and more guided by machine learning techniques, will that imply that our future decisions will become less irrational and more resembling the processes in today’s antiquated models? How’s that for an irony? 2Reading this (NBER 23968) on identification in macroeconomics I cannot help but feel there is also a whiff of non-neutral money hanging over this line of reasoning. 3An advantage with using OECD Economic Outlook data, containing forecasts, is that it relieves some of the end-point problems with the HP-filters.
Table 1: More stable economic developments after an inflation targeting regime was established.Note: In all the charts above, the unemployment rate deviation from NAIRU is measured along the x-axis and inflation deviations from inflation target along the y-axis. The break-points in time constitute the times when the respective inflation target was established in inflation expectations, i.e.; when inflation expectations become aligned to the inflation target (when possible links to sources are available in respective MB-chart).
Admittedly, I have often spoken against the pervasive influence of the Phillips curve on monetary policy decisions. However, and as the scatter charts above make abundantly clear, exploiting that relationship has improved the real performance of all economies dramatically over the past decades. For all countries, the red dots cover a smaller area than the blue dots, which is quite extraordinary given that the red dots also cover the global financial crisis (GFC), which for many/most countries were the worst economic crisis since the 1930’s. Despite the GFC, and the dramatic rise in unemployment rates that followed suit, inflation remained remarkably stable.
Later, and painstakingly slowly to be sure, CB:s have also been able to make steady progress towards their objectives to stabilize both labor markets and inflation.
On a more fundamental note, I know that many commentators maintain that low interest rates were a root cause of the global financial crisis, per se. And while this is certainly possible, it can at least be argued that it was not only (or even mainly) a monetary policy failure. Rather, I think most economists would agree that it was predominantly a regulatory failure.
A simple monetary VAR
There are of course other ways of visualizing the improvements in stabilization policy outcomes. Below, I use the impulse-response functions (IRF:s) of a simple monetary VAR (for the US economy), with inflation (CPI), unemployment rate (UNR), and the real Fed Funds Rate (rFFR)4. In the reduced form Macrobond IRF, the ordering matters why I for the more recent period (from 1994) put UNR first, rFFR second and CPI last. This is hoped to reflect that labor markets react with a lag whereas prices react almost instantly to new economic information (and with the FED somewhere in between). For the earlier period (before 1994) I change the ordering to reflect a more passive FED, putting the real FFR first, UNR second and inflation last. (I never said I wasn’t heavy-handed!).
Be advised that as the Macrobond application cannot yet construct structural VAR:s or orthogonalize the IRF:s we have to be very careful about interpretations (also, the IRF:s are calculated from unit shocks and not in STD:s nor are the variables of similar scales).
4I first performed the calculations outside of Macrobond, but as we have a rudimentary IRF (from a reduced form VAR) in the application I thought we could at least give it a go. And, yes, I too want to see Cholesky in Macrobond (keep them support-tickets coming guys and gals).
||From 1994 onwards|
Table 2: IRF:s for period before (“pre-IT”) and after (“post-IT”) inflation targeting regimes.Note: I have not cut the sample periods at the “great moderation”, which is perhaps more common, but instead at a time when inflation expectations aligned with the FED:s inflation target (around 1994 in the US case). In the MB-document you can see the results and improve on the model yourself. There you can also see the impact on and from shocks to the real FFR.
Generally, and thanks to changing the ordering of the real FFR between the pre- and post-IT periods we get most IRF:s to behave the expected way. For the pre-IT period, a one-unit shock in UNR gradually leads to an even higher unemployment rates that only later, as the real FFR slowly adapts, eventually comes back down. In the post-IT period, monetary policy reacts instantly to incoming information adapting interest rates, why the UNR falls back down in the following quarter(s). Importantly, the effects on inflation from the shock to UNR is stronger and swifter in the pre-IT period than in the post-IT period, suggesting that inflation targeting has had the desired self-calibrating effect on inflation and inflation expectations (underlining the hypothesis of a flatter Phillips curve).
In a similar vein, we can also see that a one-unit shock to inflation has stronger and much more immediate effects on unemployment in the pre-IT than in the post-IT period. However, the difference in effects on inflation is, perhaps, somewhat less intuitive. Sure enough, the inflation shock seems to give rise to a swifter positive effect on inflation in coming quarters in the pre-IT period, but the effects seem larger and more persistent in the post-IT period (which is of course explicable, but maybe not as clear-cut).
On what side is the grass really greenest?
All-in-all, and to conclude, this exercise was not only meant as an ingratiating way to try to make amends with all those that feel sympathy for the current monetary policy set-up (and policy makers). It was more of a reminder to myself to appreciate the immense improvement in stabilization policy outcomes that has been achieved over the past few decades and to take care not to exaggerate the, from a historical perspective, small current deviations from stabilization policy targets. If we argue for more fundamental changes in the stabilization policy set-up, we better make sure our alternative can provide tangible improvements to the current set-up. That is a high bar to pass.
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