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Jeff Fuhrer Federal Reserve Bank of Boston May 29, 2014.

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Presentation on theme: "Jeff Fuhrer Federal Reserve Bank of Boston May 29, 2014."— Presentation transcript:

1 Jeff Fuhrer Federal Reserve Bank of Boston May 29, 2014

2 This paper Looks at many of the difficult MP issues that CB’s have confronted over the past 5-7 years So it was fun to read (and re-live)! Key topics: I.Does raising π e at the ZLB work? (do i and π e enter “IS” curves with equal magnitude and opposite signs? Does π e enter at all?) II.Is forward guidance a good idea? Effective? How is “anchoring expectations” working? III.Asset purchases—how/do they work? Through term or risk premia? IV.Aggregate supply issues (very briefly) I will comment on pieces of I-III 2

3 I. Nominal or real rates? or how/does expected inflation affect real activity? Great question Hard to answer At the crux: Identification – Carl has a lot of interesting empirical results – I will argue that many of his results (which he doesn’t push too hard) are fraught with identification issues – But I am still quite sympathetic to the hypothesis that nominal rates may be as important as real rates 3

4 ID of any interest rate effects in the R-S model is dicey. Do nominal Rates matter? Does expected inflation matter? 4

5 What should we expect to learn from reduced-form regressions? Consider a simple model (simpler than Iacoviello and Neri) Compute analytical reduced-form coefficients for different values of γ 0 Exactly the same as the regression coefficients implied by the model In general, will not retrieve the IS curve coefficients from these regressions – Will be combination of Phillips, policy rule, etc. 5

6 Analytical reduced-form SE’s depend on shock variances and sample size; here we calibrate to this sample At γ 0 =0, structural coeffs. on (r t, Eπ t+1 ) are (-σ, σ) RF coeffs. on lagged (r, π) are -0.6, Conclusion: Be careful what we infer from reduced- from regressions about what’s going on underneath

7 My DSGE estimates In this sample, id of γ 0 is tough Full- sample estimate (se’s indicated with “+” Hard to reject the classic real- rate version of the model Not conclusive, but an indication of robustness issues Beginning of estimation sample 7 FIML estimates of γ 0, rolling samples Sample size = 100 quarters

8 Bayesian estimates of the simple model Just for completeness—same results 8

9 Estimated parameter distributions 9

10 Why ID is hard: AUTOCORRELATION FUNCTION for Carl’s VAR ALL neg. correls. among Y, r, π Pos. struc. correls. must arise from policy rule, Phillips shocks 10 Need serious identifying assumps. to identify the underlying structure from the raw correls.

11 Conclusion on E(π) issue The DSGE estimates are probably of more interest, as they hold greater promise of identification And they say that – It depends on how you do it – The strongest result you can get is at the 10% confidence level – So it’s not clear that inflation acts differentially from its role in defining the real interest rate So the results aren’t clear What to do? – Examine more detailed micro data, sectorally disaggregated models and data? 11

12 II. Forward guidance: How to think about this? Carl employs a useful framework: Which implies Fed can guide by – Providing information about the outlook (term 3) – Engineering an increase in inflation expectations during the ZLB period (term 1, i = 0 to S) – Changing expectations about the length of the ZLB period (change S) – Reducing expectations about real rates once ZLB is over (term 2) Real rate at ZLB 2. Real rate after ZLB 3. Natural rate (proxy for outlook)

13 Another way to look at this: Why would we ever need forward guidance? If Fed behaves systematically, following a rule, then only a few things can change guidance – Change in the outlook for inflation and output Can an increase in inflation expectations be separated from changes in the outlook? Changes in Fed policy/goals? – Change in the inflation goal – Change in policy preferences, as reflected in α and δ – Change in estimates of the equilibrium funds rate or equilibrium output 13

14 But both are pretty simple frameworks In most normal cases, I agree with Carl that the desire to manipulate expectations may be ill-founded – Better to discuss the outlook, policy preferences, etc.—the inputs to expectations But the world can be a bit more complicated, especially recently How so? – Heightened uncertainty in the wake of the recession About the structure of the economy—supply side, interest rate effects, credit supply effects, QE effects, etc. About the nature and persistence of unusual shocks (financial crisis) About the shape and nature of nonlinearities in the policy “rule” About whether the Fed believes there is value in considering policies that might deviate from the rule, e.g. Woodford – May be that forward guidance helps to clarify the Fed’s views on some of these margins, and also their intentions, which may be difficult to forecast from simple rules in these special circumstances 14

15 Some complications with forward guidance Temporarily higher inflation versus change in long-run target – Hard to communicate the distinction Promises without actions (as Carl points out) – Our forward guidance was always coupled with QE—helpful? Did QE do all the signaling? Only signaling? Distinguishing statements about policy from statements about the outlook ( Classic problem, not unique to current circumstances) – Heightened by perception that the Fed has an information advantage Mitigants – Most often a temporary problem – FOMC forecasts help (although the structure is certainly not perfect) – We have LOTS of other opportunities to clarify our intentions regarding outlook, policy 15

16 On “anchored expectations” 1. Empirics While long-run expectations are remarkably stable, do they anchor actual inflation? Japan: Is the problem that folks don’t believe the Fed will ultimately return inflation to 2%? Probably not. – Expectations are anchored. The question is whether those long- run expectations exert a strong pull on current inflation and short-term expectations – The answer to that question is not clear just yet 16

17 The good and not so good news on that front for the US Long-run (SPF) expectations may ~anchor short-run expectations 17 The anchoring of actual inflation is less obvious, but perhaps it will manifest itself soon.

18 Another way of looking at this Notional 2% line indexed to 1994:Q1 core PCE value Actual Core PCE 6% gap Expectations have been AT LEAST 2% throughout this 20-year period. But we have not attained an average inflation rate of 2% 18 SO: Expectations anchored, but inflation not so much?

19 What do we mean by “anchored expectations?” 2. Theory Which model do we have in mind? First is often used as shorthand, but has little/no theoretical basis (π t =c + ε t ) Second is getting closer to a conventional model Third is a conventional model, but won’t usually have the same implications as first or second model 19 Description of the data Bow toward theory (what happened to Eπ t+1 ?)

20 Carl’s section 3 model His model posits a link between short-run expectations and the CB’s target inflation rate: – This is good as an expository device, but is it a model? – If δ=1, expectations are always equal to π T ? Why would they be? – No theory basis for this kind of expectations formation Rational expectations are already perfectly “anchored to the target” – Double-anchoring? Belt and suspenders? – May be better to use this kind of apparatus for characterizing deviations of trend inflation/long-run expectations from π T ? Practical matter: How much attention do price-setters pay to π T ? → Forward guidance may not be about this kind of anchoring, but about clarifying uncertainties discussed earlier? 20

21 “Anchoring” in an RE model How quickly E t π t+1 →π * – Depends on all key parameters [μ, σ, ρ, a π, a y ] Example: How else can we “anchor” expectations in this sense (i.e. ensure reasonably quick return to π * ?) 21 Rate of convergence as a function of a π

22 III. Identification issues apply to the term/risk premium analysis in section 4 Identification in “forecasting equations” is weak – Makes results hard to interpret That said, it might help to disaggregate further – For example: 10-year Treasury has an effect on mortgage rates (in many CB models and in the real world) – Mortgage rates affect residential investment Perhaps both term spreads and risk spreads matter, once properly identified Look at some evidence, both RF and structural 22

23 Evidence in support of this hypothesis: A disaggregated forecasting regression 23

24 And some more structural estimates of risk versus term spread (still a bit ad hoc) Results favor the term premium Certainly not conclusive, but again, a test of robustness, id, etc rest of DSGE

25 Summary Huge number of topics in this paper They’re all important Key issues – Empirical: Identification of the real/expected inflation rate effects is problematic, so conclusions are murky – Forward guidance: motivations may lie well outside the model, and outside most of historical experience Anchored expectations: What it means theoretically, how to model it require more serious attention – Term/risk premia: ID an issue here, too. More disaggregated work might provide more compelling empirical evidence, as might more structural modeling 25


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