According to the commonly held view, quantitative easing (QE) often is seen as a substitute for conventional monetary policy, if the short-term interest rate is constrained by the zero lower bound. Yet despite its prominent role, the macroeconomic effects of QE – in particular, its impact on output, inflation, and aggregate investment – remain the subject of an open debate. The empirical evidence that aims to answer this question is, by and large, limited to evidence from vector autoregression models. While some studies developed structural models to study the effects of QE (most notably, Gertler and Karadi 2013, Chen et al. 2012, and Carlstrom et al. 2017), none of the studies presents estimates over the relevant sample period due to the econometric challenges posed by the zero-lower bound (which requires a nonlinear estimation). As such, a structural investigation of quantitative easing is absent.
In a new paper (Boehl et al. 2020), we estimate a large-scale dynamic general equilibrium (DSGE) model with several financial frictions to gauge the effects of quantitative easing in the US. In contrast to earlier studies, our nonlinear Bayesian likelihood approach (based on Boehl (2020)) allows us to include the zero lower bound with data on the Federal Reserve’s balance sheet into the estimation process. We distinguish between three policy measures taken by the Federal Reserve, namely emergency liquidity provisions, treasury purchases, and private security purchases (illustrated in Figure 1). Therefore, our approach enables us to take a far more in-depth account of the macroeconomic effects of these programmes.
Figure 1 The Fed’s balance sheet expansion (% of GDP)
Note: Liquidity injections are defined as the sum of central bank liquidity swaps (#WACBS), the federal agency debt securities held by the Federal Reserve (#FEDDT), the term auction credit held by the Federal Reserve (#TERAUCT) and other loans held by the Federal Reserve (#OTHLT), divided by nominal GDP (#GDP). Corporate bonds are defined as the sum of the current face value of mortgage-backed obligations held by Federal Reserve Banks (#WSHOMCB) and the net portfolio holdings of the Commercial Paper Funding Facility LLC (#WCPFF), again divided by nominal GDP. Finally, Government bonds represents the U.S. Treasury securities held by the Federal Reserve (#TREAST) over GDP. St Louis FRED mnemonics in parenthesis.
QE lifting investment, but failing to generate inflation
Based on counterfactual simulations of our estimated model, quantitative easing raised output by about 1.2% between 2009 to 2015. According to our results, this reflects a net increase in investment of nearly 9%, which was accompanied by a 0.7% drop in aggregate consumption. Figures 2 and 3 illustrate these results using counterfactual simulations of key macroeconomic series in the absence of the unconventional measures.
Figure 2 Counterfactual simulations without unconventional measures
Notes: Counterfactual simulations without the policy measures on the left. Net contribution of each measure on the right. Effects in both graphs are cumulative. Means over 1,000 simulations drawn from the posterior. Annualised measures where applicable.
Figure 3 Counterfactual simulations without unconventional measures
Notes: Counterfactual simulations without the policy measures on the left. Net contribution of each measure on the right. Effects in both graphs are cumulative. Means over 1,000 simulations drawn from the posterior. Annualised measures where applicable.
Both government bond and private security purchases were effective in improving borrowing conditions for households and firms. Particularly, purchases of private capital securities significantly facilitated new investment. On the other hand, the Federal Reserve’s emergency liquidity provisions at the onset of the global financial crisis had negligible macroeconomic effects, despite sharply lowering the credit spread by around 100 basis points. A key reason for this finding lies in the short-lived nature of the latter measures.
The QE-induced rise in investment increased the production capacity. This led to a mild disinflationary effect of about 0.25% annually, which contrasts with earlier studies on quantitative easing (e.g. Chen et al. 2012, Gertler and Karadi 2013, Carlstrom et al. 2017).
Understanding the disinflationary effect of QE
Our result of a disinflationary effect of quantitative easing is well-aligned with the recent findings found in existing research on the effects of financial shocks. Specifically, expansionary financial shocks can be disinflationary, if supply effects dominate demand effects. Quantitative easing, in turn, can be interpreted as such an expansionary financial shock, which substantially lowers long-term interest rates. The resulting surge in investment raises the capital stock in our model. Facing a higher production capacity, firms lower the degree of capital utilisation which pushes down the associated marginal costs. This mechanism resembles a channel identified by Acharya (2019), who find that cheap credit to impaired firms in the euro area had a disinflationary effect by creating excess production capacity. Against this backdrop, our results suggest that aggregate supply channels dominated in determining the response of inflation to large-scale asset purchase programmes.
Implications for monetary policy
Our finding of a disinflationary effect of QE is highly policy relevant. In the US, large-scale asset purchasing programmes create a trade-off for policymakers between stabilising prices and output. For the euro area, the result may be even more pressing as its asset purchase programme was explicitly undertaken with the goal of stabilising inflation and inflation expectations at a time when fears of deflation surged.
Authors' note: The views presented are those of the authors and do not reflect the official position of De Nederlandsche Bank, the Deutsche Bundesbank or the Eurosystem.
References
Abbate, A, S Eickmeier and E Prieto (2016), “Financial shocks and inflation dynamics”, Discussion Papers 41/2016, Deutsche Bundesbank.
Acharya, V V (2019), “Creating zombies and disinflation: A cul de sac for accommodative monetary policy”, VoxEU.org, 11 November.
Baumeister, C and L Benati (2013), “Unconventional monetary policy and the Great Recession: Estimating the macroeconomic effects of a spread compression at the zero lower bound”, International Journal of Central Banking 9(2): 165-212.
Bernanke, B S (2020), “The new tools of monetary policy”, American Economic Review 110(4): 943-983.
Boeckx, J, M Dossche and G Peersman (2017), “Effectiveness and transmission of the ECB's balance sheet policies”, International Journal of Central Banking 13(1): 297-333.
Boehl, G (2020), “Efficient solution, filtering and estimation of models with OBCs”, unpublished manuscript.
Boehl, G and P Lieberknecht (2020), “The hockey stick Phillips curve and the zero lower bound”, unpublished manuscript.
Boehl, G, G Goy and F Strobel (2020), “A structural investigation of Quantitative Easing”, De Nederlandsche Bank Working Paper No. 691.
Carlstrom, C T, T S Fuerst and M Paustian (2017), “Targeting long rates in a model with segmented markets”, American Economic Journal: Macroeconomics 9(1): 205-242.
Chen, H, V Curdia and A Ferrero (2012), “The macroeconomic effects of large-scale asset purchase programmes”, The Economic Journal 122(564): F289-F315.
Debortoli, D, J Galí and L Gambetti (2019), “On the empirical (ir)relevance of the zero lower bound constraint”, NBER Working Paper 25820.
Doniger, C, J Hebden, L Pettit and A Skaperdas (2019), “Substitutability of monetary policy instruments”, FED Notes (2019-07), 17.
Gambacorta, L, B Hofmann and G Peersman (2014), “The effectiveness of unconventional monetary policy at the zero lower bound: A cross-country analysis”, Journal of Money, Credit and Banking 46(4): 615-642.
Gertler, M and P Karadi (2013), “QE 1 vs. 2 vs. 3. . . : A framework for analyzing large-scale asset purchases as a monetary policy tool”, International Journal of Central Banking 9(1): 5-53.
Gilchrist, S, R Schoenle, J Sim and E Zakrajsek (2017), “Inflation dynamics during the financial crisis”, American Economic Review 107(3): 785-823
Hamilton, J D and J C Wu (2012), “The effectiveness of alternative monetary policy tools in a zero lower bound environment”, Journal of Money, Credit and Banking 44: 3-46.
Kapetanios, G, H Mumtaz, I Stevens and K Theodoridis (2012), “Assessing the economy-wide effects of quantitative easing”, The Economic Journal 122(564): F316-F347.
Kiley, M T (2018), “Quantitative easing and the `new normal' in monetary policy”, The Manchester School 86: 21-49.
Sims, E and J C Wu (2020), “Evaluating central banks' tool kit: Past, present, and future”, Journal of Monetary Economics (forthcoming).
Weale, M and T Wieladek (2016), “What are the macroeconomic effects of asset purchases?”, Journal of Monetary Economics 79: 81-93.
Endnotes
1 See Hamilton and Wu (2012), Gertler and Karadi (2013), Kiley (2018), Doniger et al. (2019), Bernanke (2020), Debortoli et al. (2019) and Sims and Wu (2020), to name a few.
2 Including, for example, Kapetanios et al (2012), Baumeister (2013), Gambacorta et al. (2014), Weale and Wieladek (2016), Boeckx et al. (2017).
3 Consistent with prevailing supply effects, Abbate et al. (2016) show that financial shocks that lower firms' funding costs and increase credit growth and stock prices indeed reduce inflation in the short run. Similarly, but using granular micro-data, Gilchrist et al. (2017) show that firms with binding liquidity constraints increased prices during the global financial crisis, while unconstrained firms lowered them. Relatedly, Boehl and Lieberknecht (2020) show that a binding zero lower bound constraint can amplify the inflationary tendency of contractionary financial shocks.
