Feldstein Lecture Slides

Empirically Evaluating Economic Policy in Real Time John B. Taylor The Martin Feldstein Lecture National Bureau of Economic Research July 10, 2009

The Purpose of Today’s Feldstein Lecture • To evaluate macroeconomic policy during the crisis, focusing on the period since the panic in the fall 2008 by comparing actual policy with counterfactuals: – About monetary policy – About fiscal policy • Each counterfactual is composed of several sub- counterfactuals • “Real time” because the crisis is ongoing

Why the Emphasis on “Real Time”? • The financial crisis has made it essential – You have to evaluate policy quickly to be helpful • Interesting: loads of new data and new policies • Raises new questions about the use of high frequency data and simulation techniques • It really is different

How is it different? • Most macro-money research not done in real time – Taylor (1979): • fixed growth rate rule would work better than actual policy but not as good as optimal policy: 1953-75 – Feldstein and Stock (1997): • evaluated policy rules for M2: 1959-92 – Cecchetti, Flores-Lagunes, Krause (2006): • Has Monetary Policy Become More Efficient? 24 countries: 1982-1998 – Bernanke (2005) • Analysis of Great Moderation: pre and post 1984 • A recent exception – Lars Svensson (2009): • modified Taylor Curve based on forecasts • Evaluates Riksbank policy: February 2009

Figure 1 from Taylor (1979) The Counterfactual s

Figure 1 from Bernanke (2005), “The Great Moderation”

Figure 3 from Svensson (2009) “Evaluating Monetary Policy” Figure 3. The modified Taylor curve

Four Phases of the Crisis • The Root Causes – 2003-2006 – The Great Deviation? • From Flare-up to Panic: Aug 2007- Sept 2008 – The Great Delay? • Panic of Sept 2008 – The Great Scare? • Post Panic Period: Sept 2008- present The Focus of this Lecture

The Panic of Fall ‘08

Actual Monetary Policy Since the Panic of 2008 • During the week of 17 Sept 2008, reserve balances rose, for the first time, above the level required to keep the federal funds rate on target, which was then 2% – Quantitative easing (QE) began before the funds rate hit 0 – Reserves were increased to finance loans, first to investment banks through PDCF, and to purchase securities – Reserves were not increased to accommodate a shift in money demand, or a decline in velocity • Reserves then continued to increase through end of 08 – To finance additional securities purchases and loans – Drove interest rate to zero , which FOMC then ratified – Have remained elevated and are expected to increase

Billions of dollars 1,000 December 31, 2008 $ 848 Billion 800 Reserve Balances of Depository Institutions at Federal Reserve Banks 600 400 200 September 10, 2008 $ 8 Billion 0 2005 2006 2007 2008 2009

0 200 400 600 800 1,000 30 JUL 13 AUG 27 AUG 10 SEP Billions of dollars 24 SEP 08 OCT 22 OCT 05 NOV 19 NOV Weekly Average 03 DEC Wednesday Level 17 DEC 31 DEC 14 JAN 28 JAN 11 FEB 25 FEB 11 MAR 25 MAR 08 APR 22 APR 06 MAY 20 MAY 03 JUN 17 JUN

Billions of dollars Securities Repos 2,400 Loans from TAF Major Factors Supplying Reserves Other loans Private Portfolio CB Liquidity Swap 2,000 Other Assets 1,600 1,200 800 400 17 DEC 31 DEC 28 JAN 14 JAN 08 APR 17 JUN 03 DEC 22 APR 03 JUN 25 MAR 11 MAR 08 OCT 22 OCT 13 AUG 10 SEP 27 AUG 24 SEP 19 NOV 05 NOV 11 FEB 25 FEB 20 MAY 30 JUL 06 MAY

Billions of dollars 900 Private Portfolio 800 Commercial Paper Funding Facility Maiden Lane I (Bear Stearns) 700 Maiden Lane II (AIG) Maiden Lane III (AIG) 600 Mortgage Backed Securities GSE Debt 500 400 300 200 100 0 08 APR 03 DEC 17 DEC 31 DEC 14 JAN 28 JAN 25 MAR 22 APR 03 JUN 17 JUN 11 MAR 08 OCT 22 OCT 13 AUG 27 AUG 24 SEP 10 SEP 06 MAY 30 JUL 05 NOV 19 NOV 11 FEB 25 FEB 20 MAY

billions 0 200 400 600 800 1,000 of dollars 30 Jul 6 Aug 13 Aug 20 Aug 27 Aug 3 Sep 10 Sep 17 Sep 24 Sep (left scale) 1 Oct 8 Oct Reserve Balances 15 Oct (right scale) 22 Oct 29 Oct 5 Nov 12 Nov 19 Nov 26 Nov Effective Federal Funds Rate 3 Dec 10 Dec 17 Dec 24 Dec 31 Dec 0.0 0.5 1.0 1.5 2.0 2.5 percent

Timing of FOMC Decisions: Fall 2008 • 10/8: FOMC votes to cut funds rate to 1.5 % from 2.0 % – But for the 2 weeks ending Oct 8, the funds rate was already well below 2.0 %, averaging 1.45 % • 10/29: FOMC votes to cut funds rate to 1 % from 1.5% – But for the 2 weeks ending Oct 29, the funds rate was already well below 1.5 %, averaging .76 % • 12/16: FOMC votes to cut funds rate to 0-.25% from 1% – But for the 2 weeks ending Dec 17, the rate was already in that range, averaging .14 % • Thus, decisions to increase reserve balances drove down the funds rate rather than the FOMC decisions about the target rate.

Choosing a Counterfactual Monetary Policy • A general counterfactual: When the optimal interest rate (e.g. Taylor rule) hits “zero” (or slightly positive, say .25%) • Trading Desk keeps reserve balances at a level consistent with . 25 % and keeps the growth rate of money from falling – like a CGRR • This is how quantitative monetary models are usually simulated for policy evaluation. (Wieland presentation yesterday) • Avoids Great Depression or Japan Lost Decade mistakes where money growth declined • A more specific counterfactual, closer to actual policy: • MBS = $0 (rather than $500B and rising to $1,200B) • TAF = $0 (rather than as much as $500B),

Billions of dollars 1,000 December 31, 2008 $ 848 Billion 800 Reserve Balances of Depository Institutions at Federal Reserve Banks 600 400 200 September 10, 2008 $ 8 Billion 0 2005 2006 2007 2008 2009

Billions of dollars 1,000 800 Counterfactual Reserve Balances of Depository Institutions at Federal Reserve Banks 600 400 200 September 10, 2008 $ 8 Billion 0 2005 2006 2007 2008 2009

0 200 400 600 800 1,000 30 JUL 13 AUG 27 AUG 10 SEP Billions of dollars 24 SEP 08 OCT 22 OCT 05 NOV 19 NOV Weekly Average 03 DEC Wednesday Level 17 DEC 31 DEC 14 JAN 28 JAN 11 FEB 25 FEB 11 MAR 25 MAR 08 APR 22 APR 06 MAY 20 MAY 03 JUN 17 JUN

0 200 400 600 800 1,000 30 JUL 13 AUG 27 AUG 10 SEP Billions of dollars 24 SEP 08 OCT 22 OCT 05 NOV 19 NOV 03 DEC 17 DEC 31 DEC 14 JAN 28 JAN 11 FEB 25 FEB Counterfactual Weekly Average 11 MAR 25 MAR 08 APR 22 APR 06 MAY 20 MAY 03 JUN 17 JUN

Billions of dollars Securities Repos 2,400 Loans from TAF Major Factors Supplying Reserves Other loans Private Portfolio CB Liquidity Swap 2,000 Other Assets 1,600 1,200 800 400 03 DEC 17 DEC 31 DEC 14 JAN 28 JAN 08 APR 22 APR 03 JUN 17 JUN 11 MAR 25 MAR 08 OCT 22 OCT 13 AUG 27 AUG 10 SEP 24 SEP 25 FEB 20 MAY 05 NOV 19 NOV 11 FEB 06 MAY 30 JUL

Billions of dollars Major Factors Supplying Reserves CF Securities 2,400 Repos Other loans CF Private Portfolio CB Liquidity Swap 2,000 Other Assets 1,600 1,200 800 400 03 DEC 17 DEC 31 DEC 14 JAN 28 JAN 08 APR 22 APR 03 JUN 17 JUN 11 MAR 25 MAR 08 OCT 22 OCT 13 AUG 27 AUG 10 SEP 24 SEP 25 FEB 20 MAY 05 NOV 19 NOV 11 FEB 06 MAY 30 JUL

Billions of dollars 900 Private Portfolio 800 Commercial Paper Funding Facility Maiden Lane I (Bear Stearns) 700 Maiden Lane II (AIG) Maiden Lane III (AIG) 600 Mortgage Backed Securities GSE Debt 500 400 300 200 100 0 03 DEC 17 DEC 31 DEC 28 JAN 08 APR 03 JUN 14 JAN 22 APR 17 JUN 11 MAR 25 MAR 22 OCT 08 OCT 13 AUG 27 AUG 24 SEP 10 SEP 05 NOV 19 NOV 11 FEB 06 MAY 20 MAY 25 FEB 30 JUL

Billions of dollars 900 Counterfactual Private Portfolio 800 Commercial Paper Funding Facility Maiden Lane I (Bear Stearns) 700 Maiden Lane II (AIG) Maiden Lane III (AIG) 600 GSE Debt 500 400 300 200 100 0 03 DEC 17 DEC 31 DEC 08 APR 14 JAN 28 JAN 22 APR 03 JUN 17 JUN 11 MAR 25 MAR 08 OCT 22 OCT 13 AUG 27 AUG 10 SEP 24 SEP 19 NOV 06 MAY 20 MAY 05 NOV 11 FEB 25 FEB 30 JUL

Basis points 300 280 30 year fixed mortgage rate minus 10 year Treasuries 260 240 220 200 180 160 140 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09

Millions of dollars 500,000 400,000 Fed purchases of MBS 300,000 200,000 100,000 0 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09

Basis Points 160 140 Spread: FNMA Bonds - Treasuries 120 100 80 60 40 CDS on FNMA Bonds 20 0 07M01 07M07 08M01 08M07 09M01 09M07

Basis Points 300 30 year fixed mortgage rate 280 minus 10 year Treasuries 260 240 220 200 without MBS purchases 180 with MBS purchases 160 140 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09 Jul 09

Billions of dollars Percent 600 500 Term Auction Facility (right scale) 400 300 5 200 4 100 3 0 2 Libor-repo 1 Lois 0 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09

Millions of Dollars Percent 4.5 4.0 200,000 3.5 Ten Year Treasury Yield (right axis) 150,000 3.0 2.5 100,000 2.0 50,000 Cumulative Fed Purchases of Long Term Treasuries (left axis) 0 Oct 08 Jan 09 Apr 09

What About Fiscal Policy? • What should be the counterfactual? – Only the automatic stabilizers? • No stimulus package in 2008? • No stimulus package in 2009? • Still too early for 2009, but here is a preliminary look…

Here We Go Again? Billions of dollars 11,200 10,800 Disposable personal without stimulus package 10,400 income 10,000 Personal consumption 9,600 expenditures 9,200 Jan 07 Jul 07 Jan 08 Jul 08 Jan 09

What about government purchases? Table 3 from Cogan, Cwik, Taylor, Wieland (2009) Deficit, Federal Government Purchases, and Transfers to State and Local Governments for Purchases of Goods and Services in the February 2009 Stimulus Legislation (billions of dollars) Fiscal Increase Increase Increase Year in Federal in Transfers to in Federal Purchases States, Localities Deficit 2009 21 48 184 2010 47 107 400 2011 46 47 134 2012 36 8 36 2013 25 4 27 1014 27 0 22 2015 11 0 5 2016 -2 0 -8 2017 -3 0 -7 2018 -2 0 -6

New Keynesian Estimate of Impact of Government Purchases G is about as forecast by CCTW in Q1 and Q2

Percent of GDP .8 .6 Government purchases (G) .4 .2 .0 Consumption (C) -.2 Investment (I) C plus I -.4 2009 2010 2011 2012 2013

Conclusion • The financial crisis demonstrates the need for real time policy evaluation. – Fast-moving events, unprecedented policy responses – 24 hr news cycle, blogs, quick spread of 1st idea – Good policy depends on it – Will not end with the crisis • We now can collect and process data rapidly. – No reason to wait 30 years as Friedman/Schwartz • Can do better: – More higher-frequency data (eg. Diebold) – Improved techniques (eg. Svensson) – Better outlets (Journal of Real Time Policy Evaluation)

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Feldstein Lecture Slides

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