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The current policy debate is influenced by the possibility that the first-quarter GDP data were affected by “residual seasonality.” That is, the statistical procedures used by the Bureau of Economic Analysis (BEA) did not fully smooth out seasonal variation in economic activity. If this is indeed the case, then the weak readings of the economy in the first quarter give an inaccurate picture of the state of the economy. In this post, we argue that unusually adverse winter weather, rather than imperfect seasonal adjustment by the BEA, was an important factor behind the weak first-quarter GDP data.
In February, the Federal Reserve Bank of New York’s trading desk announced it will publish a new overnight bank funding rate early next year. The new rate will be based on both federal funds and Eurodollar transactions reported in a new data collection—the FR 2420 Report of Selected Money Market Rates. In a previous post, we explained how FR 2420 fed funds transaction data will replace brokered data as the base for the fed funds effective rate. This post provides insights on the Eurodollar market in advance of the publication of the overnight bank funding rate.
Marco Del Negro, Marc Giannoni, Matthew Cocci, Sara Shahanaghi, and Micah Smith
Second post in the series
In a recent series of blog posts, the former Chairman of the Federal Reserve System, Ben Bernanke, has asked the question: “Why are interest rates so low?” (See part 1, part 2, and part 3.) He refers, of course, to the fact that the U.S. government is able to borrow at an annualized rate of around 2 percent for ten years, or around 3 percent for thirty years. If you expect that inflation is going to be on average 2 percent over the next ten or thirty years, this implies that the U.S. government can borrow at real rates of interest between 0 and 1 percent at the ten- and thirty-year maturities. This phenomenon is by no means limited to the United States. Governments in Japan and Germany are able to borrow for ten years at nominal rates below 1 percent, and the ten-year yield on Swiss government debt is slightly negative. Why is that?
Marco Del Negro, Marc Giannoni, Matthew Cocci, Sara Shahanaghi, and Micah Smith
First in a two-part series
There are various types of economic forecasts, such as judgmental forecasts or model-based forecasts. In this post, we provide an update of the economic forecasts implied by the Federal Reserve Bank of New York’s (FRBNY) dynamic stochastic general equilibrium (DSGE) model, which we introduced in a series of five blog posts in September 2014 here. It continues to predict a gradual recovery in economic activity with a progressive but slow return of inflation toward the Federal Open Market Committee’s (FOMC) long-run target of 2 percent. This forecast remains surrounded by significant uncertainty. Please note that the DSGE model forecasts are not the official New York Fed staff forecasts, but only an input to the overall forecasting process at the Bank.
Today, the Federal Reserve Bank of New York (FRBNY) is hosting the spring meeting of its Economic Advisory Panel (EAP). As has become custom at this meeting, FRBNY staff are presenting their forecast for U.S. growth, inflation, and unemployment through the end of 2016. Following the presentation, members of the EAP, which consists of leading economists in academia and the private sector, are asked to discuss the staff forecast. Such feedback helps the staff evaluate the assumptions and reasoning underlying the forecast and the key risks to it. Subjecting the staff forecast to periodic evaluation is also important because it informs the staff’s discussions with New York Fed President William Dudley about economic conditions. In that same spirit, we are sharing a short summary of the staff forecast in this post. For more detail, please see the material from the EAP meeting on our website.
Note: A PDF version of this post fully documents the authors’ sources.
Negative interest rates have evolved, over the past few years, from a topic of modest academic interest to a practical reality. Short- and intermediate-term sovereign debt of several European countries, including Germany, Denmark, the Netherlands, Sweden, Austria, and Switzerland, now trades at negative yields.
Mounting evidence says that “low-risk” investing delivers superior returns, comparable to strategies based on value, size, and momentum. Such tactics include the “risk parity” (RP) asset allocation approach, which received considerable attention during the 2013 taper tantrum when many RP funds reportedly deleveraged. This strategy requires long or overweight positions in low-risk asset classes, such as government bonds, and offsetting short or underweight positions in risky asset classes, including shares. The low-risk umbrella also covers “betting against beta” (BAB) within, rather than across, asset classes. For example, investing in shorter- as opposed to longer-duration bonds beats the bond market, or owning low-beta at the expense of high-beta shares outpaces the S&P 500. Whether RP or BAB, what matters is return per unit of risk, the bang for the buck. Put more formally, RP and BAB profitability rests on an inverse relation between Sharpe ratios (SRs) and beta, the covariance of asset returns with the market portfolio. Such findings contradict the intuition that higher returns compensate for risk. Instead, investors profit handsomely by levering up relatively safe assets and shorting comparatively risky securities. However, as my New York Fed staff report argues, alternative reasoning and samples, as well as the types and number of “risks,” raise questions about not only BAB with government bonds (BABgov) but perhaps also RP. The investment implications are obvious, but the arguments and underlying data patterns also hint at key policy issues.
On April 1, 2014, the Federal Reserve began collecting transaction-level data on federal funds, Eurodollars, and certificates of deposits from a large set of domestic banks and agencies of foreign banks operating in the United States. Previously, the Fed had only received fed funds and Eurodollar data from major brokers, and not directly from the banks borrowing in these markets. These new data, collected on form FR 2420, have helped the Fed better understand activity in the fed funds and Eurodollar markets. In this post, we focus on the new data on fed funds, in light of the Federal Reserve Bank of New York’s Trading Desk announcement that it plans to use these data to calculate and publish the fed funds effective rate. We plan to publish other posts on the fed funds and Eurodollar markets over the next several months.
The monetary base in the United States, defined as currency plus bank reserves, grew from about $800 billion in 2008 to $2 trillion in 2012, and to roughly $4 trillion at the end of 2014 (see chart below). Some commentators have viewed this increase in the monetary base as a sure harbinger of inflation. For example, one economist wrote that this “unprecedented expansion of the money supply could make the '70s look benign.” These predictions of inflation rest on the monetarist argument that nominal income is proportional to the money supply. The fact that the money supply has expanded rapidly while real income has grown very modestly means that sooner or later prices will have to catch up. Most academic economists (from Cochrane to Krugman and Mankiw) disagree. The monetarist argument arguably applies only to non-interest-bearing central bank liabilities, but since October 2008 a large fraction of the monetary base has consisted of reserves that pay interest (the so-called IOER, or interest on excess reserves) and one linchpin of the Fed’s “policy normalization principles” consists precisely in raising the IOER along with the federal funds rate. Since reserves pay close to market interest rates, they are close substitutes for other short-term assets such as Treasury bills from a bank’s perspective. As long as the central bank can affect the return on these short-term assets by adjusting the IOER, controlling inflation with a large balance sheet seems no different than it was before the Great Recession.
Marco Del Negro, Raiden Hasegawa, and Frank Schorfheide
Second in a two-part series
As an economist, you make policy recommendations at any point in time that depend on what model of the economy you have in mind and on your assessment of the state of the economy. One can see these points play out in the current discussion about the timing of interest rate liftoff and the speed of the subsequent renormalization. If you think nominal rigidities are not all that important, you are likely to conclude that accommodative policies won’t do much for growth but will generate inflation. Similarly, if you are convinced that the economy is already firing on all cylinders, you may see little need for prolonged accommodation. The problem is, you are not quite sure about the state of the economy or what the right model is. If you are a Bayesian, you may want to try to put probabilities on different models/states of the world and take it from there. The first post in this series, “Combining Models for Forecasting and Policy Analysis,” introduced a procedure called dynamic pools that shows how to do just that. In this post, we apply that procedure to a policy exercise. We can’t publicly discuss current policies, so we will instead apply our method to consider alternative monetary policies at the onset of the Great Recession.
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