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The New York Fed engages with individuals, households and businesses in the Second District and maintains an active dialogue in the region. The Bank gathers and shares regional economic intelligence to inform our community and policy makers, and promotes sound financial and economic decisions through community development and education programs.
Credit conditions tightened considerably in the second half of 2015 and U.S. growth slowed. We estimate the extent to which tighter credit conditions last year were responsible for the slowdown using the FRBNY DSGE model. We find that growth would have slowed substantially more had the Federal Reserve not delayed liftoff in the federal funds rate.
Stefano Eusepi, Erica Moszkowski, and Argia Sbordone
The May 2016 forecast of the Federal Reserve Bank of New York’s (FRBNY) dynamic stochastic general equilibrium (DSGE) model remains broadly in line with those of our two previous semiannual reports (see our May 2015 and December 2015 posts). In the past year, the headwinds that contributed to slower growth in the aftermath of the financial crisis finally began to abate. However, the widening of credit spreads associated with swings in financial markets in the second half of 2015 and the first few months of this year have had a negative impact on economic activity. Despite this setback, the model expects a rebound in growth in the second half of the year, so that the medium-term forecast remains, as in the December post, one of steady, gradual economic expansion. The model also continues to predict gradual progress in the inflation rate toward the Federal Open Market Committee’s (FOMC) long-run target of 2 percent.
Monitoring the economic and financial landscape is a difficult task. Part of the challenge stems from simply having access to data. Even if this requirement is met, there is the issue of identifying the key economic data releases and financial variables to focus on among the vast number of available series. It is also critical to be able to interpret movements in the data and to know their implications for the economy. Since last June, New York Fed research economists have been helping on this front, by producing U.S. Economy in a Snapshot, a series of charts and commentary capturing important economic and financial developments. At today’s Economic Press Briefing, we took reporters covering the Federal Reserve through the story of how and why the Snapshot is produced, and how it can be helpful in understanding the U.S. economy.
In recent years, policymakers in advanced and emerging economies have employed a variety of macroprudential policy tools—targeted rules or requirements that enhance the stability of the financial system as a whole by addressing the interconnectedness of individual financial institutions and their common exposure to economic risk factors. To examine the foreign experience with these tools, we constructed a novel macroprudential policy (MAPP) index. This index allows us to quantify the effects of these policies on bank credit and house prices, two variables that are often the target of policymakers because of their links to boom-bust leverage cycles. We then used the index in the empirical analysis to measure the effectiveness of these policies in emerging market countries and advanced economies. Our estimates suggest that macroprudential tightening can significantly reduce credit growth and house price appreciation.
In the late 1800s, a surge in silver production made a shift toward a monetary standard based on gold and silver rather than gold alone increasingly attractive to debtors seeking relief through higher prices. The U.S. government made a tentative step in this direction with the Sherman Silver Purchase Act, an 1890 law requiring the Treasury to significantly increase its purchases of silver. Concern about the United States abandoning the gold standard, however, drove up the demand for gold, which drained the Treasury’s holdings and created strains on the financial system’s liquidity. News in April 1893 that the government was running low on gold was followed by the Panic in May and a severe depression involving widespread commercial and bank failures.
Today, the Federal Reserve Bank of New York (FRBNY) is hosting the spring meeting of its Economic Advisory Panel (EAP). As has become the custom at this meeting, the FRBNY staff is presenting its forecast for U.S. growth, inflation, and the unemployment rate. Following the presentation, members of the EAP, which consists of leading economists in academia and the private sector, are asked to critique the staff forecast. Such feedback helps the staff evaluate the assumptions and reasoning underlying its forecast as well as the forecast’s key risks. The feedback is also an important part of the forecasting process 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, see the FRBNY Staff Outlook Presentation from the EAP meeting on our website.
Grant Aarons, Daniele Caratelli, Matthew Cocci, Domenico Giannone, Argia Sbordone, and Andrea Tambalotti
What is the weather today? You don’t need to be a meteorologist to answer this question. Just take a look outside the window. Macroeconomists do not have this luxury. The first official estimate of GDP this quarter will not be published until the end of July. In fact, we don’t even know what GDP was last quarter yet! But while we wait for these crucial data, we float in a sea of information on all aspects of the economy: employment, production, sales, inventories, you name it. . . . Processing this information to figure out if it is rainy or sunny out there in the economy is the bread and butter of economists on trading desks, at central banks, and in the media. Thankfully, recent advances in computational and statistical methods have led to the development of automated real-time solutions to this challenging big data problem, with an approach commonly referred to as nowcasting. This post describes how we apply these techniques here at the New York Fed to produce the FRBNY Nowcast, and what we can learn from it. It also serves as an introduction to our Nowcasting Report, which we will update weekly on our website starting this Friday, April 15.
Bonni Brodsky, Marco Del Negro, Joseph Fiorica, Eric LeSueur, Ari Morse, and Anthony Rodrigues
In our previous post, we showed that the gap between the market-implied path for the federal funds rate and the survey-implied mean expectations for the federal funds rate from the Survey of Primary Dealers (SPD) and the Survey of Market Participants (SMP) narrowed from the December survey to the January survey. In particular, we provided explanations for this narrowing as well as for the subsequent widening from January to March. This post continues the discussion by presenting a novel approach called “tilting” that yields insights by measuring how much the survey probability distributions have to be altered to match the market-implied path of the federal funds rate. We interpret any discrepancy between the original and tilted distributions as arising from either risk premia or dispersion in beliefs.
Bonni Brodsky, Marco Del Negro, Joseph Fiorica, Eric LeSueur, Ari Morse, and Anthony Rodrigues
Over the past year, market pricing on interest rate derivatives linked to the federal funds rate has suggested a significantly lower expected path of the policy rate than responses to the New York Fed’s Survey of Primary Dealers (SPD) and Survey of Market Participants (SMP). However, this gap narrowed considerably from December 2015 to January 2016, before widening slightly at longer horizons in March. This post argues that the narrowing between December and January was mostly the result of survey respondents placing greater weight on lower rate outcomes, while the subsequent widening between January and March likely reflects an increased demand for insurance against states of the world where the policy rate remains at very low levels.
Correction: In the original version of this post, the chart “Average Daily Fedwire Payments Are Higher at Quarter-End” contained incorrect data. The chart has now been updated. We regret the error.
The federal funds market is an important source of short-term funding for U.S. banks. In this market, banks borrow reserves on an unsecured basis from other banks and from government-sponsored enterprises, typically overnight. Before the financial crisis, the Federal Reserve implemented monetary policy by targeting the overnight fed funds rate and then adjusting the supply of bank reserves every day to keep the rate close to the target. Before the crisis, reserves were generally in scarce supply, which periodically caused temporary spikes in the fed funds rate during times of high demand, typically at the end of each quarter. In this post, we show that the Fed actively responded to quarter-end volatility by injecting reserves into the banking system around these dates.
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