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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.
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 The first official estimate of GDP this quarter will not be published until the end of July.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.
Variousnewsreports have asserted that the slowdown in China was a key factor driving down commodity prices in 2015. It is true that China’s growth eased last year and, owing to its manufacturing-intensive economy, that slackening could reasonably have had repercussions for commodity prices. Still, growth in Japan and Europe accelerated in 2015, with the net result that global growth was fairly steady last year, casting doubt on the China slowdown explanation. An alternative story relies on the strong correlation between the dollar and commodity prices over time. A simple regression shows that both global growth and the dollar track commodity prices, and in this framework, it is the rise of the dollar that captures last year’s drop in commodity prices. Thus a forecast of stable global growth and a relatively unchanged dollar suggests little change in commodity prices in 2016.
Everyone disagrees, even professional forecasters, especially about big economic questions. Has potential output growth changed since the financial crisis? Are we bound for a period of “secular stagnation”? Will the European economy rebound? When is inflation getting back to mandate-consistent level? In this post, we document to what degree professional forecasters disagree and discuss potential reasons why.
In March, the Federal Reserve and thirty-one large bank holding companies (BHCs) disclosed their annual Dodd-Frank Act stress test (DFAST) results. This is the third year in which both the BHCs and the Fed have published their projections. In a previous post, we looked at whether the Fed’s and the BHCs’ stress test results are converging in the aggregate and found mixed results. In this post, we look at stress test projections made by individual BHCs. If the Fed’s projections are very different from a BHC’s in one year, do the BHC projections change in the following year to close this gap? Or are year-to-year changes in BHC stress test projections driven more by changes in underlying risk factors? Evidence of BHCs mimicking the Fed would be problematic if it meant that the BHCs are not really independently modelling their own risks. Convergence poses a potential risk to the financial system, since a financial system with monoculture in risk measurement models could be less stable than one in which firms use diverse models that collectively might be more likely to identify emerging risks.
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.
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.
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.
Liberty Street Economics features insight and analysis from economists working at the intersection of research and policy. The editors are Michael Fleming, Andrew Haughwout, Thomas Klitgaard, and Donald Morgan.
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