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After a period of stability, oil prices started to decline in mid-2015, and this downward trend continued into early 2016. As we noted in an earlier post, it is important to assess whether these price declines reflect demand shocks or supply shocks, since the two types of shocks have different implications for the U.S. economic outlook. In this post, we again use correlations of weekly oil price changes with a broad array of financial variables to quantify the drivers of oil price movements, finding that the decline since mid-2015 is due to a mix of weaker demand and increased supply. Given strong interest in the drivers of oil prices, the oil price decomposition is information we will be sharing in a new Oil Price Dynamics Report on our public website each Monday starting today. We conclude this post using another model that finds that the higher oil supply boosted U.S. economic activity in 2015, though this impact is expected to wear off in 2016.
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 The first official estimate of GDP this quarter will not be published until the end of July.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.
We launched the U.S. Economy in a Snapshot in June 2015 to provide interested readers with a monthly update of current economic and financial developments. Combining charts and summary points, the packet covers a range of topics that include labor and financial markets, the behavior of consumers and firms, survey responses, and the global economy.
When we launched our research blog five years ago this week, we didn’t expect to set any internet traffic records while writing about economics. Still, we saw that a blog would be a good way to build familiarity with our research and policy analysis, and to share the expertise of our staff when it’s relevant to issues in the public eye. As I said back at the birth, our goal was to deliver “lively, clear, and analytically sound” posts and, in that, I think we have succeeded.
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.
Olivier Armantier, Wilbert van der Klaauw, Giorgio Topa, and Basit Zafar
Correction: In the right panel of the chart, “Mean Probability of Deflation in the SCE,” we have corrected the labels for the group earning less than $75k, which were initially transposed. We regret the error.
The expectations of U.S. consumers about inflation have declined to record lows over the past several months. That is the finding of two leading surveys, the Federal Reserve Bank of New York’s Survey of Consumer Expectations (SCE) and the University of Michigan’s Survey of Consumers (SoC). In this post, we examine whether this decline is broad-based or whether it is driven by specific demographic groups.
Marco Del Negro, Marc Giannoni, Pearl Li, Erica Moszkowski, and Micah Smith
We have implemented the FRBNY DSGE model in a free and open-source language called Julia. The code is posted here on GitHub, a public repository hosting service. This effort is the result of a collaboration between New York Fed staff and folks from the QuantEcon project, whose aim is to coordinate development of high performance open-source code for quantitative economic modeling.
Marco Del Negro, Marc Giannoni, Erica Moszkowski, Sara Shahanaghi, and Micah Smith
This post presents 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 first introduced in a series of blog posts in September 2014. The model continues to predict a gradual recovery in economic activity, but one that will proceed at a slightly slower pace than was forecast in our April update. It also predicts a 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.
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