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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.
The Federal Reserve has kept interest rates at historic lows for the last six years, but eventually rates will return to their long-term averages. That means both policymakers and the public will once again be asking one of the classic questions in monetary economics: What are the impacts of rising interest rates on the real economy? Our recent New York Fed staff report “Interest Rates and the Market for New Light Vehicles,” considers this question for the U.S. market for new cars and light trucks. We find strong evidence that rising rates will dampen activity: Our model predicts that in the short-run a 100-basis-point increase in interest rates will cause light vehicle production to fall at an annual rate of 12 percent and sales to fall at an annual rate of 3.25 percent.
The dollar rose sharply against both the euro and yen in 2014 and 2015 and non-oil import prices subsequently fell. An explanation for this relationship is that a stronger dollar reduces the dollar-denominated cost of producing something in Germany or Japan, giving firms room to lower their dollar prices in order to gain sales against their U.S. competitors. A breakdown by type of good, however, shows that import prices for autos, consumer goods, and capital goods tend not to move much with changes in the dollar as foreign firms choose to keep the prices of their goods stable in the U.S. market. Instead, the connection between import prices and the dollar largely reflects the tendency for commodity prices to fall in dollar terms when the dollar strengthens. As a consequence, the dampening effect of a stronger dollar on U.S. inflation is transmitted much more through falling commodity prices than through cheaper imported cars and consumer goods.
Income, or wealth, inequality is not something that central bankers generally worry about when setting monetary policy, the goals of which are to maintain price stability and promote full employment. Nevertheless, it is important to understand whether and how monetary policy affects inequality, and this topic has recently generated quite a bit of discussion and academic research, with some arguing that the Federal Reserve’s expansionary policy of recent years has exacerbated inequality (see, for instance, here or here), while others reach the opposite conclusion (see here or here). This disagreement can be attributed in part to the different channels through which expansionary monetary policy can affect inequality: its effect on asset prices would tend to increase inequality, while its effect on labor incomes and employment would likely decrease inequality. In this post, I study one particular channel through which Fed policies may have disparate effects—namely, mortgage refinancing—and I focus on dispersion across locations in the United States.
Update (12.9.15): We revised the chart package linked to in the second paragraph of this post to correct a spreadsheet error. A new note also clarifies our methodology. Please see the addendum below.
We know that different people experience different inflation rates because the bundle of goods and services that they consume is different from that of the “typical” household. This phenomenon is discussed in this publication from the Bureau of Labor Statistics (BLS), and this article from the New York Fed. But did you know that there are substantial differences in inflation experience depending on the level of one's housing costs? In this post, which is based upon our updated staff report on “The Measurement of Rent Inflation,” we present evidence that price changes for rent, which comprises a large share of consumer spending, can vary considerably across households. In particular, we show that rent inflation is consistently higher for lower-cost housing units than it is for higher-cost units. Note that since owners' equivalent rent inflation is estimated from observed changes in rent of rental units, this finding applies to homeowners as well. While we cannot be certain about why this is the case, it appears to be at least partly related to how additional units are supplied to the housing market: in higher-price segments additional units primarily come from new construction, while most of the increase in lower-price segments comes from units that previously were occupied by higher-income households.
The risk of becoming unemployed varies substantially across different groups within the labor market. Although the “headline” unemployment rate draws the most attention from the news media and policymakers, there is rich heterogeneity underlying this overall measure. We delve into the data to describe how unemployment and job loss risk vary with demographics (gender, age, and race), skill (educational attainment), and job characteristics (occupation and earnings).
How much someone earns is an important determinant of many significant decisions over the course of a lifetime. Therefore, understanding how and why earnings are dispersed across individuals is central to understanding dispersion in a wide range of areas such as durable and non-durable consumption expenditures, debt, hours worked, and even health. Drawing on a recent New York Fed staff report "What Do Data on Millions of U.S. Workers Reveal about Life-Cycle Earnings Risks?", this blog post investigates the nature of earnings inequality over a lifetime. It finds that earnings are subject to significant downside risk and that such risk contributes substantially to overall earnings dispersion.
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