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Look for our next post on November 12, 2024.
Standard metrics point to an improvement in Treasury market liquidity in 2024 to levels last seen before the start of the current monetary policy tightening cycle. Volatility has also trended down, consistent with the improved liquidity. While at least one market functioning metric has worsened in recent months, that measure is an indirect gauge of market liquidity and suggests a level of current functioning that is far better than at the peak seen during the global financial crisis (GFC).
This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since June 2024. As usual, we wish to remind our readers that the DSGE model forecast is not an official New York Fed forecast, but only an input to the Research staff’s overall forecasting process. For more information about the model and variables discussed here, see our DSGE model Q & A.
The rapid rise in Artificial Intelligence (AI) has the potential to dramatically change the labor market, and indeed possibly even the nature of work itself. However, how firms are adjusting their workforces to accommodate this emerging technology is not yet clear. Our August regional business surveys asked manufacturing and service firms special topical questions about their use of AI, and how it is changing their workforces. Most firms that report expected AI use in the next six months plan to retrain their workforces, with far fewer reporting adjustments to planned headcounts.
Economic surveys are very popular these days and for a good reason. They tell us how the folks being surveyed—professional forecasters, households, firm managers—feel about the economy. So, for instance, the New York Fed’s Survey of Consumer Expectations (SCE) website displays an inflation uncertainty measure that tells us households are more uncertain about inflation than they were pre-COVID, but a bit less than they were a few months ago. The Philadelphia Fed’s Survey of Professional Forecasters (SPF) tells us that forecasters believed last May that there was a lower risk of negative 2024 real GDP growth than there was last February. The question addressed in this post is: Does this information actually have any predictive content? Specifically, I will focus on the SPF and ask: When professional forecasters indicate that their uncertainty about future output or inflation is higher, does that mean that output or inflation is actually becoming more uncertain, in the sense that the SPF will have a harder time predicting these variables?
The post-COVID years have not been kind to professional forecasters, whether from the private sector or policy institutions: their forecast errors for both output growth and inflation have increased dramatically relative to pre-COVID (see Figure 1 in this paper). In this two-post series we ask: First, are forecasters aware of their own fallibility? That is, when they provide measures of the uncertainty around their forecasts, are such measures on average in line with the size of the prediction errors they make? Second, can forecasters predict uncertain times? That is, does their own assessment of uncertainty change on par with changes in their forecasting ability? As we will see, the answer to both questions sheds light of whether forecasters are rational. And the answer to both questions is “no” for horizons longer than one year but is perhaps surprisingly “yes” for shorter-run forecasts.
Long-run trends in increased access to credit are thought to improve real activity. However, “rapid” credit expansions do not always end well and have been shown in the academic literature to predict adverse real outcomes such as lower GDP growth and an increased likelihood of crises. Given these financial stability considerations associated with rapid credit expansions, being able to distinguish in real time “good booms” from “bad booms” is of crucial interest for policymakers. While the recent literature has focused on understanding how the composition of borrowers helps distinguish good and bad booms, in this post we investigate how the composition of lending during a credit expansion matters for subsequent real outcomes.
The Federal Reserve Bank of New York’s July 2024 SCE Labor Market Survey shows a year-over-year increase in the average reservation wage—the lowest wage respondents would be willing to accept for a new job—to $81,147, but a decline from a series’ high of $81,822 in March 2024. In this post, we investigate how the recent dynamics of reservation wages differed across individuals and how reservation wages are related to individuals’ expectations about their future labor market movements.
The Federal Reserve (Fed) implements monetary policy in a regime of ample reserves, where short-term interest rates are controlled mainly through the setting of administered rates, and active management of the reserve supply is not required. In yesterday’s post, we proposed a methodology to evaluate the ampleness of reserves in real time based on the slope of the reserve demand curve—the elasticity of the federal (fed) funds rate to reserve shocks. In this post, we propose a suite of complementary indicators of reserve ampleness that, jointly with our elasticity measure, can help policymakers ensure that reserves remain ample as the Fed shrinks its balance sheet.
The Federal Reserve (Fed) implements monetary policy in a regime of ample reserves, whereby short-term interest rates are controlled mainly through the setting of administered rates. To do so, the quantity of reserves in the banking system needs to be large enough that everyday changes in reserves do not cause large variations in the policy rate, the so-called federal funds rate. As the Fed shrinks its balance sheet following the plan laid out by the Federal Open Market Committee (FOMC) in 2022, how can it assess when to stop so that the supply of reserves remains ample? In the first post of a two-part series, based on the methodology developed in our recent Staff Report, we propose to assess the ampleness of reserves in real time by estimating the slope of the reserve demand curve.
Shortly after the collapse of Silicon Valley Bank (SVB) in March 2023, a consortium of eleven large U.S. financial institutions deposited $30 billion into First Republic Bank to bolster its liquidity and assuage panic among uninsured depositors. In the end, however, First Republic Bank did not survive, raising the question of whether a reallocation of liquidity among financial institutions can ever reduce the need for central bank balance sheet expansion in the fight against bank runs. We explore this question in this post, based on a recent working paper.
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