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Report
Income Inequality and Job Creation
We propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing costs for bank-dependent firms, which in turn create fewer jobs. Exploiting variation across U.S. states and an IV strategy, we provide evidence for the channel. Calibrating a general equilibrium model to our cross-regional estimates, we show that rising inequality increases the employment share of large firms, reduces the ...
Discussion Paper
Does Income Inequality Affect Small Firms?
The share of income going to high-income households has increased significantly in the United States in recent decades. In 1980, the average income share of earners in the top 10 percent was around 30 percent. However, by 2015, it had surpassed 45 percent. The employment share of small firms has also declined, with a decrease of approximately 5 percentage points over the same period. In this post, we use variation across states to show a correlation between these two developments, with states having the greatest increase in the upper income share also tending to be those with the biggest job ...
Report
The New York Fed DSGE Model: A Post-Covid Assessment
We document the real-time forecasting performance for output and inflation of the New York Fed dynamic stochastic general equilibrium (DSGE) model since 2011. We find the DSGE's accuracy to be comparable to that of private forecasters before Covid, but somewhat worse thereafter.
Report
Estimating HANK for Central Banks
We provide a toolkit for efficient online estimation of heterogeneous agent (HA) New Keynesian (NK) models based on Sequential Monte Carlo methods. We use this toolkit to compare the out-of-sample forecasting accuracy of a prominent HANK model, Bayer et al. (2022), to that of the representative agent (RA) NK model of Smets and Wouters (2007, SW). We find that HANK’s accuracy for real activity variables is notably inferior to that of SW. The results for consumption in particular are disappointing since the main difference between RANK and HANK is the replacement of the RA Euler equation with ...
Discussion Paper
The New York Fed DSGE Model Perspective on the Lagged Effect of Monetary Policy
This post uses the New York Fed DSGE model to ask the question: What would have happened to interest rates, output, and inflation had the Federal Reserve been following an average inflation targeting (AIT)-type reaction function since 2021:Q2, when inflation began to rise—as opposed to keeping the federal funds rate at the zero lower bound (ZLB) until March 2022, and then raising it aggressively thereafter? We show that actual policy was more accommodative in 2021 than implied by the AIT reaction function and then more contractionary in 2022 and beyond. On net, the lagged effect of monetary ...
Discussion Paper
A Bayesian VAR Model Perspective on the Lagged Effect of Monetary Policy
Over the last few years, the U.S. economy has experienced unusually high inflation and an unprecedented pace of monetary policy tightening. While inflation has fallen recently, it remains above target, and the economy continues to expand at a robust pace. Does the resilience of the U.S. economy imply that monetary policy has been ineffectual? Or does it reflect that policy acts with “long and variable lags” and so we haven’t yet observed the full effect of the monetary tightening that has already taken place? Using a Bayesian vector autoregressive (BVAR) model, we show that economic ...
Discussion Paper
The New York Fed DSGE Model Forecast— September 2023
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 2023. 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.