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Bank:Federal Reserve Bank of Cleveland  Series:Working Papers 

Working Paper
Raising the Inflation Target: How Much Extra Room Does It Really Give?

Some, but less than intended. The reason is a shift in the behavior of the private sector: Prices adjust more frequently, lowering the potency of monetary policy. We quantitatively investigate this channel across different models, based on a calibration using micro data. By raising the target from 2 percent to 4 percent, the monetary authority gets only between 0.51 and 1.60 percentage points of effective extra policy room for monetary policy (not 2 percentage points as intended). Getting 2 percentage points of effective extra room requires raising the target to more than 4 percent. Taking ...
Working Papers , Paper 20-16

Working Paper
The Politics of Flat Taxes

We study the determination of flat tax systems using a workhorse macroeconomic model of inequality. Our first result is that, despite the multidimensional policy space, equilibrium policies are typically unique (up to a fine grid numerical approximation). The majority voting outcome features (i) zero labor income taxation, (ii) simultaneous use of capital income and consumption taxation, and (iii) generally low transfers. We discuss the role of three factors?the initial heterogeneity in sources of income, the mobility of income and wealth, and the forward-looking aspect of voting?in ...
Working Papers , Paper 14-42R

Working Paper
Average Inflation Targeting: Time Inconsistency And Intentional Ambiguity

We study the implications of the Fed's new policy framework of average inflation targeting (AIT) and its ambiguous communication. The central bank has the incentive to deviate from its announced AIT and implement inflation targeting ex post to maximize social welfare. We show two motives for ambiguous communication about the horizon over which the central bank averages inflation as a result of time inconsistency. First, it is optimal for the central bank to announce different horizons depending on the state of the economy. Second, ambiguous communication helps the central bank gain ...
Working Papers , Paper 21-19R

Working Paper
Forecasting US Inflation Using Bayesian Nonparametric Models

The relationship between inflation and predictors such as unemployment is potentially nonlinear with a strength that varies over time, and prediction errors error may be subject to large, asymmetric shocks. Inspired by these concerns, we develop a model for inflation forecasting that is nonparametric both in the conditional mean and in the error using Gaussian and Dirichlet processes, respectively. We discuss how both these features may be important in producing accurate forecasts of inflation. In a forecasting exercise involving CPI inflation, we find that our approach has substantial ...
Working Papers , Paper 22-05

Working Paper
Applications of Markov Chain Approximation Methods to Optimal Control Problems in Economics

In this paper we explore some of the benefits of using the finite-state Markov chain approximation (MCA) method of Kushner and Dupuis (2001) to solve continuous-time optimal control problems. We first show that the implicit finite-difference scheme of Achdou et al. (2017) amounts to a limiting form of the MCA method for a certain choice of approximating chains and policy function iteration for the resulting system of equations. We then illustrate the benefits of departing from policy function iteration by showing that using variations of modified policy function iteration to solve income ...
Working Papers , Paper 21-04

Working Paper
Minimum Wage Increases and Vacancies

We estimate the impact of minimum-wage increases on the quantity of labor demanded as measured by firms’ vacancy postings. We use propriety, county-level vacancy data from the Conference Board’s Help Wanted Online database. Our identification relies on the disproportionate effects of minimum-wage hikes on different occupations, as the wage distribution around the binding minimum wage differs by occupation. We find that minimum-wage increases during the 2005-2018 period have led to substantial declines in vacancy postings in at-risk occupations, occupations with a larger share of ...
Working Papers , Paper 19-30

Working Paper
Estimating the Trend Unemployment Rate in the Fourth Federal Reserve District

We estimate trend unemployment rates for Ohio, Pennsylvania, Kentucky, and West Virginia, states that span parts of the Fourth District of the Federal Reserve System. Our estimated unemployment rate trend for the District as a whole stood at 5.7 percent in 2020:Q1 compared to a 4.7 percent observed unemployment rate within the District, implying a tight labor market by historical standards.
Working Papers , Paper 20-19

Working Paper
The Dynamics of the Racial Wealth Gap

We reconcile the large and persistent racial wealth gap with the smaller racial earnings gap, using a general equilibrium heterogeneous-agents model that matches racial differences in earnings, wealth, bequests, and returns to savings. Given initial racial wealth inequality in 1962, our model attributes the slow convergence of the racial wealth gap primarily to earnings, with much smaller roles for bequests or returns to savings. Cross-sectional regressions of wealth on earnings using simulated data produce the same racial gap documented in the literature. One-time wealth transfers have only ...
Working Papers , Paper 19-18

Working Paper
The Geographic Effects of Monetary Policy

We study the differential regional effects of monetary policy exploiting geographical heterogeneity in income across cities in the United States. We find that prices and employment in poorer cities react more to monetary policy shocks. The results for prices hold for a wide range of narrow consumer expenditure categories. The results are consistent with New Keynesian models that allow for a differential share of hand-to-mouth consumers across regions, but not with models in which regions have different slopes of the Phillips curve. We show that an increase in heterogeneity across cities ...
Working Papers , Paper 22-15

Working Paper
Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility

We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for ...
Working Papers , Paper 19-29

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