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Working Paper
The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models
Having efficient and accurate samplers for simulating the posterior distribution is crucial for Bayesian analysis. We develop a generic posterior simulator called the "dynamic striated Metropolis-Hastings (DSMH)" sampler. Grounded in the Metropolis-Hastings algorithm, it draws its strengths from both the equi-energy sampler and the sequential Monte Carlo sampler by avoiding the weaknesses of the straight Metropolis-Hastings algorithm as well as those of importance sampling. In particular, the DSMH sampler possesses the capacity to cope with incredibly irregular distributions that are full ...
Report
Forecasting CPI Shelter under Falling Market-Rent Growth
Shelter (housing) costs constitute a large component of price indexes, including 42 percent of the widely followed core Consumer Price Index (CPI). The shelter prices measured in the CPI capture new and existing renters and tend to lag market rents. This lag explains how in recent months the shelter-price index (CPI shelter) has accelerated while market rents have pulled back. We construct an error correction model using data at the metropolitan statistical area level to forecast how CPI shelter will evolve. We forecast that CPI shelter will grow 5.88 percent from September 2022 to September ...
Report
A Faster Convergence of Shelter Prices and Market Rent: Implications for Inflation
The Federal Reserve currently faces a “last-mile” problem in bringing inflation back to its 2 percent target. Following the series of federal funds rate hikes that began in March 2022 and ended in July 2023, core (excluding food and energy) Personal Consumption Expenditure (PCE) inflation dropped from a year-over-year peak of 5.6 percent in February 2022 to 2.9 percent in December 2023. At the end of 2023, hopes were high that falling inflation would allow the Fed to cut interest rates several times in 2024. However, the disinflation process slowed noticeably in early 2024, prompting ...
Working Paper
Price dispersion and inflation: new facts and theoretical implications
From a macroeconomic perspective, price rigidity is often perceived to be an important source of price dispersion, with significant implications for the dynamic properties of aggregate variables, welfare calculations, and the design of optimal policy. For instance, in standard New Keynesian models, the key cost of business cycles stems from the price dispersion resulting from firms' inability to adjust prices instantaneously. However, different macroeconomic models make conflicting predictions about the level of price dispersion, as well as about its dynamic properties and sensitivity to ...
Working Paper
The Pass‐Through of Gaps between Market Rent and the Price of Shelter
The gap between market rent and the price of shelter was 6.6 percent larger in December 2023 relative to December 2019. Because shelter prices comprise 36 percent of the Consumer Price Index and therefore influence monetary policy decisions, it is vital to understand the pass‐through of this difference, or “market‐shelter gap.” I use MSA‐level variation to answer this question. When there is a positive market–shelter gap, the price of shelter grows faster and market rent grows slower until the gap closes, which takes about five years. Faster shelter‐price growth and slower ...
Working Paper
The Pass‐through of Gaps between Market Rent and the Price of Shelter
The gap between market rent and the price of shelter was 6.6 percent larger in December 2023 relative to December 2019. Because shelter prices comprise 36 percent of the Consumer Price Index and therefore influence monetary policy decisions, it is vital to understand the pass‐ through of this difference, or “market‐shelter gap.” I use MSA‐level variation to answer this question. When there is a positive market–shelter gap, the price of shelter grows faster and market rent grows slower until the gap closes, which takes about five years. Faster shelter‐price growth and slower ...
Working Paper
Have Standard VARs Remained Stable since the Crisis?
Small or medium-scale VARs are commonly used in applied macroeconomics for forecasting and evaluating the shock transmission mechanism. This requires the VAR parameters to be stable over the evaluation and forecast sample, or to explicitly consider parameter time variation. The earlier literature focused on whether there were sizable parameter changes in the early 1980s, in either the conditional mean or variance parameters, and in the subsequent period till the beginning of the new century. In this paper we conduct a similar analysis but focus on the effects of the recent crisis. Using a ...
Working Paper
Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts
This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in density accuracy than does just tilting based on the nowcast means. Hence entropic tilting can offer?more so for persistent variables than not-persistent variables?some benefits for accurately estimating the ...
Working Paper
Combining Survey Long-Run Forecasts and Nowcasts with BVAR Forecasts Using Relative Entropy
This paper constructs hybrid forecasts that combine both short- and long-term conditioning information from external surveys with forecasts from a standard fixed-coefficient vector autoregression (VAR) model. Specifically, we use relative entropy to tilt one-step ahead and long-horizon VAR forecasts to match the nowcast and long-horizon forecast from the Survey of Professional Forecasters. The results indicate meaningful gains in multi-horizon forecast accuracy relative to model forecasts that do not incorporate long-term survey conditions. The accuracy gains are achieved for a range of ...
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 ...