Quantile Inferences for Inflation and Its Variability: Does a Threshold Inflation Rate Exist?

Using quantile regressions and cross-sectional data from 152 countries, we examine the relationship between inflation and its variability. We consider two measures of inflation - the mean and median - and three different measures of inflation variability - the standard deviation, coefficient of variation, and median deviation. Using the mean and standard deviation or the median and the median deviation, the results support both the hypothesis that higher inflation creates more inflation variability and that inflation variability raises inflation across quantiles. Moreover, higher quantiles in both cases lead to larger marginal effects of inflation (inflation variability) on inflation variability (inflation). Using the mean and the coefficient of variation, however, the findings largely support no correlation between inflation and its variability. Finally, we also consider whether thresholds for inflation rate or inflation variability exist before finding such positive correlations. We find evidence of thresholds for inflation rates below 3 percent, but mixed results for thresholds for inflation variability.

Monetary Policy Delegation, Contract Costs, and Contract Targets

We reconsider the optimal central banker contract derived in Walsh (1995). We show that if the government's objective function places weight (value) on the cost of the contract, then the optimal inflation contract does not completely neutralize the inflation bias. That is, a fraction of the inflation bias emerges in the resulting inflation rate after the central banker's monetary policy decision. Furthermore, the more concerned the government is about the cost of the contract or the less selfish (more benevolent) is the central banker, the smaller is the share of the inflation bias eliminated by the contract. No matter how concerned the government is about the cost of the contract or how unselfish (benevolent) the central banker is, the contract always reduces the inflationary bias by at least half. Finally, a central banker contract written in terms of output (i.e., incorporating an output target) can completely eradicate the inflationary bias, regardless of concerns about contract costs.

Using Leading Indicators to Forecast US Home Sales in a Bayesian VAR Framework

This paper uses Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting US home sales. The benchmark Bayesian model includes home sales, the price of homes, the mortgage rate, real personal disposable income, and the unemployment rate. We evaluate the forecasting performance of six alternative leading indicators by adding each, in turn, to the benchmark model. Out-of-sample forecast performance over three periods shows that the model that includes building permits authorized consistently produces the most accurate forecasts. Thus, the intention to build in the future provides good information with which to predict home sales. Another finding suggests that leading indicators with longer leads outperform the short-leading indicators.