Changing inflation dynamics and uncertainty in the United States.

• Author: Miles, William

1. Introduction

   After the great inflation of the 1970s, the level of inflation declined in the 1980s and has remained moderate since. However, despite the relatively low level of price changes, there have been, in the current decade, a number of shocks with the potential to affect inflation. These include the bursting of the tech stock bubble, the rise and fall of the housing bubble, the 9/11 terrorist attacks, the Iraq War, and high energy prices.

   While these events have not yet appeared to increase the mean of inflation substantially, they may have an impact on the persistence of inflation shocks. Even more importantly, they could raise uncertainty over the future path of price changes. Inflation uncertainty can decrease the information content of prices (Friedman 1977) and negatively affect investment (Cabellero 1991). Thus, while a moderate level of inflation may have no direct effect on Gross Domestic Product (GDP), inflation uncertainty can negatively impact growth. Grief et al. (2004) and Grier and Grief (2006) find a negative effect of inflation uncertainty on output in the United States and Mexico, respectively.

   There have been previous papers on inflation uncertainty in the United States, but few have allowed for many structural changes to examine the level of uncertainty for different time periods. Moreover, no study has investigated whether uncertainty or any other aspect of inflation has changed in response to the aforementioned shocks of the current decade.

   We will accordingly investigate changes in U.S. inflation, inflation persistence, and inflation uncertainty over time with two estimation strategies. We will first employ a Markovswitching model that allows for shifts in the mean, persistence, and variance. Results will indicate that uncertainty has indeed increased in the present decade.

   Secondly, as a check on these results, we will employ a generalized autoregressive conditional heteroskedasticity (GARCH) (1) model. While other papers have employed the GARCH technique to investigate inflation and uncertainty, most assume parameter constancy. We will instead employ dummies and test for structural breaks. Results will confirm the Markov-switching finding that uncertainty has risen since the end of the 1990s. Moreover, by analyzing headline inflation as well as the CPI less energy costs with both models, our results suggest that more volatile energy prices are at the root of the higher uncertainty.

   This paper proceeds as follows: Section 2 summarizes the previous literature on U.S. inflation, its persistence, and uncertainty. Section 3 describes in detail the methodology to be employed. Results are discussed in section 4, and section 5 concludes.

2. Previous Literature

   There have been a number of attempts at modeling the dynamics of inflation, in particular its mean, persistence, and uncertainty. Some papers have examined only one of these three important properties, ignoring the others, often to the detriment of correct inference.

   Whether the mean, or level, of inflation is subject to breaks because of policy has attracted interest from researchers. Perron (1989) examines the time series properties of the CPI by allowing for two changes in the mean. Clark (2003) and Levin and Piger (2003) both find that allowing for structural changes in inflation affects other properties, such as estimated persistence.

   The persistence of inflation shocks will typically be high when the credibility of the central bank is low and vice-versa. If agents have confidence in the inflation-fighting intentions of the Federal Reserve (the Fed), a temporary spike in prices will not typically cause a prolonged rise in inflation. Along these lines, Alogoskoufis and Smith (1991) examine changes in inflation persistence resulting from departures from exchange rate pegs. The authors find that episodes such as the United States leaving the gold standard and the collapse of the Bretton Woods system did in fact lead to palpable increases in inflation persistence. Burdekin and Siklos (1999) conjecture that while the exchange rate regime has an effect on credibility, other events can also impact persistence. These authors find that events such as oil shocks have larger impacts than changes in currency pegs.

   Persistence is often measured as the size of autoregressive moving average (ARMA) (2) coefficients in the time series model of inflation. By using a Markov-switching model, Kim (1993) finds that infrequent permanent shocks contribute most to the persistence in the inflation rate. Some recent papers, such as Levin and Piger (2003), find that persistence has declined in recent years in the United States. Cecchetti and Debelle (2006) note that, in examining inflation dynamics, it is vital to distinguish between changes in the level and changes in persistence. Failure to control for a change in one will lead to a bias in testing, in which there appears to have been a change in the other, even if no such change has actually occurred. These authors control for both mean and persistence change in their paper, and find that, while there has been a structural decline in persistence in the United States in recent years, it is of small magnitude.

   The last, but perhaps most important, aspect of the inflation process to be investigated is uncertainty. As long as inflation is moderate, its level is likely neutral for output. However, Friedman (1977) argues that uncertainty concerning future inflation lowers the information content of prices, and thus hampers commerce and lowers income. Moreover, by making future price changes more difficult to forecast, inflation uncertainty can lower investment, and hence output (Cabellero 1991). Grier et al. (2004) find that, for the United States, an increase in inflation uncertainty indeed lowers GDP growth. Grier and Grier (2006) find this negative impact of inflation uncertainty on growth holds for Mexico as well.

   Given the importance of inflation uncertainty, there have been many papers investigating it for the United States. Many question how much an increase in the level of inflation raises uncertainty. In a model in which shocks are decomposed into transitory and permanent components, Kim (1993) finds that high uncertainty about permanent shocks is associated with a positive shift in inflation. Caporale and McKiernan (1997) and Grier and Perry (1998) both find that an increase in the level of inflation raises inflation uncertainty in the United States, as Friedman (1977) claimed would be the case. In order to investigate inflation uncertainty, some empirical proxy must be obtained. Traditionally, the measure of uncertainty has been taken to be either the unconditional variance of inflation or the conditional variance of estimated inflation shocks (GARCH models; see Thornton 2007). GARCH models have become fairly standard in investigating inflation. However, Markov-switching models have become increasingly employed in macroeconomics and have been recommended as an alternative to GARCH (Kim and Nelson 1999, p. 3). (3) This is particularly the case since Kim and Nelson have developed a model that allows for changes in the mean, persistence, and variance of the series. As we detail below, we will employ the Markov-switching method first, and then use the GARCH model as a check.

   While inflation and its properties in the United States have been widely analyzed, most assume parameter constancy in uncertainty or allow for at most a one-time break. Many changes have occurred in the last 8 to 10 years: the tech bubble, the end of Alan Greenspan's tenure, the rise and bursting of the housing bubble, and perhaps most importantly, the 9/11 terrorist attack, the Afghanistan and Iraq wars, and volatile energy prices. Given these changes, it is a delusion to presume constancy in uncertainty. Higher energy prices may well affect the uncertainty surrounding inflation, and no paper has focused on how the changes of the last decade may have had an impact. This paper aims to fill that gap.

3. Methodology

   There are two ways to model changing variance (or uncertainty). The changing variance of a Markov-switching model results from regime changes in the variance structure. That differs from the varying variances of a GARCH model, which are dependent on the squares of previous innovations and previous conditional variances within a given structure (regime). A state-dependent Markov-switching model is useful in modeling structural change where the parameters vary as the regime changes. If the dates of the regime switching are known, the state variable is equivalent to a dummy variable. In that case, a dummy variable incorporated in a GARCH model to reflect changes in mean and variance of inflation should not be inferior to the Markov-switching model and both models should capture the structural change, if there is indeed a...