Academic journal article Economic Commentary (Cleveland)

Simple Ways to Forecast Inflation: What Works Best?

Academic journal article Economic Commentary (Cleveland)

Simple Ways to Forecast Inflation: What Works Best?

Article excerpt

There are many ways to forecast the future rate of inflation, ranging from sophisticated statistical models involving hundreds of variables to hunches based on past experience. We generate a number of forecasts using a simple statistical model and an even simpler estimating rule, adding in various measures thought to be helpful in predicting the course of inflation. Then we compare their forecast accuracy. We find that no single specification outperforms all others over all time periods. For example, the median and 16 percent trimmed-mean measures outperform all other specifications during the 1990s, and survey-based inflation expectations seem to do better during volatile periods.

Just about everybody pays attention to inflation and wonders when prices are going up, and by how much. Households and businesses need estimates of future prices to make well-informed decisions. Policymakers, whose job is to aid in those decisions by promoting stable prices, need accurate forecasts in order to monitor inflation and make course corrections when necessary.

To get a glimpse into the probable future, one can use a statistical model. In this Commentary, we investigate a few simple versions of these to forecast Consumer Price Index (CPI) inflation, along with some even-simpler rules of thumb. We start with univariate forecasting techniques. Then, in an effort to improve these forecasts, we investigate the forecasting properties of other variables that are thought to affect inflation- economic slack, underlying inflation, and survey measures of expected inflation. We compare the forecast accuracy of a number of different specifications with variants of all of these.

We find that there isn't just one dominant specification that outperforms all other forecast models in every time period. Also, over the past ten years, simple statistics- such as annual inflation rates in alternative price-change measures and inflation expectations obtained from surveys- turn out to be more informative than the statistical models we tested.

A Starting Point

Inflation tends to be a relatively persistent process, which means that current and past values should be helpful in forecasting future inflation. Applying that intuition, we construct two basic models that exploit information embed ded in past values of CPI inflation. Each uses a different technique to forecast CPI inflation over the year ahead: One is based on regression analysis and the other is based on the naïve specification made popular by Atkeson and Ohanian (2001). Later, we add and switch out different variables and different ways of measuring these variables to get other specifications.

The first specification is a regression that forecasts one -yearahead CPI inflation using lags of the CPI (specifically, past values of the quarterly annualized percent change in the CPI).1 We estimate this regression in a recursive manner, starting with a sample that includes 40 quarters of data and adds an additional data point to the sample in each successive quarter.2 This approach is equivalent to saying that the next year's inflation is a function of all past values of inflation up to 4 quarters before. The regression analysis figures out the parameters of that function.

The second specification forecasts one-year-ahead CPI inflation using a naïve specification, in which the forecast over the year ahead is simply the past four-quarter growth rate in the CPI. For example, the four-quarter growth rate in the CPI stands at 1.2 percent through the third quarter of 2010. Using the naïve technique, 1.2 percent becomes our forecast for inflation over the next four quarters (through the third quarter of 2011). This approach is equivalent to saying that inflation over the upcoming year is most likely to be what it was in the past year up to that point.

Because it is possible that the underlying inflation process has changed over time, we test the forecasting performance of these models over a variety of time periods. …

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