American inflation remains in check despite Donald Trump’s tariffs
Our predictive index is close to the Federal Reserve’s 2% target
US consumer prices, , % change on a year earlier
The Economist’s prediction model Weights items by price-change rank
%
Headline inflation Weights items by share of
spending
%
Donald Trump’s tariffs are widely expected to increase American consumer prices, and the Federal Reserve has cited them as a reason that it is not lowering interest rates. So far, however, inflation has remained in check. According to data released by the Bureau of Economic Analysis (BEA) on June 27th, the personal consumption expenditures (PCE) index—the central bank’s preferred year-on-year measure of inflation—rose at just a 1.1% annualised rate between March and May, down from 3.6% from October to February.
Are such moderate price increases just the calm before the storm? The Economist’s predictive inflation index helps to provide an answer. Conventional headline measures of inflation are reported on a trailing annual basis, which makes them lagging indicators even the best of times. Given the record uncertainty that President Donald Trump has introduced into American economic policy, data from early 2024 probably say little about where prices are headed now. However, focusing on the latest numbers comes with its own pitfalls. Monthly inflation data are highly volatile, rely on imprecise seasonal adjustments, and can be influenced by extreme price changes for specific items, such as the recent spike in the cost of eggs caused by an outbreak of bird flu.
Our index addresses this trade-off between recency and reliability by making two complementary adjustments to the calculation of headline PCE inflation. First, it discounts outliers. Just like the “trimmed-mean” measure of underlying inflation produced by the Federal Reserve Bank of Dallas, we sort the PCE index’s hundreds of components by their price changes in each month, and re-weight each month’s data based on those rankings. However, rather than outright discarding items with the highest and lowest month-on-month inflation—the Dallas Fed excludes the bottom 24% and top 31% of the consumption basket—we assign a new weight to each item using a smooth curve. Those whose monthly price changes are near the median get around 35% more emphasis in our measure than they do in PCE. In contrast, those at the 10th and 90th percentiles get 30-35% less weight, and those at the outer extremes—currently eggs and petrol—shrink near to zero.
Next, our index blends each of these months of data into an average, with the latest numbers counting more than older ones. The stability introduced by re-weighting items every month enables us to put a large premium on the most recent figures: 30 days’ worth of information from six months ago matter roughly as much as one day in the current month does. These ratios were chosen to maximise our index’s accuracy at forecasting PCE inflation during the subsequent year.* Its predictive nature is clearly visible in the chart above: in historical cases where inflation abruptly rose or fell, such as 1975, 2008 or 2021, our series starts to lurch months before the trailing annual headline figure catches up.
Because our index is the time-weighted product of the past 12 monthly inflation indices—each with its own ranking of price changes by item—the hill-shaped image above does not reflect the exact composition of our tracker. Instead, it produces a close approximation by applying our weighting scheme to year-on-year price changes in each good or service. The headline result reflects the richness of the full model.
The numbers from May are the first data release that includes Mr Trump’s new 10% global tariff, as well as his short-lived triple-digit levy on Chinese goods. However, because importers bent over backwards to avoid bringing in products at such punitive temporary rates, monthly PCE inflation barely changed from the preceding month. Without any evidence of rising price pressures, our predictive index fell from 2.7% to 2.5%. Whenever inflation does start to pick up, our measure will respond swiftly.
How concentrated is inflation?
The chart below tracks excess concentration” in American inflation over the past 65 years. For example, in the year to January 1999 there was high excess concentration. The price of tobacco rose 33% after cigarette manufacturers agreed to an expensive legal settlement for medical costs caused by their products, whereas only one other item saw an increase above 10%. Such imbalances between index components are more typical of environments with 6% inflation, but overall inflation at the time was only 1%.
When inflation is high, prices for some goods and services tend to rise much faster than others. By contrast, when inflation is near the Fed’s target of 2%, prices tend to behave similarly. Exceptions to this pattern offer clues about inflation’s persistence.
To detect such exceptions, we first calculate the standard deviation of year-on-year price changes across the index—a measure of how bunched together or spread out they are. Next, we measure the average historical relationship between this standard deviation and the overall inflation level. Finally, we ask whether the standard deviation is in line with this historical relationship. We call a deviation from the trend “excess concentration”.
When excess concentration is positive—meaning that the items whose prices are rising fastest are driving the headline rate to an unusual degree—inflation often reverts towards its longer-term historical average. On the other hand, when excess concentration is negative—meaning the level of inflation is more uniform than normal—recent changes in inflation tend to be "sticky", and persist into the future. Excess concentration is a good predictor of whether our predictive inflation index is above or below headline inflation.
Sources: U.S. Bureau of Economic Analysis, The Economist
* For statistically minded readers, at the time of publication in May 2025, our model’s root mean square error at predicting headline PCE inflation during the subsequent 12 months was 1.42 percentage points. This compares with 1.63 points for simply using headline inflation to predict itself, and 1.65 points for the “core” measure that excludes food and energy prices. Since January 1978, the first month covered by the Dallas Fed’s trimmed-mean measure, our predictive inflation index’s root mean square error is 1.29 percentage points, compared with 1.40 points for the one-month trimmed mean, 1.36 points for the six-month, and 1.44 points for the 12-month.
American inflation remains in check despite Donald Trump’s tariffs
Our predictive index is close to the Federal Reserve’s 2% target
US consumer prices, , % change on a year earlier
The Economist’s prediction model Weights items by price-change rank
%
Headline inflation Weights items by share of
spending
%
Donald Trump’s tariffs are widely expected to increase American consumer prices, and the Federal Reserve has cited them as a reason that it is not lowering interest rates. So far, however, inflation has remained in check. According to data released by the Bureau of Economic Analysis (BEA) on June 27th, the personal consumption expenditures (PCE) index—the central bank’s preferred year-on-year measure of inflation—rose at just a 1.1% annualised rate between March and May, down from 3.6% from October to February.
Are such moderate price increases just the calm before the storm? The Economist’s predictive inflation index helps to provide an answer. Conventional headline measures of inflation are reported on a trailing annual basis, which makes them lagging indicators even the best of times. Given the record uncertainty that President Donald Trump has introduced into American economic policy, data from early 2024 probably say little about where prices are headed now. However, focusing on the latest numbers comes with its own pitfalls. Monthly inflation data are highly volatile, rely on imprecise seasonal adjustments, and can be influenced by extreme price changes for specific items, such as the recent spike in the cost of eggs caused by an outbreak of bird flu.
Our index addresses this trade-off between recency and reliability by making two complementary adjustments to the calculation of headline PCE inflation. First, it discounts outliers. Just like the “trimmed-mean” measure of underlying inflation produced by the Federal Reserve Bank of Dallas, we sort the PCE index’s hundreds of components by their price changes in each month, and re-weight each month’s data based on those rankings. However, rather than outright discarding items with the highest and lowest month-on-month inflation—the Dallas Fed excludes the bottom 24% and top 31% of the consumption basket—we assign a new weight to each item using a smooth curve. Those whose monthly price changes are near the median get around 35% more emphasis in our measure than they do in PCE. In contrast, those at the 10th and 90th percentiles get 30-35% less weight, and those at the outer extremes—currently eggs and petrol—shrink near to zero.
Next, our index blends each of these months of data into an average, with the latest numbers counting more than older ones. The stability introduced by re-weighting items every month enables us to put a large premium on the most recent figures: 30 days’ worth of information from six months ago matter roughly as much as one day in the current month does. These ratios were chosen to maximise our index’s accuracy at forecasting PCE inflation during the subsequent year.* Its predictive nature is clearly visible in the chart above: in historical cases where inflation abruptly rose or fell, such as 1975, 2008 or 2021, our series starts to lurch months before the trailing annual headline figure catches up.
Because our index is the time-weighted product of the past 12 monthly inflation indices—each with its own ranking of price changes by item—the hill-shaped image above does not reflect the exact composition of our tracker. Instead, it produces a close approximation by applying our weighting scheme to year-on-year price changes in each good or service. The headline result reflects the richness of the full model.
The numbers from May are the first data release that includes Mr Trump’s new 10% global tariff, as well as his short-lived triple-digit levy on Chinese goods. However, because importers bent over backwards to avoid bringing in products at such punitive temporary rates, monthly PCE inflation barely changed from the preceding month. Without any evidence of rising price pressures, our predictive index fell from 2.7% to 2.5%. Whenever inflation does start to pick up, our measure will respond swiftly.
How concentrated is inflation?
The chart below tracks excess concentration” in American inflation over the past 65 years. For example, in the year to January 1999 there was high excess concentration. The price of tobacco rose 33% after cigarette manufacturers agreed to an expensive legal settlement for medical costs caused by their products, whereas only one other item saw an increase above 10%. Such imbalances between index components are more typical of environments with 6% inflation, but overall inflation at the time was only 1%.
When inflation is high, prices for some goods and services tend to rise much faster than others. By contrast, when inflation is near the Fed’s target of 2%, prices tend to behave similarly. Exceptions to this pattern offer clues about inflation’s persistence.
To detect such exceptions, we first calculate the standard deviation of year-on-year price changes across the index—a measure of how bunched together or spread out they are. Next, we measure the average historical relationship between this standard deviation and the overall inflation level. Finally, we ask whether the standard deviation is in line with this historical relationship. We call a deviation from the trend “excess concentration”.
When excess concentration is positive—meaning that the items whose prices are rising fastest are driving the headline rate to an unusual degree—inflation often reverts towards its longer-term historical average. On the other hand, when excess concentration is negative—meaning the level of inflation is more uniform than normal—recent changes in inflation tend to be "sticky", and persist into the future. Excess concentration is a good predictor of whether our predictive inflation index is above or below headline inflation.
Sources: U.S. Bureau of Economic Analysis, The Economist
* For statistically minded readers, at the time of publication in May 2025, our model’s root mean square error at predicting headline PCE inflation during the subsequent 12 months was 1.42 percentage points. This compares with 1.63 points for simply using headline inflation to predict itself, and 1.65 points for the “core” measure that excludes food and energy prices. Since January 1978, the first month covered by the Dallas Fed’s trimmed-mean measure, our predictive inflation index’s root mean square error is 1.29 percentage points, compared with 1.40 points for the one-month trimmed mean, 1.36 points for the six-month, and 1.44 points for the 12-month.
American inflation remains in check despite Donald Trump’s tariffs
Our predictive index is close to the Federal Reserve’s 2% target
US consumer prices, , % change on a year earlier
The Economist’s prediction model Weights items by price-change rank
%
Headline inflation Weights items by share of
spending
%
Donald Trump’s tariffs are widely expected to increase American consumer prices, and the Federal Reserve has cited them as a reason that it is not lowering interest rates. So far, however, inflation has remained in check. According to data released by the Bureau of Economic Analysis (BEA) on June 27th, the personal consumption expenditures (PCE) index—the central bank’s preferred year-on-year measure of inflation—rose at just a 1.1% annualised rate between March and May, down from 3.6% from October to February.
Are such moderate price increases just the calm before the storm? The Economist’s predictive inflation index helps to provide an answer. Conventional headline measures of inflation are reported on a trailing annual basis, which makes them lagging indicators even the best of times. Given the record uncertainty that President Donald Trump has introduced into American economic policy, data from early 2024 probably say little about where prices are headed now. However, focusing on the latest numbers comes with its own pitfalls. Monthly inflation data are highly volatile, rely on imprecise seasonal adjustments, and can be influenced by extreme price changes for specific items, such as the recent spike in the cost of eggs caused by an outbreak of bird flu.
Our index addresses this trade-off between recency and reliability by making two complementary adjustments to the calculation of headline PCE inflation. First, it discounts outliers. Just like the “trimmed-mean” measure of underlying inflation produced by the Federal Reserve Bank of Dallas, we sort the PCE index’s hundreds of components by their price changes in each month, and re-weight each month’s data based on those rankings. However, rather than outright discarding items with the highest and lowest month-on-month inflation—the Dallas Fed excludes the bottom 24% and top 31% of the consumption basket—we assign a new weight to each item using a smooth curve. Those whose monthly price changes are near the median get around 35% more emphasis in our measure than they do in PCE. In contrast, those at the 10th and 90th percentiles get 30-35% less weight, and those at the outer extremes—currently eggs and petrol—shrink near to zero.
Next, our index blends each of these months of data into an average, with the latest numbers counting more than older ones. The stability introduced by re-weighting items every month enables us to put a large premium on the most recent figures: 30 days’ worth of information from six months ago matter roughly as much as one day in the current month does. These ratios were chosen to maximise our index’s accuracy at forecasting PCE inflation during the subsequent year.* Its predictive nature is clearly visible in the chart above: in historical cases where inflation abruptly rose or fell, such as 1975, 2008 or 2021, our series starts to lurch months before the trailing annual headline figure catches up.
Because our index is the time-weighted product of the past 12 monthly inflation indices—each with its own ranking of price changes by item—the hill-shaped image above does not reflect the exact composition of our tracker. Instead, it produces a close approximation by applying our weighting scheme to year-on-year price changes in each good or service. The headline result reflects the richness of the full model.
The numbers from May are the first data release that includes Mr Trump’s new 10% global tariff, as well as his short-lived triple-digit levy on Chinese goods. However, because importers bent over backwards to avoid bringing in products at such punitive temporary rates, monthly PCE inflation barely changed from the preceding month. Without any evidence of rising price pressures, our predictive index fell from 2.7% to 2.5%. Whenever inflation does start to pick up, our measure will respond swiftly.
How concentrated is inflation?
The chart below tracks excess concentration” in American inflation over the past 65 years. For example, in the year to January 1999 there was high excess concentration. The price of tobacco rose 33% after cigarette manufacturers agreed to an expensive legal settlement for medical costs caused by their products, whereas only one other item saw an increase above 10%. Such imbalances between index components are more typical of environments with 6% inflation, but overall inflation at the time was only 1%.
When inflation is high, prices for some goods and services tend to rise much faster than others. By contrast, when inflation is near the Fed’s target of 2%, prices tend to behave similarly. Exceptions to this pattern offer clues about inflation’s persistence.
To detect such exceptions, we first calculate the standard deviation of year-on-year price changes across the index—a measure of how bunched together or spread out they are. Next, we measure the average historical relationship between this standard deviation and the overall inflation level. Finally, we ask whether the standard deviation is in line with this historical relationship. We call a deviation from the trend “excess concentration”.
When excess concentration is positive—meaning that the items whose prices are rising fastest are driving the headline rate to an unusual degree—inflation often reverts towards its longer-term historical average. On the other hand, when excess concentration is negative—meaning the level of inflation is more uniform than normal—recent changes in inflation tend to be "sticky", and persist into the future. Excess concentration is a good predictor of whether our predictive inflation index is above or below headline inflation.
Sources: U.S. Bureau of Economic Analysis, The Economist
* For statistically minded readers, at the time of publication in May 2025, our model’s root mean square error at predicting headline PCE inflation during the subsequent 12 months was 1.42 percentage points. This compares with 1.63 points for simply using headline inflation to predict itself, and 1.65 points for the “core” measure that excludes food and energy prices. Since January 1978, the first month covered by the Dallas Fed’s trimmed-mean measure, our predictive inflation index’s root mean square error is 1.29 percentage points, compared with 1.40 points for the one-month trimmed mean, 1.36 points for the six-month, and 1.44 points for the 12-month.
American inflation remains in check despite Donald Trump’s tariffs
Our predictive index is close to the Federal Reserve’s 2% target
US consumer prices, , % change on a year earlier
The Economist’s prediction model Weights items by price-change rank
%
Headline inflation Weights items by share of
spending
%
Donald Trump’s tariffs are widely expected to increase American consumer prices, and the Federal Reserve has cited them as a reason that it is not lowering interest rates. So far, however, inflation has remained in check. According to data released by the Bureau of Economic Analysis (BEA) on June 27th, the personal consumption expenditures (PCE) index—the central bank’s preferred year-on-year measure of inflation—rose at just a 1.1% annualised rate between March and May, down from 3.6% from October to February.
Are such moderate price increases just the calm before the storm? The Economist’s predictive inflation index helps to provide an answer. Conventional headline measures of inflation are reported on a trailing annual basis, which makes them lagging indicators even the best of times. Given the record uncertainty that President Donald Trump has introduced into American economic policy, data from early 2024 probably say little about where prices are headed now. However, focusing on the latest numbers comes with its own pitfalls. Monthly inflation data are highly volatile, rely on imprecise seasonal adjustments, and can be influenced by extreme price changes for specific items, such as the recent spike in the cost of eggs caused by an outbreak of bird flu.
Our index addresses this trade-off between recency and reliability by making two complementary adjustments to the calculation of headline PCE inflation. First, it discounts outliers. Just like the “trimmed-mean” measure of underlying inflation produced by the Federal Reserve Bank of Dallas, we sort the PCE index’s hundreds of components by their price changes in each month, and re-weight each month’s data based on those rankings. However, rather than outright discarding items with the highest and lowest month-on-month inflation—the Dallas Fed excludes the bottom 24% and top 31% of the consumption basket—we assign a new weight to each item using a smooth curve. Those whose monthly price changes are near the median get around 35% more emphasis in our measure than they do in PCE. In contrast, those at the 10th and 90th percentiles get 30-35% less weight, and those at the outer extremes—currently eggs and petrol—shrink near to zero.
Next, our index blends each of these months of data into an average, with the latest numbers counting more than older ones. The stability introduced by re-weighting items every month enables us to put a large premium on the most recent figures: 30 days’ worth of information from six months ago matter roughly as much as one day in the current month does. These ratios were chosen to maximise our index’s accuracy at forecasting PCE inflation during the subsequent year.* Its predictive nature is clearly visible in the chart above: in historical cases where inflation abruptly rose or fell, such as 1975, 2008 or 2021, our series starts to lurch months before the trailing annual headline figure catches up.
Because our index is the time-weighted product of the past 12 monthly inflation indices—each with its own ranking of price changes by item—the hill-shaped image above does not reflect the exact composition of our tracker. Instead, it produces a close approximation by applying our weighting scheme to year-on-year price changes in each good or service. The headline result reflects the richness of the full model.
The numbers from May are the first data release that includes Mr Trump’s new 10% global tariff, as well as his short-lived triple-digit levy on Chinese goods. However, because importers bent over backwards to avoid bringing in products at such punitive temporary rates, monthly PCE inflation barely changed from the preceding month. Without any evidence of rising price pressures, our predictive index fell from 2.7% to 2.5%. Whenever inflation does start to pick up, our measure will respond swiftly.
How concentrated is inflation?
The chart below tracks excess concentration” in American inflation over the past 65 years. For example, in the year to January 1999 there was high excess concentration. The price of tobacco rose 33% after cigarette manufacturers agreed to an expensive legal settlement for medical costs caused by their products, whereas only one other item saw an increase above 10%. Such imbalances between index components are more typical of environments with 6% inflation, but overall inflation at the time was only 1%.
When inflation is high, prices for some goods and services tend to rise much faster than others. By contrast, when inflation is near the Fed’s target of 2%, prices tend to behave similarly. Exceptions to this pattern offer clues about inflation’s persistence.
To detect such exceptions, we first calculate the standard deviation of year-on-year price changes across the index—a measure of how bunched together or spread out they are. Next, we measure the average historical relationship between this standard deviation and the overall inflation level. Finally, we ask whether the standard deviation is in line with this historical relationship. We call a deviation from the trend “excess concentration”.
When excess concentration is positive—meaning that the items whose prices are rising fastest are driving the headline rate to an unusual degree—inflation often reverts towards its longer-term historical average. On the other hand, when excess concentration is negative—meaning the level of inflation is more uniform than normal—recent changes in inflation tend to be "sticky", and persist into the future. Excess concentration is a good predictor of whether our predictive inflation index is above or below headline inflation.
Sources: U.S. Bureau of Economic Analysis, The Economist
* For statistically minded readers, at the time of publication in May 2025, our model’s root mean square error at predicting headline PCE inflation during the subsequent 12 months was 1.42 percentage points. This compares with 1.63 points for simply using headline inflation to predict itself, and 1.65 points for the “core” measure that excludes food and energy prices. Since January 1978, the first month covered by the Dallas Fed’s trimmed-mean measure, our predictive inflation index’s root mean square error is 1.29 percentage points, compared with 1.40 points for the one-month trimmed mean, 1.36 points for the six-month, and 1.44 points for the 12-month.