SPEECH
SVERIGES RIKSBANK
DATE:
13/05/2019
SE-103 37 Stockholm
SPEAKER: (Brunkebergstorg 11)
Deputy Governor Henry Ohlsson
VENUE:
Swedish Society of Financial Analysts
Tel +46 8 787 00 00
Fax +46 8 21 05 31
registratorn@riksbank.se
www.riksbank.se
Underlying inflation - for better or for
worse∗
Thank you very much for the invitation to come and speak at the Swedish
Society of Financial Analysts!
Concepts such as underlying inflation and core inflation are often used in
discussions on inflation and monetary policy. But what do these concepts
actually mean? And what do measures of underlying inflation actually
measure? My aim today is to discuss why central banks are interested in
underlying inflation and how it can be measured.
I will first explain why we need to measure underlying inflation (or core
inflation, which I regard as a synonym) and then briefly describe the history
of the concept. There are two common methods of calculating underlying
inflation, and on the basis of these I will discuss which properties are
desirable for measures of underlying inflation. I will also present different
examples of measures.
There are measures that are better or worse with regard to how well they
fulfil the criteria for desirable properties. But a first conclusion is that there
is no measure that is unequivocally better than all the others. Although
there are a couple of measures that are clearly better than others.
A second conclusion is that this shortage of an unequivocal measures
means that problems may arise for monetary policy decision-makers. On
the one hand, we need to test our way forward with regard to the available
data and look at them from different aspects and for this we need the
different measures. But we also need, on the other hand, to be clear and
transparent with regard to the objective for monetary policy. I would say
that we have seen examples of this conflict in recent years. It can be
problematic if monetary policy decision-makers talk too much about underlying
inflation, because the rest of the world can start to believe that the focus
of monetary policy is shifting from the actual inflation rate to some other
measure.
Differentiate between lasting and temporary price
movements
We need some background to understand why these are important
questions. According to the Sveriges Riksbank Act, the objective for monetary
policy is to maintain price stability. In 1993, the General Council of the
Riksbank specified the operational target for monetary policy to be an annual
rate of inflation of 2 per cent. For a long time the inflation target was
expressed in terms of the Consumer Price Index (the CPI), but since 2017, it
has been expressed in terms of the CPI with a fixed interest rate (the CPIF).
In the CPIF the direct effects of changes in mortgage rate have been
1 2
excluded.
The following description of the concept of inflation can be found on the
Riksbank's website:
If prices of some individual goods or services rise, this is not
inflation. Prices of individual goods and services can rise because,
for instance, it is more difficult to get hold of them. For
instance, the price of oil may rise as oil reserves diminish. Such
price increases are usually called relative price increases and
are thus not inflation. For inflation to exist there should be an
increase in the general price level, that is, prices in general
should rise. And if one is to call it inflation, the price increase
should be lasting. If, for instance, the government raises VAT,
this has a one-off effect on the general price level, but does not
lead to prices continuing to rise.
The key concepts in the quotation are "prices in general" and "lasting".
Using this as a starting point, it is reasonable to try to calculate measures of
inflation that distinguish temporary price changes regarding individual
products from more lasting price changes covering several goods and
services. Most central banks tend to use concepts such as core inflation or
underlying inflation in this way.3
The concept of underlying inflation began to be used
more often during the 1970s
Underlying inflation, or core inflation, is a concept that has long been used
in the monetary policy debate. However, it has received rather more
sporadic treatment in academic literature. The concept is defined for the first
time in an article by Schreder (1952), analysing text in a number of articles
from the JSTOR database (see Wynne, 2008). This was in connection with a
discussion on the general increase in prices that was expected to happen in
the United States:
Even those who tend to agree with the concept of a rough
balance between supply and demand (i.e. no inflationary or
deflationary gap), point out that there is still a huge money supply-
and that is the hard core of inflation....
In time, this concept began to be used increasingly often, especially when
inflation began to rise at a rapid pace in the beginning of the 1970s (see
Figure 1).4 According to Gordon (1975a), the higher rate of inflation was
due to a number of factors: That food prices had begun to increase faster
as a result of supply factors in the United States and abroad, that the oil
price had risen as a result of production restrictions introduced by the
OPEC oil cartel, that the Nixon administration had introduced price and
wage freezes and the devaluation of the dollar in 1971 and 1973. Now a
need arose to distinguish temporary price increases from more lasting
ones.
Figure 1. CPI inflation in the USA
Annual percentage change.
16
14
12
10
8
6
4
2
0
-2
-4
50 55 60 65 70 75 80 85 90 95 00 05 10 15
Source: Bureau of Labor Statistics
In November 1974, when the rate of inflation according to the consumer
price index amounted to more than 12 per cent in the United States,
Milton Friedman wrote the following in Newsweek:5
That monetary expansion on the average could be expected to
produce an inflation of something like 6 per cent a year, not of
12 per cent. The difference is attributable to nonmonetary
factors, mostly transitory, and so constitutes something of a
bubble on the underlying inflation trend. In the past, when such
bubbles have broken, the rate of inflation has declined
precipitously. For example, in the Korean War bubble, the rate of
inflation of consumer prices fell from 10 per cent to 2 per cent in
nine months.
One now began to use different filtered measures in the econometric
research literature. Gordon (1975b) calculates an equation where the
aggregate selling prices exclude energy and food prices. He motivated his choice
by saying that variations in food and energy prices, particularly in the
beginning of the 1970s, were due to other factors than domestic supply and
7
demand.6
Some calculations had been published earlier, but from 1978 the Bureau of
Labor Statistics (BLS) started regular reporting of monthly figures for the
consumer price index and the producer price index that excluded food and
energy prices. At the same time, the Bureau of Economic Analysis (BEA)
began to publish monthly figures for the consumption deflator (PCE)
excluding food and energy prices.8
Back in the early days of the inflation targeting regime, it was part of the
Riksbank's monetary policy strategy not to allow effects assessed as
temporary to influence the actual policymaking to any great extent.9 Since
then, various ways of measuring underlying inflation have been designed
and used in inflation reports, in-depth articles, commentaries and
speeches, for instance. Their purpose has been to describe the inflation
process and chisel out an assessment of future inflation.10 Various
employees at the Riksbank have also written articles of a more theoretical nature
on this subject.11
Two different ways of calculating core inflation
Despite the concept of underlying inflation often being used in central bank
communication, there is no unequivocal way to describe it. The most
common methods can be divided into two main lines.12 The first involves using
different statistical methods to exclude, or dampen, the effect of
temporary price changes over time.13 The second involves excluding
predetermined goods and services whose prices usually vary a lot.
Let us look at an example of the first type. Figure 2 shows how inflation in
Sweden has developed since 2011. It shows clearly a falling trend in
inflation up to 2014-2015. After that, the trend has been an upward one. But it
is also clear that there are major variations from one month to the next. In
other words, there is a considerable amount of white noise in the series.
Figure 2. CPIF inflation
Annual percentage change.
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
11 12 13 14 15 16 17 18 19
CPIF
Sources: Statistics Sweden and the Riksbank
A simple way of getting rid of the white noise and clarifying the actual
trend is to calculate a twelve-month moving average for the series.14 Figure
3 shows the results of such a calculation. Now the white noise has been
removed and the underlying development is shown more clearly. But it must
be emphasised that this is the underlying development given the method I
have chosen.
Figure 3. CPIF inflation, twelve-month moving average
Annual percentage change.
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
11 12 13 14 15 16 17 18 19
CPIF, twelve-month moving average
Sources: Statistics Sweden and the Riksbank
The second common method of calculating underlying inflation is, as we
said, to exclude certain predetermined prices or groups of prices. Perhaps
the most common measure used internationally is inflation excluding
energy and food prices. Energy prices contribute to more temporary and
short-term price movements, the same applies to food prices. One reason
for these price movements is temporary variations in demand. Unusual
weather can also temporarily affect prices of, for instance, electricity,
cereals, fruit and vegetables. The risk of excluding entire product groups is that
prices in these groups may have changed in a more lasting manner.
CPIF excluding energy is an often used index in Sweden. The columns in
Figure 4 show how energy prices have contributed to CPIF inflation. The
contribution from energy prices has at times been fairly large (just over a
percentage point). But one can also see that it does not vary as much from
month to month if it is expressed as an annual percentage change. A
positive contribution in one month tends to be followed by a positive
contribution the next month, too.
Figure 4. CPIF inflation and energy prices
Annual percentage change.
4.0 105.1
3.5 104.9
3.0 104.7
2.5 104.5
2.0 104.3
1.5 104.1
1.0 103.8
0.5 103.6
0.0 103.4
-0.5 103.2
-1.0 103.0
06 07 08 09 10 11 12 13 14 15 16 17 18 19
CPIF / CPIF excluding energy (index = 100 year 2000), right
Contributions to CPIF, oil-related products
Contribution to CPIF, electricity
Sources: Statistics Sweden and the Riksbank
The solid line in the figure is a different way of showing the same thing. The
line shows the ratio between index figures for the CPIF and the CPIF
excluding energy. When energy prices rise faster than other prices, the
numerator (CPIF) increases faster than the denominator (the CPIF excluding
energy) and the ratio rises. Energy prices rose more slowly than other
prices during the period 2012-2016. Since 2016, on the other hand, energy
prices have risen faster than other prices in the CPIF.
Which properties are desirable for measures of
underlying inflation?
Which properties are then desirable for measures of underlying inflation?
Here are some of those most often discussed in academic literature:15
The measure of underlying inflation should have the same average as the
actual inflation rate.
The average for the measure of the underlying inflation should agree with
the average of the actual inflation rate. In other words, it is desirable that
measures of underlying inflation neither underestimate nor overestimate
the long-term development of inflation, the measure should have the same
average as inflation. Policy-makers should thus not be guided by an
indicator that can be misleading and can lead to conducting a too tight or too
expansionary monetary policy. If the average is the same as for the target
variable, it makes communication easier.
The measure of underlying inflation should covary with the trend in actual
inflation and should have lower volatility.
The underlying measure should covary with long-term movements in the
target variable.16 When inflation has a rising trend, the underlying inflation
measure should increase at the same pace.17 On the other hand, the
underlying measure should vary less than the target variable.
The measure of underlying inflation should say something about the future
actual inflation rate.
The measure should be forward-looking, that is, say something about
future inflation. If the underlying measure covaries with the trend in actual
inflation, this should give some guidance regarding future developments.
Let us imagine a situation where the underlying measure of inflation is
higher than the actual rate of inflation. If it is a good measure of underlying
inflation, it should then show the actual inflation rate rising somewhat
further ahead.
The measure of underlying inflation should covary with factors that are
important to the development of the actual inflation rate.
It is also desirable that the measure covaries with macroeconomic factors
that are important to the development of actual inflation, such as the
general demand situation in the economy. The better the measure succeeds in
adjusting for white noise, the better it should covary with various
explanatory factors.
The measure of underlying inflation should be easy to understand.
The underlying measure should be available soon after new data on actual
inflation has been published. It should also be easy to understand how the
indicators have been calculated.
An Economic Commentary published by the Riksbank in 2018 contains a
study of a number of measure of underlying inflation.18 One of the better
measures according to the above criteria is based on so-called principal
component analysis, CPIFPC. This method aims to capture common trends
among the various price indices for the sub-groups that together comprise
the CPIF. As a final step, the component is standardised so that it has the
same mean value as the CPIF. This means that the first criterion is met by
definition. The other criteria are well met in relation to other measures,
possibly with the exception of the final criterion, of being easy to
understand.
Figure 5 shows how CPIFPC has developed since 1995. It is clear that the
underlying measures shows less white noise than the original CPIF series,
but it is not entirely without white noise. At the same time, it is easy to
detect the more long-term trends.
Figure 5. Underlying measure calculated using principal component
analysis and CPIF inflation
Annual percentage change.
3.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
11 12 13 14 15 16 17 18 19
CPIFPC
CPIF
Sources: Statistics Sweden and the Riksbank
Which consumer prices should be excluded?
The CPIFPC is a measure of underlying inflation calculated using the
statistical method. In this section I show how consumer prices for various groups
of goods and services have developed since 1995. This is the type of basic
data needed when calculating underlying inflation with the aid of
exclusion.
Table 1 below shows the fifteen components (or price groups) in the CPIF
that have varied the most since 1995, measured according to the standard
deviation for the series.19 The results show that prices of coffee, tea and
cocoa have had the most volatile rates of price increase during the period.
Table 2 shows the fifteen components that have varied the least. This
shows that rents have had the least volatile rate of price increase during
the period.
Table 1. Components of the CPIF with the highest volatility 1995-2019
Sub-index of the CPIF Standard Average Weight
deviation
Coffee, tea, cocoa 12.6 1.8 0.4
Owner-occupied housing: freehold, prop- 11.5 1.5 0.7
Fruit and vegetables, Swedish 10.7 2.2 0.3
Owner-occupied housing: heating excluding 10.0 5.6 0.5
Owner-occupied housing: electricity 8.5 4.2 2.8
Pharmaceuticals 8.5 3.5 1.2
Tobacco products 8.0 4.7 1.6
Cameras, film 7.8 -7.4 0.1
Gold products 7.8 4.2 0.3
Owner-occupied housing, apartments: repa- 7.3 2.0 3.0
Fuel 7.2 3.2 2.5
Gas and electricity, rented and tenant- 7.2 4.4 1.1
Fruit and vegetables, imported 6.3 2.8 0.7
Dental fees 6.3 4.8 0.9
Books 6.3 1.6 0.4
Sources: Statistics Sweden and the Riksbank.
Note. The calculations are made using monthly data expressed an annual percentage change. The weights for all
groups included in the calculation add up to 100.
Table 2. Components of the CPIF with the lowest volatility 1995-2019
Sub-index of the CPIF Standard Average Weight
deviation
Rent 1.0 1.9 9.6
Alcohol, restaurant visits 1.2 2.3 1.0
Personal hygiene (services) 1.3 3.3 1.8
Garage costs 1.3 2.1 0.2
Water and sewerage, refuse collection, 1.3 2.7 1.0
Food (outside the home) 1.4 2.6 5.4
Lottery, pools20 1.4 1.3 1.1
Alcohol, purchased in store 1.5 1.1 2.1
Fizzy drinks, light beer 1.5 0.9 0.8
Entertainment and recreation 1.6 2.0 3.5
Personal hygiene (goods) 1.7 1.0 1.3
Capital stock21 1.9 4.6 3.3
Funeral, home insurance, bank, education 2.0 2.8 2.6
Diverse leisure goods 2.0 0.3 0.5
Furniture, carpeting and lighting 2.0 0.2 2.0
Sources: Statistics Sweden and the Riksbank.
Note. The calculations are made using monthly data expressed an annual percentage change. The weights for all
groups included in the calculation add up to 100.
Figure 6 shows the fifteen components with the highest volatility according
to the procedure above, aggregated into an index (see dark blue line). The
total weight of these goods and services in the CPIF is close to 17 per cent.
The red line in the figure also shows an aggregate measure for the
components with the lowest volatility. Their weight in the CPIF is on average 38
per cent during the period. The remaining share is also aggregated and
plotted in the same figure (pale blue line). The series of volatile prices
oscillates between 4.5 and 10.1 (14.6 percentage points) measured as an
annual percentage change, while the index with less volatile prices amounts
to between 1.1 and 3.8 per cent during this period of time.
Figure 6. Prices with high and low volatility in the CPIF respectively
Annual percentage change.
12
10
8
6
4
2
0
-2
-4
-6
95 00 05 10 15
Prices with high volatility (weight about 17 percent)
Prices with low volatility (weight about 38 percent)
Other prices (weight about 45 percent)
Sources: Statistics Sweden and the Riksbank
The average for the index with high volatility is 3.0 per cent, calculated
from 1995.22 The average for CPIF inflation since 1995 is 1.6 per cent. In
other words, if one excludes the groups with high volatility one obtains a
measure of underlying inflation that has a much lower average than the
actual inflation for the period of time concerned. And at the same time, the
average for the groups with low price volatility is also higher than the
actual rate of inflation; 2.2 per cent compared with 1.6 per cent.
It is common to divide the CPIF into separate indices for goods, services,
energy and food. The prices of these large groups can have different
determinants, which motivates sometimes wanting to distinguish between the
price increases.23 Service prices have the greatest weight among the
components with the lowest volatility (Table 2).24 There is more of a mixture
among prices with high volatility, but all energy prices are included there.
There are fewer services prices in the group with high volatility.
It is not the same prices that are volatile all the time. Figure 7 plots the
volatility of four price indices over time. The standard deviations here are
calculated over a moving 36-month period25. The figure shows that volatility
has different levels during different periods. The prices of
Swedish-produced fruit and vegetables varied more in 1995-2019 than between 2005
and 2019. The volatility of prices of coffee and cocoa has increased again
after being quite low over a 15-year period. The price movements on books
have become much more volatile in recent years.26 The same applies to
prices for foreign travel.27 The changed volatility can often depend on
changes in measuring methods. A new way of measuring prices of foreign
air travel, for instance, has contributed to a new seasonal pattern with a
higher volatility in recent years.
Figure 7. Rolling standard deviation for different price indices
Annual percentage change
35 35
30 30
25 25
20 20
15 15
10 10
5 5
0 0
95 97 99 01 03 05 07 09 11 13 15 17 19
Fruits and vegetables, Swedish
Coffee and cocoa
Books
Air travel
Sources: Statistics Sweden and the Riksbank
25
The calculations are based on annual percentage changes.
26
The price measurement for books was changed in 2007. The new method, where one follows the prices on a
top list for books sold, probably gives greater variations than the old method where one followed the prices on a
number of indicator books. The top list changes every month, while the indicator list changed less often. Sales
are now captured to a greater extent than before.
27
Foreign air travel was not included in Table 1, but a new method of measuring prices has contributed to a new
seasonal pattern with a higher volatility in recent years.
Underlying measures and actual monetary policy
The different measures of underlying inflation give us the opportunity to
approach the existing data and view it from different angles. But in the
next step, the measures will be used by policymakers responsible for
monetary policy. Here a conflict arises between on the one hand bringing
forward various underlying measures and on the other hand being clear and
transparent about the objective of monetary policy. The conflict become
particularly clear if one chooses to use measures of underlying inflation
that systematically deviate from the actual rate of inflation. Or, in other
words, the poorer the measure of underlying measure of inflation, the
greater the communication challenges.
I would say that we have seen clear examples of this conflict in recent
years: In June 2018, analysts at Handelsbanken Capital Markets considered
it necessary to deny that the Riksbank had changed its target variable in a
newsletter.28
The Riksbank's focus on CPIFXE in connection with the April
meeting resulted in questions on whether the Riksbank was
changing target variable or rudder once again. This is not the
case.
Another example is from September 2018. Nyhetsbyrån Direkt writes:29
CPIFXE inflation (the CPIF excluding energy), the measure on
which the Riksbank is currently focusing a lot on, ...
I have quoted two examples here but there are more. The measure that
was intended to make it easier for us to understand and to help us in our
monetary policy decision-making has instead come to be perceived as the
actual target for monetary policy.
For my part, I handle the trade-off between taking in other measures than
actual inflation in the assessment and being clear that CPIF inflation is the
target for monetary policy as follows. The desirable properties for an
underlying measure as I described earlier in my speech are very important to
me. The underlying measures I analyse are of decisive significance when
assessing the development of inflation. And I can say here that CPIF excluding
energy does not fare well in the competition.
In my speeches and presentations on monetary policy, I have in principle
always referred to CPIF inflation. When I have included underlying
measures, they have never been alone. This has been my way of
emphasising that it is CPIF inflation -nothing else - that is the target for monetary
policy. When speaking at the monetary policy meetings I have gone a stage
further. On these occasions I have in principle never discussed underlying
measures, but focused entirely on CPIF inflation.
What have we learnt?
There can be a lot of white noise in the time series data used for economic
analysis. This applies in particular to monthly data. One therefore needs to
adjust the data for white noise and seasonal variations.
If one wants to analyse the development of inflation, for instance, one
must make these adjustments. The purpose is to calculate the underlying
rate of inflation, or, in other words, core inflation. There are largely two
different ways of doing this. One is to use more or less advanced statistical
methods to adjust for white noise and temporary changes. It is also
possible to exclude goods and services groups that have proved to have volatile
prices.
Today I have presented a number of examples of measures calculate with
the different methods. There are a number of important properties that
one wants these measures to have. They should, for instance, show the
same basic development as the non-adjusted series. There are measures
that are better or worse with regard to how well they fulfil the criteria for
desirable properties. But there is no measure that is unequivocally better
than all the others.
And this is where the problems arise for the monetary policy
decision-makers. There is, on the one hand, a need to approach the existing data with
caution and to regard them from different perspectives. But at the same
time, there is on the other hand, a need to be clear and transparent with
regard to the objective for monetary policy. I have shown obvious
examples of this conflict from recent years.
Another thing that it is important to realise is that what I have presented
here are different types of decomposition of the development of inflation.
Decomposition can occur in different ways, I have talked about two main
methods here, but they both concern decomposition.
It is not a question of basic explanations of the development of inflation.
Inflation is the result of a very large number of economic agents' individual
decisions to change prices. This can be a businessman altering a price list,
or negotiation between a buyer and a seller, it can be a public
procurement, it can be an employee setting prices with the aid of software that
compares competitors' prices on-line, etc. Exactly what lies behind these
decisions, what explains them, is not something we can learn from
decomposition.
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