Česká národní banka


Should more attention be paid to house prices?

Mojmír Hampl and Tomáš Havránek (Central Banking 28. 4. 2017)

The Czech National Bank’s experiences offer insights into using a broader inflation measure with a greater weight on housing to address both monetary and macro-prudential policy requirements

The Czech Republic is one of the few countries where headline consumer price index (CPI) inflation directly includes house prices. Such inclusion, however, is mostly symbolic, because house prices account for just 16% of the CPI’s owner-occupied-housing component, which in turn represents only 9% of the CPI. This approach gives house prices a 1.4% weight in headline inflation. Moreover, the data on house prices merely covers purchases of dwellings that are new to the household sector, so all transactions between households are excluded from the CPI, even though most real-estate deals occur on the secondary market.

There are good, practical reasons for such treatment, and the Czech Statistical Office (the agency responsible for computing the CPI) is at the frontier concerning the approach to measuring owner-occupied housing. Its current approach is very close to the one Eurostat will probably incorporate into the Harmonised Index of Consumer Prices (HICP) in 2018 or 2019. House prices not only play a prominent role for macro-prudential policy, but also reflect important expenditure that a typical household makes at least once and that influences its consumption behaviour relevant to other segments of the CPI.

We believe it is important to integrate macro-prudential and monetary policy views and not set the tools for each policy independently. Saying so does not diminish the importance of purely macro-prudential tools, which have also been implemented by the Czech National Bank (CNB) – such as a non-zero countercyclical capital buffer, a buffer rate for systemically important banks and a limit on loan-to-value for mortgages.

But these relatively new tools have their limits. For example, while they seem to work well in tightening the financial cycle, there is scarce evidence on the functioning of purely macro-prudential tools when it comes to easing conditions on the financial market. Paying more attention to house prices when conducting monetary policy allows one to utilise the main, well-tested tool of the central bank, the interest rate, which also has strong effects on house prices1. Therefore, we call for a syncretic approach: leaning against the wind not using the interest rate solely, but in tandem with macro-prudential measures and with the symmetry inherent to the inflation target in mind.

This article will look at the reasons for incorporating house prices into the CPI and why it makes sense from a macro-prudential and a monetary policy point of view to consider a broader measure of inflation that also includes house prices. It will also outline how statistical bureaus measure inflation related to owner-occupied housing and describe the experiences of the CNB in this area.

Macro-prudential reasons for incorporation

The Great Recession was famously preceded by a housing bubble in the US. In the aftermath of the crisis, many economists constructed early warning systems, in which house prices are often found to play a prominent role.2 The issue is also described in the classic book on financial crises, Manias, Panics, and Crashes: A History of Financial Crises by Aliber and Kindleberger,3 and in This Time Is Different: Eight Centuries of Financial Folly by Reinhart and Rogoff (2009).4 Especially in developed countries and during recent decades, house prices have tended to increase fast before a crisis, decrease markedly during the crisis and rise only gradually when the first signs of recovery kick in. The prominence of house prices among the large number of potential early warning indicators has led many commentators to stress the interaction between this variable and the stance of monetary policy. As with many other issues in the recent discussion on macro-prudential policy, however, it is perhaps not surprising that no clear consensus on the matter has yet been reached.

One stream of thought – represented, for example, by Assenmacher-Wesche and Gerlach5and Svensson6 – puts forward the notion that using monetary policy as a tool to stem an increase in house prices is too costly and detrimental to the welfare of the country. Williams1 conducts a meta-analysis of the empirical estimates reported in this literature and finds that a typical result implies a 1% loss in GDP associated with a 4% reduction in house prices delivered by monetary policy contraction. Often missing from the discussion, however, are the positive effects of such a policy on GDP and employment in times of downturn, when traditional CPI targeting implies less easing than what would be optimal if house prices were also taken into account. In other words, it is important to realise the symmetrical nature of inflation targeting even if the definition of the targeted statistical series changes.

Several studies have demonstrated the usefulness of incorporating financial stability considerations (including, most prominently, house prices) into monetary policy rules under inflation targeting. Because our focus is on the Czech Republic, we are mostly interested in evidence for small, open economies. Aydin and Volkan7 provide such evidence using a structural model calibrated for the case of South Korea. They find that paying attention to house prices pays off for monetary policy in terms of smoother business cycle fluctuations compared with conventional inflation targeting. Tentative evidence for the Czech Republic is presented by Žáček,8 who uses a similar structural model and finds that incorporating financial variables (including house prices) into the monetary policy rule helps macroeconomic stability in terms of the implied volatility of inflation and output. Therefore, we cannot discard the merit of Czech monetary policy potentially leaning against the wind of change in house prices.

Conceptual reasons for incorporation

As Goodhart eloquently puts it: “My dictionary (Longman) defines inflation as a fall in the value of money, not as a rise in the consumer price index. If I spend my money now on obtaining a claim on future housing services by buying a house, or on future dividends by buying an equity, and the price of that claim on housing services or on dividends goes up, why is that not just as much inflation as when the price of current goods and services rises?”9 Why not, indeed?

House prices are typically excluded from official inflation measures, although other goods that also provide a flow of future services – durables, such as motor vehicles and washing machines – are included. There is no clear theoretical reason for such treatment; rather, it is a convention that arises from intuition and convenience, as we will discuss in the next section. The argument supporting the conventional exclusion of house prices goes as follows: for houses, the investment component relative to the consumption component is larger than for other durables, such as cars. Moreover, a portion of the house value does not depreciate (think land), and is therefore often considered a good store of value. In spite of that, anecdotal evidence suggests that most households treat at least their first home purchase as pure consumption. Furthermore, it can be shown on theoretical grounds that the prices of all assets – including houses, stocks and bonds – should, in principle, be included in inflation if we are to measure the current cost of expected lifetime consumption (in the Fisherian tradition of a proper definition of intertemporal substitution), instead of merely current consumption.10

Put simply, every investment translates into future consumption. Just as we pay for the insurance of our property (such insurance is typically included in inflation measures), we pay for life insurance to protect our family against tragedies (life insurance is typically not included in inflation – something that is also the case in the Czech Republic, as Czech Statistical Office 2016 data shows).11 We invest in houses, stocks and bonds to ensure a good standard of living after we retire and provide for the education of our children – and, potentially, their own housing needs. All across the developed world, the importance of private allowances for retirement has increased in the wake of great demographic change, which ensures that less money will be available for retirees from the government under pay-as-you-go pension systems. In the Czech Republic, the stock market is relatively small, and houses represent the main investment item for the majority of the population. So, for our purposes, there is little reason to consider other indices of asset price inflation than house prices.

Aside from Alchian and Klein10 and Goodhart,9 zamany other authors have argued for the inclusion of house prices in the consumer price index. For example, Bryan et al12 show for the case of the US that the omission of house prices introduces an excluded goods bias and results in underestimation of the CPI by about 0.25 of a percentage point annually. Diewert and Nakamura13 also point to the need for a more direct measure of house price inflation in the official CPI index. They suggest that the recent period of low official inflation may be the result of mismeasurement of underlying consumer prices.

Measuring owner-occupied housing

It is widely known that house prices were included with a substantial weight in the official measure of inflation in the US prior to 1983. Other components of owner-occupied housing (a term used for the description of general costs related to home ownership often simply – but usually imprecisely – called ‘imputed rent’) employed in the CPI at that time were mortgage interest rates, property taxes, insurance rates and maintenance costs. It is less widely known, however, that the intention to separate the investment from the consumption part of home purchases was not the main reason for the change in the treatment of owner-occupied housing.

The paper accompanying the change and published by the Bureau of Labor Statistics (responsible for computing the CPI in the US),14 features a section entitled ‘Why the CPI must be changed’. In that section, the need to focus on shelter services instead of investment in housing is mentioned only in passing.

In contrast, the following problems are stressed:

  1. serious difficulties in obtaining data on house purchases not financed by mortgages insured by the Federal Housing Administration;
  2. financial innovations that make it harder to collect reliable data on mortgage rates;
  3. changes in tax laws that complicate the use of house prices in inflation measures;
  4. public distrust of the current measure of the CPI, given (among other things) the substantial volatility of house prices.

That is, the principal reason why house prices are typically excluded from the main inflation measure is empirical, rather than theoretical. It is difficult to collect reliable data on house prices, especially at monthly frequency and without a significant delay, and the series tends to be more volatile than the other components of the CPI. But still, it is hard to entirely ignore home ownership and the associated costs in inflation measures (it should be noted at this point that owner-occupied housing is missing entirely from the HICP gauge published by Eurostat, although it is planned to be included in the future). In most countries, a large proportion of people live in their own homes, which means that the costs associated with housing are not directly observable in terms of market rents. There are four main approaches to measuring owner-occupied housing (the acquisitions, rental equivalence, user cost and payments methods), which we outline in table A. Table B provides a few examples of countries that use the different approaches.

The acquisitions approach covers all expenses of households connected with home purchases. It is usually formulated in ‘net’ form, which means that transactions between households are ignored and only purchases of dwellings new to the household sector (typically from developers) are included. Other aspects taken into account are costs associated with reconstruction, repairs and maintenance, as well as insurance and property charges. More details about this method are available, for example, from Eurostat publications.15 The method is currently employed by Australia and New Zealand, which means that the headline CPI inflation numbers of these countries directly include the prices of (new) houses, although commonly with a negligible weight.

Table A: Components of owner-occupied housing

Component Acquisitions Rental equivalence User cost Payments
House purchase Yes      
Property rates and charges Yes   Yes Yes
Owner-occupied rents   Yes    
Owner-occupied user costs     Yes  
Mortgage interest charges       Yes

Source: Adapted from Woolford, K, Owner-occupied housing: an exploration of alternative treatments of owner-occupied housing in a CPI, Technical Advisory Group Meeting report (February 17–19, 2010), International Comparison Program.

The rental equivalence approach uses the concept of imputed rent, in which the statistical bureau in charge of computing the CPI constructs the hypothetical rent paid by homeowners to themselves, typically by using the market rents observed for homes with similar characteristics. This method is problematic for countries where the rental market is not well developed. In any case, it is – to the best of our knowledge – the most widespread approach, and is used by, for example, the US, Germany, Switzerland and Norway. More details about the method – particularly its US incarnation – are provided by McCarthy et al in a 2015 report published by the Federal Reserve Bank of New York.16

The user cost approach is probably the most technically sophisticated one, and is computed as the costs of acquiring the house at the start of the period, plus all fees, taxes, mortgage payments and repairs during the period, minus the price of the house at the end of the period (the price for which the house could be sold, thus reducing the user cost). Because of the complexity of the method, many variants exist, some of which are used by Canada, Ireland and Sweden. In the case of Sweden, the inclusion of mortgage rates in the official CPI measure delivers the price puzzle: an increase in the monetary policy rate leads to inflation in the short run by definition (see Rusnák et al for a survey).17 In consequence, the Riksbank has to use an alternative measure of inflation with fixed mortgage rates, and is not happy with it.18

Finally, the rarely used payments approach (also called the cashflow approach) focuses on actual expenses associated with home ownership, such as property taxes, reconstruction and repairs, insurance, and mortgage payments, which, among other things, means that the price puzzle problem occurs here as well. The payments approach is used, for example, in Austria. Many other countries – such as Belgium, France, Italy, Spain and the UK – do not account for owner-occupied housing in their headline CPI inflation figures at all.

Table B: Examples of the treatment of owner-occupied housing

Approach Countries
Net acquisitions Australia, New Zealand
Rental equivalence Denmark, Germany, Netherlands, Norway, Switzerland;
Japan, Mexico, South Africa, US
User costs Iceland, Ireland, Finland, Sweden;
Canada
Payments Austria
Headline CPI inflation measure excludes owner-occupied housing Belgium, Estonia, euro area, France, Greece, Italy, Luxembourg, Poland, Portugal, Spain, UK;
Argentina, Brazil, China, India, Indonesia, South Korea, Russia, Saudi Arabia, Turkey

Sources: Boldsen, C (2011): Workshop on Consumer Price Indices (workshop at the United Nations Economic Commission for Europe, UNECE, Istanbul, Turkey, October 10–13, 2011); Eurostat (2012)15; Organisation for Economic Co-operation and Development (OECD), Methodological Notes: Compilation of G20 Consumer Price Index (February 2015); OECD, Methodological Notes for OECD CPI News Release (December 6, 2016); and national statistical bureaus.

The Czech experience

According to the latest available Eurostat statistics, close to 80% of Czechs live in their own houses and apartments, which is substantially above the European Union average. Moreover, the typical Czech spends 26% of her disposable income on expenses associated with housing, the highest percentage among all the Organisation for Economic Co-operation and Development (OECD) countries. These two facts underline the importance of accounting in the CPI for costs associated with the housing that one owns, which the Czech Statistical Office has been attempting to do for two decades. The changes in the definition of owner-occupied housing in the Czech CPI are described in table C.

The method of the Czech Statistical Office has traditionally been based on rental equivalence, although not on the typical ‘hedonic’ approach that approximates housing costs by using the market rent of a dwelling with similar characteristics. Prior to 2007, owner-occupied housing was defined as payments in dwellings of housing co-operatives. Since 2007, the Czech Statistical Office has assigned non-zero weights to construction work, including materials and inputs in residential buildings. A major change in the philosophy of Czech owner-occupied housing came in 2015, when the figure started to cover purchases of new dwellings as well, albeit with a small weight (8%). The weight of the latter item doubled in 2017.

Although the owner-occupied housing item in the Czech CPI is still described as being computed by the rental equivalence approach,11 in the terminology introduced above, we perceive it as being closer to the definition of the net acquisitions approach. The intention of the Czech Statistical Office is to gradually converge to the definition of owner-occupied housing prepared by Eurostat, which also employs the net acquisitions approach. A side-effect of this definition is the relatively small weight of the entire item in the Czech CPI: owner-occupied housing accounts for about 9% of the index, compared with 24% in the US, 15.6% in Japan, and 12.2% in the UK (an alternative index with owner-occupied housing; headline CPI inflation in the UK excludes home ownership costs).

Table C: The treatment of owner-occupied housing by the Czech Statistical Office

Period Component of owner-occupied housing Weighting
Until 2006 Payments in dwellings of housing co-operatives 100%
2007–2011 Construction work, including materials 77%
Payments in dwellings of housing co-operatives 23%
2012–2014 Price of construction work, including materials 41.5%
Inputs in residential buildings 41.5%
Payments in dwellings of housing co-operatives 17%
2015–2016 Construction work, including materials 38.2%
Inputs in residential buildings 38.2%
Payments in dwellings of housing co-operatives 15.6%
Purchases of new dwellings 8%
Since 2017 Self-repair and overhaul 25.0%
Maintenance, reconstruction and renovation 52.7%
Real estate brokerage 6.0%
Purchases of new dwellings 16.3%

Note: In the future, The Czech Statistical Office intends to further reduce the difference between its definition of owner-occupied housing and the definition used by Eurostat.

As discussed in a 2016 ECB technical report,19 the weight of housing indexes based on net acquisitions is typically much smaller than the weight of imputed rentals, because the latter cover not only new, but all owner-occupied, dwellings. This is a problem particularly for countries where people do not move often, such as the Czech Republic.

Figure 1 shows that the index of owner-occupied housing for the Czech Republic – computed according to the Eurostat definition, which is nevertheless very close to the current definition of the Czech Statistical Office – does not display much cyclical variation, and is only mildly correlated with house prices. The index includes prices of dwellings new to the household sector, but only with a small weight. Moreover, it excludes prices of land and dwellings sold from one household to another, which is how most transactions in the housing market take place – in the Czech Republic, it is 90%, according to Eurostat.

We can also see that prices of new dwellings show considerably less variation than prices of existing dwellings. The index of new dwellings did not capture, for example, the large decrease in house prices on the secondary market in 2009. Prices of existing dwellings provide a better early-warning signal because individual households are more sensitive to changes in sentiment than developers, who are more likely to be able to wait and see before selling.

Figure 1: Owner-occupied housing does not capture the cyclicality of house prices

Figure 1: Owner-occupied housing does not capture the cyclicality of house prices
Source: Czech Statistical Office

For these reasons, it can be argued there is merit in giving non-zero weight to prices of existing dwellings. In the first half of 2016, the CNB started to compute for its internal purposes an experimental index of broader inflation including house prices along with the traditionally defined owner-occupied housing (CPIH). The weight of existing dwellings was set to 14%, which is a significant share (especially in comparison with owner-occupied housing based on the net acquisitions approach), but still smaller than the weight used in the US CPI prior to 1983. The weight is determined by the share of consumer expenditure on existing dwellings and is consistent with the way the weights for the other components of CPI are computed. Weights for individual categories in Czech CPI and CPIH are shown in table D. An important issue that we do not tackle are the regional differences in the development in house prices, which may have important macro-prudential implications. Nevertheless, regional heterogeneity is not a problem specific to house prices, as the development of individual components of CPI differ across regions, too.

Table D: Components of Czech CPI and CPIH

Coicop* Title Weight in CPI Weight in CPIH
1 Food and non-alcoholic beverages 18.1% 15.5%
2 Alcoholic beverages and tobacco 9.3% 8.0%
3 Clothing and footwear 3.9% 3.4%
4.1 Actual rentals for housing 3.5% 3.0%
4.2 Owner-occupied housing (including new dwellings) 8.7% 7.5%
04.x Existing dwellings 0.0% 14.0%
04.y Other expenses on housing, water, electricity, gas and other fuels 12.9% 11.1%
5 Furnishings, household equipment and routine maintenance 5.8% 5.0%
6 Health 2.3% 2.0%
7 Transport 10.1% 8.7%
8 Communication 3.1% 2.6%
9 Recreation and culture 9.0% 7.7%
10 Education 0.6% 0.5%
11 Restaurants and hotels 5.8% 5.0%
12 Miscellaneous goods and services 6.9% 6.0%

Notes: * Classification of individual consumption by purpose (Coicop); CPI = consumer price index; CPIH = CPI including prices of existing dwellings
Source: Czech Statistical Office; Czech National Bank

Figure 2 shows that the broader inflation measure would call for significantly more expansionary monetary policy in 2009, 2010 and 2012,20 but somewhat tighter monetary conditions in 2015 and 2016 – although, in the latter case, even broader inflation was safely below the CNB’s target of 2%. While the difference between the CPI and the CPIH is substantial, it is not dramatic, and CPIH targeting would not radically redefine the conduct of Czech monetary policy. If anything, it would make it more aggressively countercyclical.

Figure 2: Accounting for house prices changes the profile of inflation markedly

Figure 2: Accounting for house prices changes the profile of inflation markedly
Source: Czech Statistical Office; Czech National Bank.

Time for change?

We do not argue, however, that the time has come to replace the current inflation measures with broader indices that fully incorporate house prices. Rather, we consider such broader measures of inflation to be useful supplementary indicators, similar in this function to core inflation, which, in contrast, constitutes a narrower gauge than headline CPI inflation. Goodhart’s assessment (page F338)9 za resonates with our thoughts on the matter: “Continuity, certainty and simplicity all argue against chopping and changing existing procedures. So, in the conclusions, we do not argue for replacing the present measures, but of paying rather more attention to accompanying, alternative measures which do give a more appropriate weighting to asset prices.” A major technical limitation of the broader index is that, at present, reliable data on house prices are available only at quarterly frequency and with a significant lag.

Consequently, the CNB has not dropped its focus on headline CPI inflation in favour of a broader measure that includes house prices, and does not plan to do so in the foreseeable future. But the broader measure, the CPIH, has become one of the important indicators that the CNB’s board considers when it decides on changes in the monetary policy stance. In a well-known and colourfully titled article, ‘Measuring Inflation: The Core Is Rotten’,21 the president of the Federal Reserve Bank of St Louis, James Bullard, criticises the US Federal Reserve’s focus on core inflation and argues for paying more attention to a broader gauge of inflation. To paraphrase Bullard’s provocative statement (page 223),21 we can say the following: one immediate benefit of dropping the sole emphasis on an inflation measure that excludes house purchases would be to reconnect central banks and statistical bureaus with households and businesses, who know price changes when they see them.


1. Williams, JC, ‘Measuring Monetary Policy’s Effect on House Prices’ in FRBSF Economic Letter 2015-28 (August 31, 2015), Federal Reserve Bank of San Francisco.

2. See, among others: Babecký, J, T Havránek, J Matějů, M Rusnák, K Šmídková and B Vašíček (2011), Early Warning Indicators of Economic Crises: Evidence from a Panel of 40 Developed Countries, CNB Working Paper 2011/08 (2011); Reimers, H, ‘Early Warning Indicator Model of Financial Developments Using an Ordered Logit’ in Business and Economic Research, Volume 2(2) (2012), Macrothink Institute, pages 171–191; Babecký, J, T Havránek, J Matějů, M Rusnák, K Šmídková and B Vašíček, ‘Leading Indicators of Crisis Incidence: Evidence from Developed Countries’ in Journal of International Money and Finance, Volume 35(C) (2012), European Central Bank, pages 1–19; Antunes, A, D Bonfim, N Monteiro and P Rodrigues, ‘Early Warning Indicators of Banking Crises: Exploring New Data and Tools’ in Economic Bulletin, April 2014, Banco de Portugal; Laina, P, J Nyholm and P Sarlin, Leading indicators of systemic banking crises: Finland in a panel of EU countries, Working Paper Series 1758 (February 2015), European Central Bank; and Tölö, E, ‘Early Warning Indicators of Banking Crises’ in Bank of Finland Bulletin 2/2015, Bank of Finland.

3. Aliber, R and C Kindleberger, Manias, Panics, and Crashes: A History of Financial Crises, seventh edition (Palgrave Macmillan, 2015).

4. Reinhart, C and K Rogoff, This Time Is Different: Eight Centuries of Financial Folly (Princeton University Press, 2009).

5. Assenmacher-Wesche, K and S Gerlach, ‘Monetary policy and financial imbalances: facts and fiction’ in Economic Policy, Volume 25, issue 63 (July 1, 201), Oxford University Press, pages 437–482.

6. Svensson, L, ‘Inflation Targeting and “Leaning against the Wind”’ in International Journal of Central Banking, Volume 10(2) (2014), pages 103–114.

7. Aydin, B and E Volkan, Incorporating Financial Stability in Inflation Targeting Frameworks, IMF Working Paper 11/224 (2011), International Monetary Fund.

8. Žáček, J, Should monetary policy pay attention to financial stability? A DSGE approach, master’s thesis (Institute of Economic Studies, Charles University in Prague, 2016).

9. Goodhart, C, ‘What Weight Should Be Given to Asset Prices in the Measurement of Inflation?’ in The Economic Journal, Volume 111(472) (June 2001), Royal Economic Society, pages F335–356.

10. Alchian, A and B Klein, ‘On a Correct Measure of Inflation’ in Journal of Money, Credit and Banking, Volume 5(1) (1973), pages 173–191.

11. Czech Statistical Office, Consumer Price Indices (User’s Methodological Manual) 2016 technical report.

12. Bryan, MF, SG Cecchetti and R O’Sullivan, Asset Prices in the Measurement of Inflation, NBER Working Paper 8700 (January 2002), National Bureau of Economic Research.

13. Diewert, WE and AO Nakamura, Accounting for Housing in a CPI, Working Paper 09-4 (March 2009), Federal Reserve Bank of Philadelphia.

14. Gillingham, R and W Lane, ‘Changing the treatment of shelter costs for homeowners in the CPI’ in Monthly Labor Review, June 1982, pages 9–14.

15. Eurostat, Detailed Technical Manual on Owner-Occupied Housing for Harmonised Index of Consumer Prices (March 2012); Eurostat, Methodological Manual Referred to in Commission Regulation (EU) No 93/2013 (February 2013).

16. McCarthy, J, R Peach and M Ploenzke, The Measurement of Rent Inflation, Staff Report Number 425 (revised December 1, 2015), Federal Reserve Bank of New York.

17. Rusnák, M, T Havránek and R Horváth (2013), ‘How to Solve the Price Puzzle? A Meta-Analysis’ in Journal of Money, Credit, & Banking, Volume 45(1) (February 2013), pages 37–70.

18. Jansson, P, ‘Time to Improve the Inflation Target?’ (speech by deputy governor of the Sveriges Riksbank at Handelsbanken, Stockholm, Sweden, December 3, 2015); Johansson, J, ‘How Is Inflation Measured?’ in Economic Commentaries, Number 5 (2015), Sveriges Riksbank.

19. European Central Bank, ECB Estimates of the Impact of Including Owner-Occupied Housing Price Indices into the Headline HICP technical report (July 29, 2016).

20. In 2012, the Czech National Bank reached the zero lower bound on the policy rate, and in 2013, as a means of additional monetary easing, it committed to keep the Czech koruna from appreciating beyond CZK27 to €1 ($1.07). The exchange rate commitment has significantly benefited the Czech economy (Opatrný, M, Quantifying the Effects of the CNB’s Exchange Rate Commitment: A Synthetic Control Method Approach, IES Working Paper 17/2016 (2016), Charles University, Prague). Had the Czech National Bank targeted the CPIH, instead of the CPI, the commitment would probably have been introduced earlier.

21. Bullard, JB, ‘Measuring Inflation: The Core Is Rotten’ in Federal Reserve Bank of St Louis Review (July 2011), pages 223–234.