Flexible In ation Targeting and the House Price Boom Jonas Zdrzalek

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Flexible Ination Targeting and the House Price Boom Jonas Zdrzalek

Thesis submitted for the degree of Master of Philosophy in Economics

30 Credits

Department of Economics Faculty of Social Science

University of Oslo

November 2019

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Flexible Ination Targeting and the House Price Boom

Jonas Zdrzalek

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c

2019 Jonas Zdrzalek

Flexible Ination Targeting and the House Price Boom http://www.duo.uio.no/

Printed: Reprosentralen, University of Oslo

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Acknowledgements

I want to express my gratitude to all people who supported me writing this thesis.

First, I thank my supervisor Martin Blomho Holm for outstanding supervision. Your comments and thorough guidance were incredible helpful during the process.

Furthermore, I acknowledge use of the dataset described in Mack et al. (2011). Without this data the thesis may never have been written.

I am also grateful to my friends at the University of Oslo. I enjoyed a good working envi- ronment in which support never lacked. I am particular grateful to Max. The discussions about economics and life in general were always enlightening.

Moreover, I want to thank my family, who always supported me when I needed it. Special thanks to my brother Lukas, who helped me with valuable proofreading. I learned a lot from your comments and I will never stop learning something from your great writing style.

Finally, I am very grateful to Henrike. You took care of everything while I was working on this thesis. Without you I would not have been able to perform on the same level.

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Abstract

I use panel data to estimate the long-term eects of the implementation of exible ination targeting on house prices. Additional to the standard dierence-in-dierences regression, I apply a dynamic dierence-in-dierences estimation which allows the normalization of the various treatment paths of the countries. My major result is that the adoption of exible ination targeting has a positive eect on the house price growth. The coecients are signicant 9 years after til 15years after the regime shift and are not sensitive to any modications regarding estimation design, treatment group or control group. The result supports the idea that ination targeting is amplifying house price booms rather than depressing them. Policymaker in central banks relying on exible ination targeting should take this as a guideline to closely monitor the development of house prices. To the best of my knowledge, this is the rst paper analyzing the eects of exible ination targeting on house prices by applying a dynamic dierence-in-dierences design.

Keywords: Ination Targeting, House Prices, Panel Data, Dynamic Dierence-in-Dierences

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1 Introduction

Prior to the global nancial crisis, ination targeting was praised as playing a major role in the phase of low and stable ination during the Great Moderation (see Walsh (2009) or Svensson (2010)). Nevertheless, not only the global nancial crisis in 2007/2008 but also the dotcom boom and bust of the early 2000s displayed the fact that nancial crises with uctuating asset prices were not a relic of the past.

Commentators around the globe started to question central banks. They argue that ination targeting itself is contributing in enhancing asset price booms as they overlook developments which are not relevant for the ination in short- to medium-run (see Borio et al.(2003) andBorio & White(2004)). Some academics even go a step further and call for the termination of ination targeting. Stiglitz(2008) claims that ination targeting is failing the test it faces. He calls for the abandonment of ination targeting in developed and developing countries.

On the other hand, policymakers have agreed on the consensus that while price stability is fundamental in achieving nancial stability it is not a sucient condition to ensure it.

King (2009) says that the Great Recession showed that price stability does not guarantee the soundness of the economy. He requires monetary authorities to rene and sharpen instruments used to ensure nancial stability. Constancio (2017) recalls that the main question after the nancial crisis was not if nancial stability concerns should be implemented into central banking but rather to what extent they should be included.

Knoll et al. (2017) acknowledge that house prices have been increasing since the 1960s.

Nonetheless, this time span experienced some contractionary and expansionary phases with a drastic surge in prices from the mid-1990s to present. The nancial crisis led only to a small slowdown of house price increases.

Knoll et al.(2017) identify two main reasons for the increase since the 1960s, the xed land supply and housing as a superior good. Moreover, various other factors try to explain the distinct house price growth. Demographic changes, the impact of nancial liberalization including the deregulation of mortgage markets (Cardarelli et al. (2008)), the easing of credit standards (Dell'Ariccia et al.(2012)) and the decreasing trend of real interest rates (Girouard et al. (2006)) are oered as reasons for the house price developments.

There is a body of literature investigating the eects of ination targeting. However, this literature is mainly concerned with the eects of ination targeting on macroeconomic variables such as ination itself and the output of an economy. For example, Vega &

Winkelried (2005) nd that ination targeting resulted in lower ination levels and lower ination volatility in the countries that shifted their regime, whileBall & Sheridan(2004) argue that ination targeting is not benecial for the economic performance of a country compared to non-ination targeters.

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In this thesis, I conduct an empirical investigation justied by theoretical foundations to study the eects of exible ination targeting on the house price growth. I do not deny that the previously named factors played a crucial role in the developments but I want to examine if the adoption of a new monetary policy regime can explain a part of the increase in house prices.

I use a standard and dynamic dierence-in-dierences approach (Abraham & Sun(2019)) to investigate the results in this quasi-experimental setting.

My study incorporates 29countries for the period of 1975 to 2019. 14of these countries adopted ination targeting during this period. I nd a positive average treatment eect on the treated countries of ination targeting on house prices after the implementation with the coecients being signicant about 9 years after adoption.

The paper is structured in the following way. Chapter2provides a historical introduction on the evolution of monetary policy regime in the past century. Section 3 displays the theoretical foundation on how monetary policy transmits to house prices. Further, I specify the peculiarities on why ination targeting aects house prices. This also includes a stylized asset pricing model showing the eects. Chapter4is giving detailed description on how I obtained the data to perform my empirical analysis. I also emphasize the process to select my sample. Section 5presents the empirical analysis of this thesis and the main results. I further challenge my assumptions to enhance the validity of the outcome. The last chapter 6 summarizes the main ndings and gives an outlook on further research.

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2 Brief History of Monetary Policy Regimes

Since the 1990s, the predominant monetary policy regime is exible ination targeting.

Central banks around the globe, both in developed and developing countries, started to adopt this system in the 1990s and the number is still increasing.

In this chapter, I present a brief history of how monetary policy ended up with this regime and which other regimes were left behind. I only describe each of the previous monetary policy systems briey before providing a detailed description of the current regime. The review begins with the gold standard which started to be widely used by industrial countries in the late 19th century. Then I introduce the Bretton Woods system and follow with the transition period in the 1970s and 1980s. The section concludes with a description of exible ination targeting.

2.1 Gold Standard

The gold standard provided the framework for domestic and international monetary policy since the 1870s. The idea was that domestic currencies could be exchanged into physical gold on demand and were interlinked internationally via pegging at a xed parity. Central banks adjusted the interest rate to attract funds from abroad to defend the exchange rate peg. The theoretical frameworks underlying the gold standard were the "price-specie-ow mechanism" invented by the Scottish economist David Hume and "the quantity theory of money" of which the rst ideas go back to 16th century.

Eichengreen (1995) argues that the gold standard worked ne during the period before World War I because of credibility and cooperation. In the interwar period these two factors were undermined by the scarcity of gold and the non-cooperative international behavior. Due to the increased uncertainty after the break out of the global nancial crisis in 1929 central banks started to hoard gold and the international capital markets was under severe pressure. The gold standard collapsed and one after the other country abandoned the peg.

2.2 Bretton Woods

The Bretton Woods system was set up after World War II. It included establishing the International Monetary Fund (IMF) and the World Bank. The Bretton Wood system was a xed exchange rate system in which the United States pegged the US Dollar to gold while all other countries pegged their currency to the US Dollar. This essentially means that the United States demanded gold reserves and all other participating countries needed US Dollar as a reserve asset. The IMF had the role as an international lender of last resort to cover imbalances. The theoretical frameworks underlying the Bretton Woods do not deviate substantially from the previous theories supporting the gold standard.

The reasons for the collapse of the Bretton Woods can be described by the so-called Trin-

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Dilemma (Trin(1961)). The demand for US Dollar was high as the world's largest source of reserves. In order to maintain that, the US had to run a balance of payments decits by issuing bonds and sell them abroad. This in turn led to the case that the US Dollar foreign reserves were larger than the gold reserves the US possessed. This eroded the condence in the US Dollar, and it was no longer accepted as the world's reserve currency. Eventually, other countries started to hoard gold reserves and the convertibility of US Dollar to gold was no longer credible. The US government suspended gold convertibility in 1971, which terminated the Bretton Woods system.

2.3 Transition period 1970s and 1980s

The period in the 1970s and 1980s are centuries in which the monetary operational frameworks from a today's perspective have been overloaded with an unnecessary complex monetary policy implementation. Bindseil(2016) gives the following examples for this: reserve requirements with various ratios with frequent changes, various excess reserve concept, poorly dened standing facilities and direct control measures such as caps on retail deposit remuneration. Additionally, in the 1970s the US was experiencing a period of high ination and high unemployment at the same time which called into question "common"

knowledge such as the Phillips curve. The ination period ended drastically with the so called "Volcker disination". A drastic increase in nominal interest rate in the late 1970s lowered the ination and eventually ended in a recession with massive unemployment in the early 1980s.

Simultaneously, the academic profession shifted towards new aspects. A dominant feature of this development was the Lucas Jr (1976) critique which called into play the notion of rational expectation and structural and micro-founded macroeconomic models. The main theoretical model developed after the Lucas critique was the Real Business Cycle (RBC) theory. It included the idea of micro-foundation into a dynamic stochastic general equilibrium model. Prices and wages are fully exible, and the economy is supply driven by total factor productivity (TFP) shocks. The view upon monetary policy was that it is still neutral without aecting real variables, even though empirical evidence suggested that changes in monetary policy seems to impact output and employment in the short run.

2.4 Flexible Ination Targeting

Since the 1990s ination targeting is the most prominent monetary policy regime. New Zealand was the rst country to introduce the framework in 1990 with many more to follow. The strategy is widely agreed to have been a success after ending periods of high ination in the 1970s and 1980s. The theoretical foundations for ination targeting are given by the New Keynesian models (NKM). The New Keynesian theories are a progres- sion of the RBC literature as they are also dynamic stochastic general equilibrium models.

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Unlike in the RBC models monetary policy is not neutral any more. The NKM introduces sticky prices and monopolistic competition, resulting in non-neutrality of monetary policy. Nevertheless, the long-run neutrality of monetary policy is still preserved. Gali (2002) points out that in the simple benchmark model a constant, zero ination rate is optimal.

Svensson(2010) characterizes ination targeting using three main factors. The rst aspect is an announced numerical ination target. The second point is that monetary policy decision should lead to the convergence of the ination forecast to this numerical target.

The third element is a high degree of accountability, credibility and transparency.

Blanchard et al. (2010) says that only a "few central banks, if any cared only about ination." That is the aspect which makes ination targeting exible. This aspect of exible ination targeting is outlined bySvensson (2009) and Svensson(2010). He points out that exible ination targeting is not only concerned with ination but also the wish to stabilize output. Svensson (2009) denes stabilizing the real economy as sustain

"resource utilization around a normal level[...].". Moreover, central banks follow the idea that a return to the stable target should not be immediately but rather over a longer period. Blanchard et al.(2010) further say that variation in the headline ination caused by, for example, oil prices, were allowed as long expected ination followed the designated path. Additionally, they outline that many central banks kept an eye on other asset prices (house prices, stock prices, exchange rates) apart from the impact they have on ination.

Most of them did so with anxiety.

Although that after the nancial crisis in 2007/2008 some new features to the monetary operational framework were added such as unconventional monetary policies, the monetary policy regime did not change. Nowadays many central banks in developed countries are more concerned about falling into a deationary trap while being constraint at the eective lower bound. Regardless of this development, the main target remains a low and stable ination rate combined with a stable output gap.

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3 Theory on Flexible Ination Targeting and House Prices

This section gives an general idea why monetary policy has an impact on house prices.¹ I investigate the reasoning why exible ination targeting has a positive impact on house prices. The rst sub-section gives an intuitive approach while the second displays the idea with a stylized asset pricing model. The nal sub-section examines the empirical facts of the theory.

3.1 Intuition

3.1.1 User cost of Housing

In this section, I provide the rationale why monetary policy has an impact on real estate prices. A starting point of this analysis should be the user cost of capital or in this case also called the user cost of housing. Jorgenson (1963) introduces the idea of the user cost of capital and laid the groundwork for further research. However, I use the framework of Poterba (1984) which is especially tailored towards user cost of housing. He introduces the following equation for the user cost ucas

uc=ph∗[δ+κ+ (1−θ)∗(i+µ)−π^e_H] (1) where phis the purchase price of one unit of housing, δ is the constant depreciation rate, κ is the share of current value which is spent on maintenance and expenditures, θ is the marginal income tax rate all individuals face, i is the nominal interest rate µ is the tax liabilities that occur from the housing structure and π_H^e is the expected ination of house prices, which can also be regarded as the expected appreciation of house prices.

As this paper is only concerned about the dynamics of the user cost of housing with regard to monetary policy, I simplify equation 1 by (i) assuming that the maintenance and expenditures cost κ can be included into the depreciation rate δ and (ii) assuming that the tax liabilities µ of a housing structure are equal to zero. The equation I use for further investigation has the following form:

uc=ph∗[δ+ (1−θ)∗i−π_H^e] (2) Poterba(1984) emphasizes that the user cost of housing plays a crucial role in determining the demand for residential capital. An equilibrium exists if home-owners equalize their marginal rental value for a given stock of residential properties with the user cost described in equation 1 and 2.

1The fact that monetary policy has an impact on real variables is agreed within the literature. See for example Rigobon & Sack(2004),Romer & Romer (2004) orChristiano et al. (2005)

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Mishkin (2007) who investigates house prices and the monetary transmission channel simplies equation 2 further to write the equation in real terms as

uc=ph∗[δ+ ((1−θ)∗i−πê)−(πê_H −πê)] (3) where πê is the expected ination and it follows that ((1−θ)∗i−πê) are the after tax real interest rates and (π_Hê −πê)is the expected real appreciation of residential prices.

An important issue (especially if one talks about monetary policy and residential prices) discussed in both,Poterba(1984) andMishkin(2007) is to identify the horizon of mortgage interest rates correctly. Poterba (1984) says that "while long-term mortgage interest rates are important, the short-term interest rate enters[...]" the equation of the user cost.

This means that i is dened as the short-term nominal interest rate following Porteba's notation.

Nevertheless, long-term interest rates aect house prices through the same arbitrage logic.

3.1.2 House price shift

After dening the user cost of housing three main channels can be identied through which monetary policy eect housing demand and hence house prices.

The rst channel through which monetary policy can aect house prices is through the interest rate. An interest rate adjustment leads to a change of the user cost of housing. If, for example, expansionary monetary policy reduces the user cost of housing, the demand for housing increases. The increased demand for housing is followed by a rise of the housing price.

A second eect of user cost on house prices can be inferred from equation 3. Monetary policy can inuence the expected real rate of appreciation of residential property(π_H^e −π^e). Mishkin (2007) emphasizes that an expected future monetary policy shock aects the expected house price and hence the expected real rate of appreciation of house prices.

If, for example, home owners expect an expansionary monetary policy shock in the future, the real rate of appreciation could be increased. In this case, the user cost of housing would decline followed by increased demand and house prices.

Mishkin (2007) identies the balance sheet channel as the third channel through which monetary policy aects house prices. This channel is known quite well from evaluating monetary transmission mechanisms.²

The rst strand of balance sheet literature follows the idea that nominal, not only real

2Often it is used to assess the eect of balance sheet lengthening on household's consumption spending or commercial banks' expanding their credit activity.

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interest rates can impact house prices. The literature emphasizes that the relevant rate for debt contracts with variables rates is the nominal interest rate. If the central bank alters the policy rate, the net wealth of the household changes. For example, an expansionary monetary policy shock leads to an increased net wealth of the household as their mortgage payments decline. A borrowing-constrained household is then able to borrow additional funds and housing demand followed by house prices should increase. This eect only applies to homeowners with a variable mortgage rate.

The other body of balance sheet literature is valid for every type of mortgage rate. If expansionary monetary policy, as described in the previous paragraphs, increases the house prices the balance sheet of the homeowner lengthens. A borrowing constrained household is able to pledge more collateral and hence generate new funds. Housing demand and house prices should increase accordingly.

An important aspect to mention is the inelastic supply of housing. If that would not be the case, then most prots generated from additional housing demand would be arbitraged away by housing suppliers. Poterba (1984) shows that house prices increase more if the inelasticity of housing supply is larger.

The literature acknowledges that the main factor for the inelastic supply of housing is the inelastic supply of land used for residential properties.³ Mishkin(2007) andGreen et al. (2005) emphasize that regulations restrict the supply of housing. Davis & Heathcote (2007) and Davis & Palumbo(2008) nd that land prices are increasing faster than house prices and show that the importance of land for determining the value of residential property increased signicantly. Knoll et al. (2017) determine land prices as one of the two major factors of increasing house prices since the 1960s.

Furthermore,Bjørnland & Jacobsen(2010) nd empirical evidences that the role of house prices for the monetary policy transmission mechanism has increased consistently. They point out that house prices do react strongly to monetary policy shocks.

3.1.3 Peculiarities of Flexible Ination Targeting

The previous parts of this sub-section analyzed how monetary policy in general can have an impact on house prices. In this section, I discuss why exible ination targeting has a positive eect on house prices.

The rst rationale why exible ination targeting has a peculiar eect on peaking house prices is the combination with disination. Table 1 shows how countries reduced their ination rate after implementing exible ination targeting. The table displays the fact that besides a few exceptions, ination rates declined after countries adopted exible

3Housing structures and the land it is built on are usually not separable

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ination targeting. But the important part for the house prices is not the disination Table 1: Ination targeters

Country Ination targeting Ination rate Ination rate Ination rate

adoption date at adoption 2009 2017

New Zealand 1990 3.3 0.8 1.9

Canada 1991 6.9 0.3 1.6

United Kingdom 1992 4.0 2.2 2.6

Sweden 1993 1.8 -0.3 1.8

Australia 1993 2.0 1.9 1.9

Czech Republic 1997 6.8 1.0 2.5

Israel 1997 8.1 3.3 0.2

Poland 1998 10.6 3.8 2.0

Brazil 1999 3.3 4.9 3.4

Chile 1999 3.2 1.5 2.2

Colombia 1999 9.3 4.2 4.3

South Africa 2000 2.6 7.1 5.2

Thailand 2000 0.8 -0.9

South Korea 2001 2.9 2.8 1.9

Mexico 2001 9.0 5.3 6.0

Iceland 2001 4.1 12.0 1.8

Norway 2001 3.6 2.2 1.9

Hungary 2001 10.8 4.2 2.3

Peru 2002 -0.1 2.9

Philippines 2002 4.5 1.6

Guatemala 2005 9.2 1.8

Indonesia 2005 7.4 4.6 3.8

Romania 2005 9.3 5.6

Turkey 2006 7.7 6.3 11.1

Serbia 2006 10.8 7.8

Ghana 2007 10.5 19.3

Source: (i)Roger(2010) and AREAER (2019) (ii)OECD (2019), Ination (CPI) (indicator).doi:10.1787/eee82e6e-en (Accessed on 17 October 2019)

itself but rather the associated decline in interest rates.

In previous chapters the Volcker disination in the US was already mentioned. Goodfriend

& King(2005) cite Volcker in their article saying that it is crucial "[...] to convince people that we have a hold on ination and have created a climate in which interest rates, particularly long-term rates, will tend to go down [...]." The idea during the disination period was to stabilize ination and simultaneously lower interest rates.

The IMF (2014) acknowledges that until 1992 about 88 percent of US short-term real interest rate movements can be explained by monetary policy shocks. The IMF (2014) continues that the analysis for long-term rates exhibits a similar picture.

Furthermore, the IMF (2014) reports that in the "[...] early 1990s, central banks around

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the world adopted ination reduction policies that initially required tighter monetary policy stances, similar to the U.S. Federal Reserve's." Additionally, the IMF(2014) points out that in an increasing number of countries the described development was incorporated into the adoption of ination targeting.

Hence, it can be said that exible ination targeting which is usually combined with disination is followed by decline of the general level of short- and long-term interest rates. From previous sub-sections it is known that lower interest rates reduce user cost of housing and subsequently increase housing demand and house prices.

An announcement by a credible central bank to adopt exible ination targeting could be regarded as a future monetary policy expansion. The announcement includes a clear intention to reduce ination and interest rates in the long-run. Nevertheless, central banks' credibility around the world substantially increased through the implementation of exible ination targeting. Therefore, the immediate eect should be neglectable while over a longer horizon the inuence should be observable.

Another interesting aspect Case & Shiller (2003) make is that a considerable amount of people in their survey declare housing as an investment besides the primary physical need of a shelter. This eect may be even stronger if exible ination targeting reduces the general interest level. The idea is that returns on risk free assets decline while cost of obtaining a house declines as well.

Additionally, Case & Shiller (2003) cite various participants of their survey who lost condence in the stock markets and invested into residential property instead. Countries with a low share of risky investments may experience even stronger eects as agents shift their portfolio towards housing.

As mentioned earlier a strand of literature argues that not only real but nominal interest rate can inuence the housing demand of agents. Mayer in response to Case & Shiller (2003) shows that while nominal mortgage rates in the U.S. before the nancial crisis ⁴ reached a historic low level, real rates were comparable to the level in the 1970s (and not an exceptional case).

The IMF (2014) outlined that after disinationary actions the power of monetary policy to explain real interest rate swings declined. Applying this, exible ination targeting is not only responsible for lowering the general interest rate level through disination but also plays a signicant role in reducing the wedge between real and nominal interest rates. This follows from standard Wicksellian arbitrage logic wherer =i−πand ination targets are usually set to low one digit numbers.⁵ Borio & White (2004) summarize that disination "has naturally led to a decline in nominal interest rates."

4His graph terminates around 2003

5ris the real interest rate, iis the nominal interest rate andπis the ination rate

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Mayer in response to Case & Shiller (2003) additionally makes a point why nominal interest rates could aect housing demand. He refers to the idea that household may confuse nominal and real interest rates and thus assume that nancing housing demand is less costly in low nominal interest rate environments.

Piazzesi et al. (2007) develop a theoretical model which supports this idea. They design a framework which distinguishes between smart and illusionary households. The latter type suers from ination illusion. According to their model, these types of households invest heavily into residential properties during times of low nominal interest rates. The reason is that these households are not able to realize that nominal interest rates are low due to the low expected ination.

The second aspect why exible ination targeting has a peculiar eect on house prices is the positive correlation of central banks' policy rates with the business cycle. The new regime resulted in lower short-term output volatility and longer duration of business cycles (Borio & White (2004)). Additionally, they show that the "height of troughs and peaks respectively have been lower than in the past."

I acknowledge that this is a partly a global phenomenon, but I argue that ination targeters are distinct in some factors. Firstly, the primary goal of ination targeting is a low and stable ination rate on the short- to medium-term. This could lead to central banks neglecting developments that seems unrelated to the 2-3 year horizon but bare risk for macroeconomic outcomes over a longer horizon. For example, overlooking increasing house prices that do not unfold instantly into consumer price pressure.

Borio et al. (2003) add to this idea the "paradox of credibility" in which central banks

"can be the victim of their own success". Having long-term ination rates stabilizing around the objective, asset price booms may need longer to become visible in ination rates.

Additionally, in downswings of the economy, exible ination targeting can react much more aggressive and independent to shocks than other monetary policy regimes (e.g. xed exchange rate systems). In recessions, expansionary monetary policy would lower the policy rate to boost demand and stabilize ination. House prices would either experience much lower declines or remain constant, up to slight positive developments.

Figure 1 shows the development of average yearly real house price growth from exible ination targeters and non-exible ination targeters. The graph exhibits that real house prices in exible ination targeting countries recover quicker after the nancial crisis 2007/2008 compared to the control group.

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Figure 1: Average RHPI Percentage Change

3.2 Stylized Model

In this subsection I introduce a stylized model which supports the theoretical ideas outlined in the previous subsections. The model follows the framework explained inGranziera

& Kozicki(2015). The main purpose is to nd the fundamental price of housing and show how the price adjusts under a regime shift to exible ination targeting.

The starting point is to treat housing as a nancial asset. This nancial asset generates exogenous payos. Granziera & Kozicki(2015) also call this payo rent. I abstract from housing in the utility function or any other function it may have, e.g. as collateral or as store of value. The design is formalized as a Lucas tree type model (Lucas Jr, 1978).

This type of model enables me to obtain a pricing equation for the fundamental price of a risky nancial asset, here the house price.

Furthermore, I assume that the supply of housing is completely inelastic. This is a strong assumption but in the former sub-sections I emphasized a few reasons why the assumption bears some credibility especially in the short-run. The presumption allows to design house price uctuations only on households' demand change.

Lucas' model consists of a representative agent in an endowment economy who decides on a sequence of consumption and assets to maximize the value of the expected present

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lifetime utility. Formally, the representative household has the following intertemporal utility optimization problem:

maxct,ht

E₀

∞

X

t=0

β^tU(c_t) (4)

s.t.

c_t+p_th_t= (p_t+d_t)ht−1 (5) with c_t , h_t > 0. Consumption in period t is denoted by c_t, h_t is the share of housing purchased at time t, d_t is the stochastic dividend generated by the share of housing in period t and pt is the price of the share of housing in period t. The expectation at time zero of the household are denoted by E0 and β is the discount factor. The rst-order condition results in the central asset pricing formula:

pt=βE0

U⁰(ct+1)

U⁰(c_t) (pt+1+dt+1)

(6) Due to the aspect that housing is in xed supply I assume for simplicity h_t= 1.

Granziera & Kozicki (2015) "think of the dividends as rent, the stream of consumption and services that is derived from owning (and renting out) a house [...]." For the purpose of showing how the implementation of exible ination targeting aects the house price the dividends must be described explicitly. I dene the dividends as the following

dt+1 =ηt+1−rtpt (7)

where η_t+1 stands for the consumption and services generated from housing in period t+ 1 and rtpt is a simplied version of equation 2, the user cost of housing. Denoting the user cost of housing in period t makes sense if one thinks of them as the opportunity investment the household could have undertaken in periodt.⁶ Furthermore, I assume that the interest rate r_t uctuates around a certain level r. The uctuations are generated by the states of the economy. I dene the interest rate as

r_t =r+α∆c_t+1 (8)

where α is a factor which determines the responsiveness of the interest rate to the shock.

One can think of α as the correlation coecient of business cycles and the interest rates.

∆c_t+1 is dened as the change of consumption in period t+ 1. The economy is either in a good state (boom) with probability π or in a bad state (bust) with probability 1−π.

6It is important to note that the notation is not critical for the outcome. If one prefers to use the notationr_t+1p_t+1 the interpretation of the result remain unaected even though the numbers change.

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Plugging these assumption into equation 6changes it to:

p_t=πβE₀

U⁰(c^g_t+1)

U⁰(ct) (p_t+1+η_t+1−rp_t−α∆c^g_t+1p_t)

+ (1−π)βE₀

U⁰(c^b_t+1)

U⁰(c_t) (p_t+1+η_t+1−rp_t−α∆c^b_t+1p_t)

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Moreover, I assume that utility follows a constant elasticity of substitution function of the form:

U(c) = c^1−γ 1−γ

I set the elasticity of substitution γ equal to 1 which means that the function converges to,

u(c) =ln(c)

withu⁰(c) = ¹_c. Additionally, I assume that the price of a house in the next period equal to 1, the service and consumption generated from housing equal1 as well, the consumption in period tequals1and the probability of being in a good state equals0.5.⁷ Furthermore, c_t+1 evolves accordingly:

c_t+1 =







1 +δ Good State 1−δ Bad State

That means that the change of c_t+1, ∆c_t+1 is just equal to δ in the good state and equal to −δ in the bad state. Using all these assumptions the equation 9 simplies to:

p_t = β 2

1

1 +δ(2−rp_t−αδp_t) + 1

1−δ(2−rp_t+αδp_t)

(10) After assuming values for the remaining parameters β and δ I can solve the equation for p_t for given values of α and for given values of r. Figure 2 shows the solution for the house price p_t.

Two main points can be observed from the solution of the stylized model. The rst aspect to mention is that the house price is an increasing function ofα. It can be inferred that if the interest rate rt (not the general level r) correlates stronger with the future consumption the house price is larger. A country which introduced exible ination targeting compared to a non-ination targeter is expected to have a higherα. I emphasized this aspect in a former sub-section of this chapter discussing that in ination targeting the policy rates of central banks have a higher correlation with the business cycle than in other policy regimes. This can be supported by the inuential aspect that in ination targeting the main if not the exclusive monetary policy tool is the short term interest

7p_t+1= 1,η_t+1= 1,c_t= 1andπ= 0.5

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Figure 2: House price relative to α for dierent r

rate.⁸ Subsequently, the interest rate is used by the central bank to steer the economy targeting a stable ination.

Thus, compared to other monetary policy regimes, ination targeting leads to a closer relation between output and interest rates. It can be observed that the time series of ination, output and policy rates of countries which adopted ination targeting exhibit more similar trends after the adoption than prior to the implementation.⁹

Furthermore, I also emphasized previously that the independence of central banks in a exible ination targeting supports a quicker recovery after a recession and stimulates house prices.

The second detail which can be observed in gure 2is that house prices are higher with a lower general level of interest ratesr. If the implementation of exible ination targeting is combined with a disination and followed by a general lower level of interest rates r, house prices are higher regardless of the α.

8In section 4 I outline that there is literature which argues that ination targeting is dened as having the short term nominal interest rate as the exclusive tool of monetary policy

9The next subsection presents empirical evidences on this aspect.

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3.3 Empirical Evidences

In this sub-section I present empirical evidences supporting the theoretical foundations shown in the previous parts of this chapter. Especially, I want to show that the peculiarities of ination targeting I describe are observable in the data. This means, I present evidences that the general level of interest rates denoted byris lower while the correlation α of interest rates and output is higher for ination targeters.

To perform the analysis I gathered data for the real interest rates, nominal interest rates, GDP growth rates and ination rates of my data sample which is presented in the next chapter.¹⁰ Table 2shows the data availability.

The real interest rates data was obtained from the World Bank¹¹. The short-term interest rate are collected from the Organisation for Economic Co-operation and Development (OECD)¹² except for the countries Malaysia, Hong Kong, Croatia and North Macedonia for which I used central bank data from the Bank of International Settlement (BIS)¹³ to approximate the short-term rates.

Furthermore, the data from the OECD on Japan's short term interest rate was only available in the period from 2003 to 2017. Hence, I added data from Japan's deposit rate from the OECD¹⁴ for the period of 1980 to 2002.

The data on the consumer price ination and the GDP growth rates were collected from the IMF¹⁵ while the data on the GDP deator were obtained from the World Bank¹⁶. Additionally, I calculate two further real interest rate time series using the short-term interest rates where available and deating them by (i) the GDP deator and (ii) by the CPI ination rate.

Table 3 compares the mean of the real interest rates of countries which already adopted ination targeting (post regime shift) and countries which did not (or never do).

The table shows that the average real rate after the adoption of ination targeting is lower compared to the other group. The dierence is signicant at a5percent signicance level.

In the appendix A I present the same analysis including the two other time series of the

10Table7shows the sample I use to perform my econometric analysis which coincides with the sample I use here

11Source: World Bank, Real Interest Rate in percent https://data.worldbank.org/indicator/fr .inr.rinr

12Source: OECD (2019), Short-term interest rates (indicator). doi: 10.1787/2cc37d77-en (Accessed on 08 November 2019)https://data.oecd.org/interest/short-term-interest-rates.htm

13Source: BIS, Central bank policy rateshttps://www.bis.org/statistics/cbpol.htm

14OECD, "Main Economic Indicators - complete database", Main Economic Indicators (database),http://dx.doi.org/10.1787/data-00052-en (Accessed on date 08 November 2019)

15Source: IMF, World Economic Outlook (October 2019) https://www.imf.org/external/

datamapper/datasets/WEO

16Source: World Bank, GDP Deator annual in percenthttps://data.worldbank.org/indicator/

NY.GDP.DEFL.KD.ZG

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Table 2: Data for empirical evidences

Country Real Interest Short-term GDP CPI GDP Growth

Rate Nominal Deator Ination Rate

Interest Rate

Australia 1975 - 2018 1975 - 2018 1980 - 2019 1980 - 2019 1980 - 2019

Austria 1990 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Belgium 1975 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Canada 1975 - 2018 1975 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 Colombia 1986 - 2018 1986 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 Croatia 1996 - 2014 1994 - 2019 1996 - 2018 1994 - 2019 1994 - 2019

Denmark 1987 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Finland 1987 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Greece 1995 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Hong Kong 1990 - 2018 1998 - 2019 1975 - 2018 1980 - 2019 1980 - 2019

Ireland 1984 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Israel 1995 - 2018 1992 - 2017 1980 - 2019 1980 - 2019

Japan 1975 - 2017 1980- 2017 1975 - 2018 1980 - 2019 1980 - 2019 Lithuania 1999 - 2017 1996 - 2018 1996 - 2019 1996 - 2019 Luxembourg 1999 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 Malaysia 1975 - 2018 1995 - 2019 1975 - 2018 1980 - 2019 1980 - 2019 Netherlands 1999 - 2013 1982 - 2017 1975 - 2018 1981 - 2019 1981 - 2019 New Zealand 1998 - 2017 1975 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 North Macedonia 1994 - 2018 2000 - 2019 1991 - 2018 1993 - 2019 1993 - 2019

Norway 1979 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Portugal 1986 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 South Africa 1975 - 2018 1981 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 South Korea 1996 - 2018 1991 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 Serbia 1997 - 2015 1997 - 2019 1996 - 2018 1997 - 2019 1998 - 2019 Singapore 1978 - 2018 1975 - 2018 1980 - 2019 1980 - 2019 Sweden 1992 - 2006 1982 - 2017 1975 - 2018 1980 - 2019 1980 - 2019 Switzerland 2008 - 2018 1975 - 2017 1981 - 2018 1980 - 2019 1980 - 2019 Thailand 1976 - 2001 2000 - 2019 1975 - 2018 1980 - 2019 1980 - 2019 United Kingdom 1975 - 2014 1986 - 2017 1975 - 2018 1980 - 2019 1980 - 2019

Note: Blank cells mean no data available

Table 3: Mean Dierence Real Interest Rates Pre-Adoption and Non-targeters vs. Post- Adoption

Mean Pre-Adoption and Mean Post-Adoption Dierence t−value Non-targeters

Real Interest Rate 0.0486 0.0399 0.0087^∗∗ (2.3213)

∗ p <0.1,^∗∗ p <0.05,^∗∗∗ p <0.01

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Table 4: Correlation Real Interest Rates and GDP Growth Rates for Pre-Adoption and Non-targeters vs. Post-Adoption

GDP Growth Rate

Pre-Adoption and Non-targeters Post-Adoption Correlation Coecient Correlation Coecient

Real Interest Rate .0169436 .0897077

real interest rates. The results are similar to the one presented here.

Furthermore, I show in the appendix A a table on all three real interest rates only for data after the year 1989. The idea is that the development prior to the rst adoption of ination targeting¹⁷ does not inuence the results. The result for the real interest rate obtained from the World Bank is comparable. The other rates still show a higher mean post-adoption but the dierences are not signicant any more.

The other aspect I want to present here is that the correlation of the interest rate and the GDP growth rates is higher after the regime shift. In table 4I present the correlation coecients for the two groups specied previously.

The table shows that the correlation between the real interest rate and the GDP growth rate is higher for post-adoption years.

In a similar manner to the real interest rates I present additional results in appendix A.

I display the correlation coecients for the other two real interest rates and also show a table which solely includes data after 1989. The outcome is similar to the one presented in table 4. The correlation between real interest rates after the regime shift is higher compared to pre-treatment and non-targeters group.

All in all, the presented empirical evidences here support the theoretical foundations outlined in the former sub-section that ination targeters have lower general interest rate levels r and higher correlation α of interest rates with the GDP.

17New Zealand in 1989. See table7

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4 Data

In this section I outline my data sources for the house prices and adoption date of ination targeting. The rst part introduces the house price data while the second sub-section deals with the ination targeting data.

4.1 Data on House Prices

In this thesis I used three sources for the house price data. The main data set was received from the Federal Reserve Bank (FED) of Dallas and the supplementary material was gathered from the BIS and the Organisation for Economic Co-operation and Development (OECD).

4.1.1 Data from the FED

I use the dataset described inMack et al. (2011). They produce international house price panel data which is released on a quarterly basis for a variety of countries. The rst data point is from the rst quarter of 1975.

The idea is to create comparable data for all countries. Hence, the authors select a house price index from each country which is most consistent with the quarterly U.S. house price index for existing single-family houses which is oered by the Federal Housing Finance Agency. If the data for some countries is not available for the entire time span, the authors extend the data backwards by using historical or secondary data sources.

A signicant aspect is that the data for all countries is seasonally adjusted over the entire time horizon with an unobserved components time series model. All time series are rebased to 2005 in which the index equals 100 for all countries. The house price indices are oered in nominal and real term where the real values are computed using the personal consumption expenditure deator.

Additionally, for all time series, they produce a weighted average using purchasing power parity-adjusted gross domestic product shares from the base year 2005. The authors obtain the data from national public sources mainly from central banks, statistical oces or other non-government institutions which are not involved into housing transactions to minimize potential market participant bias. Moreover, they consult private organizations such as newspapers and real-estate agencies and international organizations (e.g. BIS) in some situations.

Nonetheless, the authors inform that a few issues peculiar in measuring house prices may well be present to some extent in their database. One issue is that housing is in general a heterogeneous good. Controlling for quality and characteristics may introduce some cross-country heterogeneity in their house price indices as well. On the other hand,

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too few variables used to explain the house price developments may results in omitted variable bias. The last aspect they mention is that data on the whole housing stock are nonexistent. Thus, the indices are created with the sample of houses sold over the sample period. Depending on data availability for each country, they apply dierent method to control for sample size, quality and characteristics of the dwellings.

A table in sub-section 4.3 exhibits detailed information which data source has been used for each country.

4.1.2 Data from other sources

Additional to the FED data I use the "selected series" on real house price indices from the BIS.¹⁸ Unlike the FED series, the series covers all types of dwellings in markets for both new and existing property in the country as a whole. The real house price indices are calculated using the consumption price index deator. The BIS says that the data is selected as homogeneous as possible given the recommendations in the Handbook on Residential Property Prices Indices (RPPIs) (de Haan & Diewert, 2013). Hence, the indices in the "selected series" are all seasonal adjusted. The base year of the index is 2010.

Furthermore, I obtained data from the OECD.¹⁹ The housing price indices of the OECD incorporate the sales of new and existing properties. Likewise, the indices follow the recommendation of the RPPIs (de Haan & Diewert, 2013). The real house price indices are seasonally adjusted because they are calculated by deating the nominal price by the consumers' expenditure in each country, both seasonally adjusted.

4.2 Data on Ination Targeting

The data on the adoption of ination targeting is more controversial than one may think.

The rst debate is the question if a country is an ination targeter or not. I follow the classication used byRoger(2009) and Roger(2010) which is based on the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) database.²⁰ Nevertheless, a few countries need further investigation as their regime is not as strict and economist have dierent views on the subject. Roger (2009) and Roger (2010) acknowledges that especially the European Central Bank, the Federal Reserve Board in the U.S.

18 Source: National sources, BIS Residential Property Price database http://www.bis.org/

statistics/pp.htm

19Source: OECD (2019), Housing prices (indicator). doi: 10.1787/63008438-en (Accessed on 31 August 2019)https://data.oecd.org/price/housing-prices.htm

20For my analysis I use the report released by the IMF on April 16, 2019; See: https://www.imf.org/

en/Publications/Annual-Report-on-Exchange-Arrangements-and-Exchange-Restrictions/

Issues/2019/04/24/Annual-Report-on-Exchange-Arrangements-and-Exchange-Restrictions -2018-46162

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and the Bank of Switzerland adopted many features of ination targeting.²¹ For those countries I give a detailed discussion in the next sub-section.

The next debate in the literature is concerning the adoption date. Again, I follow the convention used byRoger(2009) andRoger(2010) based on the AREAER. The adoption date in his analysis is based on the rst moment in time when the interest rate turned into the exclusive tool to conduct monetary policy. Often in the literature this is called the fully-edged ination targeting adoption. I further consulted Vega & Winkelried (2005) who created a literature review on the adoption dates²². Table 5 shows the summary of Vega & Winkelried(2005)²³ while the adoption date following the convention from Roger (2010) was added in the last column. Vega & Winkelried(2005) also include Finland and Spain in their table but I omit them because they dropped ination targeting after joining the European Monetary Union (EMU). As already indicated, I discuss the EMU, but also Spain and Finland separately in the next sub-section. The table shows thatRoger (2010) adoption dates for most countries are widely agreed in literature.

There are only a few exceptions where the dates are distinct by three or more years.

For Canada we can observe that the fully-edged ination target range was established in 1994. Nonetheless, the rst range was announced in 1991 and the interest rate was the exclusive tool for monetary policy operations, so that I stick to 1991 as the adoption dates.

Vega & Winkelried(2005) identify two dierent potential implementation dates for Colom- bia as well. But the literature generally agrees on the year 1999 as the implementation date. BecauseUrrutia et al.(2014) show that Colombia abandoned the last exchange rate peg in 1999 I leave the implementation year 1999 unchanged.

South Korea exhibits dierences between Roger's and the widely agreed literature's implementation date as well. Roger's date is based on the date in which the interest rate became the unique instrument to steer monetary policy. Kim (2006) in a comment²⁴ on Kim et al. (2006) conrms the date by saying that the monetary indicator M3 as an instrument was discontinued. I use 2001 as my benchmark date but perform a robustness analysis with the year 1998 as well.

The implementation year in Israel in the literature and Roger's convention disagree as well.

All articles cited use 1992 as the implementation year in which the central bank of Israel for the rst time announced an explicit ination target. Contrary, Roger classies 1997

21He also says that the Bank of Japan adopted many of the features but meanwhile in 2013 they ocially announced the adoption of ination targeting.

22The articles Vega & Winkelried(2005) use in their literature review: Corbo et al.(2002),Fracasso et al.(2003),Fraga et al.(2003),Levin et al.(2004),Pétursson et al.(2004),Ball & Sheridan(2004)

23They call this table Table 1 in their paper

24Find the comment in "Monetary Policy in Asia: Approaches and Implementation" Page:168-171;

See: https://ssrn.com/abstract=1188863

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Table 5: Literature review on ination targeters' adoption date

Country Corbo Fracassco Fraga Levin Petursson Ball Vega Vega Roger et al. et al. et al. et al. (2004) et al. et al. et al. (2010) (2002) (2003) (2003) (2004) (2004) (2005) (2005)

I II

Australia 1994 1994 1993 1993 1994 1994 1994 1993

Brazil 1999 1999 1999 1999 1999 1999 1999 1999

Canada 1991 1991 1991 1991 1992/94 1991 1994 1991

Chile 1991 1991 1991 1991 1990 1991 1999 1999

Colombia 1999 1999 1999 1999 1999 1995 1999 1999

Czech

Republic 1998 1998 1998 1998 1998 1998 1998 1997

Hungary 2001 2001 2001 2001 2001 2001 2001

Iceland 2001 2001 2001 2001 2001 2001 2001

Israel 1992 1992 1992 1992 1992 1992 1997 1997

Mexico 1999 1999 1999 1999 1999 1995 1999 1999

NewZealand 1990 1988 1990 1990 1990/93 1990 1991 1990

Norway 2001 2001 2001 2001 2001 2001 2001 2001

Peru 1994 2002 1994 2002 2002 1994 2002 2002

Philippines 2002 2002 1995 2002 2002

Poland 1998 1998 1998 1998 1998 1998 1998 1998

South

Africa 2000 2000 2000 2000 2000 2000 2000 2000

South

Korea 1998 1998 1998 1998 1998 1998 1998 2001

Sweden 1993 1993 1993 1993 1995 1993 1995 1993

Switzerland 2000 2000 2000 2000 2000 2000 2000

Thailand 2000 2000 2000 2000 2000 2000 2000 2000

UK 1992 1992 1992 1992 1993 1992 1992 1992

Note: Blank cells mean no specication provided by the authors

as the implementation year in which interest rates became the only vehicle of monetary policy. I apply 1997 as my baseline case but also perform a robustness check with 1992 as a potential year of adoption.

Furthermore, the adoption date used by Roger for Chile and Peru seems to dier substantially from the ones picked in the literature. Unfortunately, I was not able to gather enough data on Chile's and Peru's real house price indices to perform an analysis so that this issue is solved by itself.²⁵ The same holds for Mexico and the Philippines.

25See the next sub-section in which I display the data I gathered initially compared to the data I used for the analysis.

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4.3 Data and Sample Selection

In this sub-section I explain which data I collected compared to the data I used for the analysis. I illustrate both the data collection and my nal sample in a table. Additionally, I outline why certain countries and their data was not used to perform my analysis.

Table 6 displays all the data of the countries I initially accumulated. The table also displays the source and the time span of the data. I collected data on 39 countries, 23 from the FED Dallas, 11 from the BIS and the remaining 5 from the OECD. A rst important point to mention is that the frequency of the data for all sources is quarterly.

In order to make the data annually I just calculated a simple average for each year.

Additionally, the data of the OECD and BIS was available until 2018 and the FED data until the rst quarter of 2019.²⁶

My nal sample deviates from the initially collected data by a few countries. Table 7 displays the data used for the baseline analysis, including the countries, whether they are ination targeters and the benchmark adoption date in case they are targeters.

I use 29 countries to perform my empirical analysis. 14 of these countries are classied as ination targeters while 15 can be categorized as non-ination targeters.

The following ten countries were dropped: Brazil, Chile, France, Germany, Iceland, Italy, Peru, Russia, Spain and United States. In the coming paragraphs I outline why these countries have been omitted from the analysis.

The rst group of countries which is precluded are the largest European Monetary Union (EMU) economies Germany, France, Spain and Italy. Roger(2010) acknowledges that the European Central Bank (ECB) "adopted many of the main elements of ination targeting [...]." On the other hand, they do not call themselves ination targeters. Also, the ECB has not set an explicit numerical target but just that it want to achieve an ination rate of less than two percent. Gertler (2004)²⁷in a comment onBall & Sheridan (2004) says that the EMU has followed a monetary policy which is indistinguishable from ination targeting and therefore should not be in the control group. A reasonable inference according to Gertler (2004) would be to call all members of the EMU ination targeters. I do not think that this would be the correct conclusion.

Firstly, it is important to know that the ECB targets the ination rate of the Monetary Union index of consumer prices (MUICP). The MUICP is calculated as the weighted average of the national harmonized index of consumer prices (HICP). The weights are based on the share of the household nal monetary consumption expenditure (HFMCE) which

26The data for 2019 or the remaining quarters in 2019 is forecasted in a simple manner

27The comment is part of chapter 6 in the book "The ination-targeting debate" in which the article is published as well; See: https://www.nber.org/books/bern04-1

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Table 6: Initial Data

Country Source First Data Point Last Data Point

Australia FED Dallas 1975 2019

Austria BIS 1975 2018

Belgium FED Dallas 1975 2019

Brazil BIS 2001 2018

Canada FED Dallas 1975 2019

Chile OECD 2002 2018

Colombia OECD 1988 2018

Croatia FED Dallas 1975 2019

Denmark FED Dallas 1975 2019

Finland FED Dallas 1975 2019

France FED Dallas 1975 2019

Germany FED Dallas 1975 2019

Greece OECD 1997 2018

Hong Kong BIS 1979 2018

Iceland BIS 2000 2018

Ireland FED Dallas 1975 2019

Israel FED Dallas 1975 2019

Italy FED Dallas 1975 2019

Japan FED Dallas 1975 2019

Lithuania BIS 1998 2018

Luxembourg FED Dallas 1975 2019

Malaysia BIS 1988 2018

Netherlands FED Dallas 1975 2019

New Zealand FED Dallas 1975 2019

North Macedonia BIS 1975 2018

Norway FED Dallas 1975 2019

Peru BIS 1998 2018

Portugal OECD 1988 2018

Russia OECD 2001 2018

South Africa FED Dallas 1975 2019

South Korea FED Dallas 1975 2019

Serbia BIS 2000 2018

Singapore BIS 2004 2018

Spain FED Dallas 1975 2019

Sweden FED Dallas 1975 2019

Switzerland FED Dallas 1975 2019

Thailand BIS 1991 2018

United Kingdom FED Dallas 1975 2019

United States FED Dallas 1975 2019

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Table 7: Benchmark Sample

Country Ination Adoption Country Ination Adoption

targeter date targeter date

Australia Yes 1993 Malaysia No

Austria No Netherlands No

Belgium No New Zealand Yes 1990

Canada Yes 1991 North Macedonia No

Colombia Yes 1998 Norway Yes 2001

Croatia No Portugal No

Denmark No South Africa Yes 2001

Finland No South Korea Yes 2001

Greece No Serbia Yes 2006

Hong Kong No Singapore No

Ireland No Sweden Yes 1993

Israel Yes 1997 Switzerland Yes 2000

Japan Yes 2013 Thailand Yes 2000

Lithuania No United Kingdom Yes 1992

Luxembourg No

are derived from national accounts data. ²⁸ I use the nal consumption expenditures of household per country in 2018 to approximate the weight which is assigned to each country in the MUICP.²⁹ The data shows that the four largest economies of the EMU Germany, France, Italy and Spain account for almost 80% of the weight. I include a pie chart of the expenditure shares in the appendix B. The four large economies dominate the ination decision while the ination of a smaller economy barely matters.

For September 2019 the ination rates of the national HICP ranges from3.0% for Slovakia to −0.5% for Cyprus, while the Euro area HICP shows an annual change of 0.8%.³⁰ Although the economies are very integrated the price development is still dierent. Hence, some countries would probably follow a dierent monetary policy if they would be able to conduct independent exible ination targeting. From my point of view, it can be concluded that while the EMU follows a policy which is indistinguishable from ination targeting, this does not hold for the small economies of the currency union. Thus, I classify all EMU economies except Germany, France, Italy and Spain as non-ination targeters.

On the other hand, I cannot certainly allocate the four large economies in the treatment group and therefore I omit them from the analysis. Additionally, setting the implementation date for the EMU respectively the four large economies would also be debatable.

28Source: Eurostat; See HICP methodlogy: https://ec.europa.eu/eurostat/statistics -explained/index.php?title=HICP_methodology

29Source Eurostat; See: https://ec.europa.eu/eurostat/web/national-accounts/data/database and open "Annual national accounts", then the "Main GDP aggregates" and afterwards the sub-le "Final consumption aggregates by durability"

30Source Eurostat; See: https://ec.europa.eu/eurostat/web/hicp/data/database and open the

"Harmonised index of consumer prices" and then the sub-le "monthly data (annual rate of change)"

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One could argue that it was in 1998 when the Maastricht treaty was signed, or it was in 1999 when the Euro was introduced as an accounting currency and the ECB started operating, or it was in 2002 when the Euro banknotes were circulated for the rst time.

The appendix C.2 covers a robustness check with adding Germany, France, Italy and Spain as ination targeters with the adoption year 1999.

Moreover, with Finland and Spain there are two countries which implemented ination targeting but changed their monetary policy regime when they joined the European Ex- change Rate Mechanism (ERM) (see Korhonen (2001) and European-Economy (1998)).

After joining the ERM, monetary policy of both countries included exchange rate stability in addition to price stability. While Spain is eliminated from the analysis due to other reasons, Finland is grouped as a non-ination targeter for the entire horizon. The period of ination targeting for Finland is rather short. They introduced it in 1993 and joined the ERM in 1996 (Korhonen(2001)). Hence, I think it is reasonable to treat Finland as a non-ination targeter. Nonetheless, as a robustness check I discard Finland from the analysis.

The United States are also neglected from the analysis. Roger (2010) recognizes that the U.S. has adopted the main features of ination targeting. The U.S. is and never was considered as an ination targeter by the AREAER, although they introduced an explicit ination target in 2012. Gertler (2004) in a comment on Ball & Sheridan (2004) says that "the conventional wisdom is that the Federal Reserve has behaved as an implicit ination targeter." He further argues that the Volcker and Greenspan administration was responsible for realizing the same benets as one may have with a exible ination targeting regime. Hence, it would be wrong to include the U.S. in the control group.

On the other hand, it is also not trivial to include the U.S. in the treatment group. None of the papers I cited in the literature review is using the U.S. as an ination targeter. Addi- tionally, the implementation date could at best be a guess. Picking 2012 would not reect the fact that the monetary policy was previously indistinguishable from ination targeting but without an announcement of an explicit target there is no obvious date to choose.

Furthermore, if setting the adoption year to a year reecting the Volcker disination in the late 1970s the data is limited as the history of house prices pre-implementation could not be observed for a long horizon. All in all, the most reasonable choice is to exclude the U.S. real house price index from the analysis.

The following countries have been deleted because the data on their real house price indices compared to their adoption date was limited: Brazil, Chile, Iceland, Peru and Russia. A substantial variation in the numbers of the ination targeting countries would make the analysis unreliable.

The rst data point recorded for Brazil was in 2001 while the implementation was already