Global Liquidity, House Prices and the Macroeconomy: Evidence from Advanced and Emerging Economies

Cesa-Bianchi, Ambrogio; Céspedes, Luis F.; Rebucci, Alessandro Working Paper Global Liquidity, House Prices and the Macroeconomy: Evidence from Advanced and Emerging Economies IDB Working Paper Series, No. IDB-WP-576 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Cesa-Bianchi, Ambrogio; Céspedes, Luis F.; Rebucci, Alessandro (2015) : Global Liquidity, House Prices and the Macroeconomy: Evidence from Advanced and Emerging Economies, IDB Working Paper Series, No. IDB-WP-576, Inter-American Development Bank (IDB), Washington, DC, https://hdl.handle.net/11319/6865 This Version is available at: https://hdl.handle.net/10419/115521 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode Global Liquidity, House Prices and the Macroeconomy: Evidence f rom Advanced and Emerging Economies Am b rogio Cesa-Bianc h i Luis F. Céspedes Alessandro Rebucci Department o f Research and Chie f Economist IDB-WP-576 IDB WORKING PAPER SERIES No. Inter-American Development Bank March 2015 Global L i qu i d i ty, House Prices and the Macroeconomy: Evidence from Advanced and Emerging Economies Am b rogio Cesa-Bianc h i* Luis F. Céspedes** Alessandro Rebucci*** * Bank of England ** Adolfo Ibáñez University *** Johns Hopkins University 2015 Inter-American Development Bank Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Cesa-Bianchi, Ambrogio. Global liquidity, house prices and the macroeconomy: evidence from advanced and emerging economies / Ambrogio Cesa-Bianchi, Luis F. Céspedes, Alessandro Rebucci. p. cm. — (IDB Working Paper Series ; 576) Includes bibliographic references. 1. Housing—Prices—Developing countries. 2. Housing—Prices—Developed countries. 3. Capital movements—Developing countries. 4. Capital movements—Developed countries. I. Céspedes, Luis F. II. Rebucci, Alessandro. III. Inter-American Development Bank. Department of Research and Chief Economist. IV. Title. V. Series. IDB-WP-576 http://www.iadb.org Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB’s name for any purpose other than for attribution, and the use of IDB’s logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license. Following a peer review process, and with previous written consent by the Inter-American Development 2015 Copyright © Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license ( http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose. No derivative work is allowed. Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association’s EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication’s author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail. Note that link provided above includes additional terms and conditions of the license. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. Abstract1 This paper first compares house price cycles in advanced and emerging economies using a new quarterly house price dataset covering the period 19902012. It is found that that house prices in emerging economies grow faster, are more volatile, less persistent and less synchronized across countries than in advanced economies. They also correlate more closely with capital flows than in advanced economies. The analysis is then conditioned on an exogenous change to global liquidity, broadly understood as a proxy for the international supply of credit. It is found that in emerging markets a global liquidity shock has a much stronger impact on house prices and consumption than in advanced economies. Finally, holding house prices constant in response to this shock tends to dampen its effects on consumption in both advanced and emerging economies, but possibly through different channels: in advanced economies by boosting the value of housing collateral and hence supporting domestic borrowing, and in emerging markets by appreciating the exchange rate and hence supporting the international borrowing capacity of the economy. JEL classifications: C32, E44, F44 Keywords: Capital flows, Emerging markets, Global liquidity, House prices, External instrumental variables 1 The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England. We would like to thank Saleem Bahaj, Philippe Bracke, Valentina Bruno, Giancarlo Corsetti, Andrea Ferrero, Peter Karadi, Ken Kuttner, Jean Imbs, Prakash Loungani, Andy Powell, Konstantinos Theodoridis, and seminar participants at the Dallas FED, American University, the XVII Workshop in International Economics and Finance, the Bundesbank, and the IMF for helpful comments and suggestions. 1 Introduction Housing is a quintessential non-tradable durable good, and the non-tradable sector has often been at the center of financial crises. Booms in the non-tradable sector fuelled by excessive credit expansions and overvalued exchange rates were at the core of the many banking and currency crises that emerging market economies experienced in the 1990s and early 2000s (Claessens, Kose, Laeven, and Valencia,2013). A similar mechanism played an important role in the recent banking and external crises in Southern Europe (Bruno and Shin,2015). These crises were often triggered by external shocks, such as a reversal of capital flows associated with tighter financing conditions in the United States and other advanced economies. But they were amplified by falling collateral values that contracted households and firms’ borrowing capacity in a procyclical manner. At the same time, over the past 20 years or so—in Asia and other emerging markets first, and in the United States and other advanced economies more recently—capital has been abundant and highly mobile, while the set of profitable investment opportunities has been limited. Against this backdrop, policymakers worldwide have worried for some time now about the side effects of large and volatile capital flows. Indeed, while some blamed (at least in part) the United States housing and financial collapse on the glut of Asian saving that exerted downward pressure on U.S. long-term interest rates, emerging economies in Latin America and Asia tended to blame excessive exchange rate appreciations, asset price bubbles and overheating on the monetary policy stimulus enacted by the United States in response to the global financial crisis. In this paper we compare house price behavior in advanced and emerging economies using a new, quarterly data set on emerging market house prices that we assembled for this purpose. We first document a new set of stylized facts for emerging markets, showing that house price inflation tends to behave like consumption growth, which is non-tradable internationally to a significant degree. By comparison, equity prices behave more like GDP, which has a much larger tradable component. In particular, we show that in emerging markets house price inflation is higher, more volatile, less persistent and less synchronized across countries than in advanced economies. We also show that house price inflation is more correlated with capital flows in emerging countries. Led by this latter fact, we then build an empirical model of house prices and capital flows in which we can identify an exogenous change to a specific component of total gross flows, i.e., “global liquidity.” Global liquidity, which we interpret in a broad sense as the international supply of credit, was a quantitatively sizable portion of total cross-border flows in the run-up to the global financial crisis (Bruno and Shin,2015). Although its share of total flows fell after the crisis (Shin,2013,Ahmed, Curcuru, Warnock, and Zlate, 2014), it remains closely associated with debt flows and international financial conditions more generally (Rey,2013). In this model we identify a global liquidity shock by aggregating bank-to-bank cross-border credit flows across all sending and receiving countries in our sample and by using the external instrumental variable 2 approach of Stock and Watson (2012) and Mertens and Ravn (2013).2By aggregating cross-border lending across all sending and receiving countries we rule out that country-specific factors affect it within a quarter. By using the external instruments identification approach, we can rule out that demand factors that are common among all countries in the sample may affect the aggregate measure. The estimation results show that a global liquidity shock affects house prices, consumption, and the current account in emerging economies much more than in advanced economies. These effects are also associated with a weaker interest rate and exchange rate response in emerging markets. In an attempt to interpret our empirical findings, we finally explore the role of collateral valuation effects linked to house price and exchange rate changes. In general equilibrium models of housing and the macroeconomy—see, for example, Iacoviello (2005), Monacelli (2009), and Liu, Wang, and Zha (2013)— collateral constraints can amplify the response of consumption and investment to ordinary business cycle shocks. Shocks, via their impact on house prices, affect the value of collateral and in turn determine the borrowing capacity of households and firms. The real exchange rate plays a similar role in the partial equilibrium model of global liquidity of Bruno and Shin (2015) and in general equilibrium models of balance sheet effects (e.g., Cespedes, Chang, and Velasco,2004,2012,Gabaix and Maggiori,2014). To accomplish this, we re-estimate the effect of the same global liquidity shock, but keeping either house prices or the exchange rate constant in the model. When we hold house prices constant, in the case of advanced economies, the main difference between the baseline and the counterfactual is the consumption response to the shock; in the case of emerging economies, instead, the main difference is the exchange rate and the current account response. In addition, when we close the exchange rate channel, we find that house prices become more stable in emerging markets, while they become more volatile in advanced economies. We interpret this evidence as suggesting that house price movements amplify the response to global liquidity shocks in both advanced and emerging economies, but possibly through different mechanisms: in advanced economies, arguably by boosting the value of housing collateral and hence supporting more household borrowing as predicted by the housing models with domestic borrowing constraints mentioned above; in emerging markets, by generating a lower default risk and a more appreciated exchange rate that support the international borrowing capacity of the economy. The paper relates to several strands of literature. A few papers have focused on house prices behavior over the business cycle. Andre (2010) and Hirata, Kose, Otrok, and Terrones (2012) document the cyclical behavior of house prices and their relation to the macroeconomy for advanced economies. Igan and Loungani (2012), Claessens, Kose, and Terrones (2012), and Cesa-Bianchi (2013) also consider emerging markets in their analyses. Unlike the previous literature, we compare advanced and emerging economies systematically by using samples of comparable country size and quarterly data over a period covering both the emerging market crises of the 1990s and the global financial crisis. 2This identification strategy uses instrumental variables to isolate the component of the VAR reduced-form residuals that are due to the structural shock of interest. Appendix Cdescribes this strategy in more detail. 3 A second strand of literature has explored the relation between capital flows and house prices in an attempt to gauge the role of so-called “global external imbalances” during the global financial crisis. Using data for advanced economies, Laibson and Mollerstrom (2010), Favilukis, Kohn, Ludvigson, and Nieuwerburgh (2012), Adam, Kuang, and Marcet (2012) and Ferrero (2012) provide evidence of a robust association between real house price appreciations and a widening of the current account deficit. Similarly, using panel data from both advanced and emerging economies, Aizenman and Jinjarak (2009) find that lagged changes in current account deficits are associated with an appreciation of real house prices. Gete (2009) and Sa, Towbin, and Wieladek (2014) investigate the causal link from the current account and capital flows to house prices in VAR models for advanced economies. Relative to this strand of literature, not only do we compare systematically advanced and emerging economies, but we also suggest a novel approach to identifying a capital account shock. Indeed, as far as we are aware of, this is the first application of the external instrumental variable approach to a capital flow shock. The paper also relates to the ongoing debate on the side effects of (and prospective exit from) exceptionally loose monetary policies that advanced economies enacted in response to the global financial crisis. Landau (2013) stresses the importance of understanding the consequences of advanced economies’ monetary policies for cross-border movements of liquid assets, which are driven more and more by global risk appetite and, to a lesser extent, by interest rate differentials. Similarly, Rey (2013) highlights the impact of monetary policy in the United States on the nature and the direction of international capital flows which, in turn, affect credit conditions and asset price behavior. Relative to these studies, we build an empirical model of global liquidity and investigate the impact of an exogenous change in such a variable on both house prices and the broader macroeconomy. The rest of the paper is organized as follow. Section 2 describes our new dataset. Section 3 compares house price characteristics in the two group of countries. Section 4 looks at the association with the broader macroeconomy and capital flows in particular. Section 5 discusses the concept of global liquidity and its links to house prices. Section 6 explores the causal link from global liquidity to consumption and house prices in a panel VAR model. Section 7 discusses and tries to interpret our empirical findings by means of a simple counterfactual exercise. Three appendices report additional information on the data and the details of the analysis. 2 A New Global House Price Data Set A contribution of the paper is the construction of a new, quarterly house price data set for 33 emerging markets with a minimum coverage from the early 2000s to 2012:Q4 for all countries except Mexico and Morocco, thus providing house price series with at least 40 observations.3This information is combined with data for 24 advanced economies from the OECD house price database. Therefore, the dataset that we will use in the analysis covers 57 countries and more than 95 percent of world GDP.4 3Mexico and Morocco house price series are slightly shorter. We use data for these two countries only in the first, descriptive part of our analysis. 4The dataset is available at: https://sites.google.com/site/ambropo/ and https://sites.google.com/site/arebucci. 4 Our new dataset on emerging economies uses information from the OECD house price database, the BIS property price dataset, the Federal Reserve of Dallas international house price database, national central banks, national statistical offices, and academic and policy publications on housing markets.5Relative to its main building blocks—i.e., the OECD, the BIS, and the Federal Reserve of Dallas datasets— we extend the time coverage of our dataset by 12 series and include 9 additional country indices. Specifically, we extended the existing series for China, Estonia, Hong Kong, Hungary, Indonesia, Lithuania, Malaysia, the Philippines, Poland, Slovakia, Slovenia, and Thailand; and we collected data for Argentina, Brazil, Chile, Colombia, Czech Republic, India, Serbia, Taiwan, and Uruguay. In the process, we also extended the coverage for three advanced economies, namely Austria, Greece, and Malta. The coverage of existing indices is extended by extrapolating backward newer series with historical data. For countries for which there exists a quarterly house price index, we extrapolate backward with the growth rate of the historical series. To make sure that the two series are comparable we use any overlapping period to evaluate the extrapolation.6For a few countries for which there exists only an annual index, we first interpolate the annual data, and then extrapolate backward the quarterly series. To interpolate annual data, we simply assume that house prices grow at a constant rate within the year. The resulting dataset is an unbalanced panel of 57 quarterly time series with varying coverage from 1990:Q1 to 2012:Q4. Figure 1provides a visual impression of the data coverage. The top panel describes advanced economies (AEs). The bottom panel describes emerging markets (EMs). The darker areas reflect the coverage of the OECD, BIS and Dallas FED data sets. The lighter areas represent either additional countries or series extended backward over time with quarterly data. The lightest areas refer to series extended backward with annual or semi-annual data. As we can see, with the sole exception of Iceland, all advanced economies are covered from 1990.7The coverage for emerging market economies is more varied, with 10 countries starting in the 1990 (compared to 23 AEs), a few more countries starting in mid-1990s, and only 3 countries with fewer than 40 quarterly observations. The specific definition and the sources of our house price indices are listed in Appendix A. As is well known, unfortunately, available house price indices do not follow a harmonized definition and methodology. As a result they are very heterogeneous. Even within the OECD house price database, for instance, indices may differ along a number of dimensions. In our dataset, some series are median while others are averages prices; most series are not quality adjusted; some refer to nationally representative types of properties while other are for specific property types; some may refer to transaction prices while others are valuations or offer prices; some refer to new houses and others to all sales. Importantly, some series are national indices, while a few are indices for a major city. Controlling for this heterogeneity in our analysis of the data is a diffcult task. We note however that, in the few instances in which we have both national house price indices and their individual city 5Appendix Aprovides details on the definitions and sources of the data. 6This part of the analysis is not reported but is available from the authors on request. 7The OECD house price database covers these countries since 1970. Very few emerging market series, however, start in the 1970s or the 1980s. 5 A similar picture emerges for emerging economies, with some important differences. Panel (b) of Figure 3plots the same co-movements for EMs. In general, consistent with the evidence of lower persistence noted earlier, we can see that the associations in emerging economies have shorter leads and lags than in advanced economies. The link with GDP growth and equity price increases is very similar to that in advanced economies. But the link with consumption growth is weaker, especially in terms of lead coefficients. House price inflation seems to lead CPI inflation with the same timing of advanced economies. House prices also lead interest rate increases, but are followed by interest rate declines, perhaps reflecting the constraints on monetary policy originating in the external sector of the economy. Emerging markets exposed to strong capital inflows, in fact, might not be able to increase interest rates to cool their domestic economy without attracting more capital, or might have to lower them to stem exchange rate pressure. Indeed, the connection between house price inflation and the external sector is qualitatively similar but quantitatively different in emerging markets. The association with cross-border credit and current account deteriorations, in particular, is stronger. At the same time, the association between house price inflation and real exchange rate appreciations is weaker and barely significant statistically. This suggests a stronger role for capital flows in EMs, also affecting monetary policy via their reaction to exchange rates. Although we do not report the results, these co-movements are robust to dropping the period from 2007:Q1 to 2012:Q4 (which encompasses the global financial crisis), as well as to starting the sample period in 1995 (as in the conditional analysis of the data below), or to dropping interpolated series from the analysis. 5 Global Liquidity and Its Impact on the Macroeconomy In the next section, we will investigate the causal relation between capital flows, house prices, and the macroeconomy by focusing on a particular component of these flows: cross-border bank lending. Crossborder bank flows are at the centre of the policy and academic discussion on global funding conditions, often referred to as “global liquidity.” In this section we will briefly discuss the concept of global liquidity, how it is measured, its determinants, and its transmission mechanism to house prices and the rest of the economy so as to facilitate the identification of shocks to such a variable in our empirical model and the interpretation of the estimation results. One of the characteristics of the international economic environment that preceded the global financial crisis is the large share of bank flows in total capital flows, originating from leveraged global banks and other financial institutions (e.g., Bruno and Shin,2015). Although their share fell after the crisis with the deleveraging of the originating institutions (Shin,2013,Ahmed, Curcuru, Warnock, and Zlate,2014), this component of international capital flows remains closely associated with debt flows and international financial conditions more generally (Rey,2013). Against this backdrop, the BIS BIS (2011,2013,2014) and other policy institutions (see, for instance, a series of policy reports by the IMF) have started to monitor a board set of “global liquidity” indicators, including both price and quantity (stock and flow) measures. These indicators essentially aim at characterizing the international supply of credit, and hence financing conditions in international financial markets. The BIS banking statistics (Table 7a and 7b), in particular, permit tracing trace cross-border bank 12 lending to the domestic bank sector, which is the main measure of global liquidity that we will use in our empirical analysis.14 Figure 4plots bank-to-bank cross-border lending deflated by the U.S. CPI, both in levels and in yearon-year changes. To construct this measure we sum across all receiving countries the difference between Table 7a and Table 7b of the BIS locational statistics (BIS,2011) and deflate it with the U.S. CPI. According to this measure, global liquidity (GL) started to increase sharply at the beginning of the 2000s, to peak in 2007, after a long period of fluctuations around a constant level.15 After falling dramatically during the acute phase of the global financial crisis, it fluctuated around a more or less steady level, with a further decline in 2011 and 2012 in coincidence with the European crisis. If we frame global liquidity as “international supply of credit” or “global financing conditions” it is possible to link it to house prices and the broader macroeconomy in an intuitive way. The first link in the chain involves the relationship between cross-border bank flows and their global drivers. A number of variables have been found to drive cross-border bank flows.16 These include monetary policy of the main convertible currencies, banks’ willingness and ability to take on risk, as well as price and quantity measures of funding conditions. Monetary policy indicators include the general level of interest rates and the slope of the yield curve. The latter is particularly important given the maturity transformation role of banks. Volatility in financial markets, usually proxied by the U.S. VIX index of stock option price volatility, can quantify banks’ willingness to take on risk (see Bekaert, Hoerova, and Lo Duca,2013). Bank leverage is perhaps the most important indicator of banks’ ability to extend credit (e.g., Bruno and Shin,2015). The TED spread (the difference between short-term interbank lending and government bond rates at same maturities) can describe the funding conditions of global banks. The literature also pointed to changes in money aggregates, such as M2, as possibly reflecting banks’ access to wholesale deposits by the corporate sector. So, if we think about global liquidity as the international supply of credit, the drivers above can be thought of as vectors of supply curve shifters (Cerutti, Claessens, and Ratnovski,2014). The next consideration is the link between the global credit supply and the current account. The current account balance is the excess of saving over investment, both of which are determined by households’ and firms’ resource allocation decisions. In the absence of credit constraints, these decisions are driven by real considerations, such as the real interest rate, income, and the marginal product of capital. The international supply of credit, at the margin, comes from economies with current account surpluses, while 14A second concept, sometime referred to as “official global liquidity,” is the sum of world international reserves (excluding gold) measured in U.S. dollars, plus the U.S. M0 deflated by the U.S. CPI. This concept also captures ease of financing, but it quantifies “the funding that is unconditionally available to settle claims through monetary authorities” (BIS,2011) and hence it less directly impacts house prices. As discussed by Matsumoto (2011), the concept of “private global liquidity” that we use can be seen as availability of funds for risky assets (measured by its corresponding quantity or price such as the risk premium), while “official global liquidity” is related to the availability of funds for safe assets. 15This shift coincides with the burst of the “dot com” equity bubble and the associated monetary policy response in the United States. House price inflation in advanced economies, however, had taken off about five year earlier, in the mid-1990s (see Figure 2). 16For a discussion and an empirical analysis of their relative importance see BIS (2011) and Cerutti, Claessens, and Ratnovski (2014), respectively. 13 Trillions US DOllars 1995 1997 1999 2001 2003 2005 2007 2009 2011 6 8 10 12 14 16 18 Percent 1995 1997 1999 2001 2003 2005 2007 2009 2011 −30 −20 −10 0 10 20 30 Constant prices (2008:Q2 US Dollars) Growth rate (year−on−year) Figure 4: GLOBAL LIQUIDITY. Foreign claims (loans and deposits, in all currencies) of all BIS reporting banks vis--vis the banking sector deflated by U.S. consumer price inflation, aggregated by all receiving countries. The upper panel displays the stock in constant 2008:Q2 US Dollars; the lower panel displays the year-on-year rate of growth. Source: BIS Locational banking Statistics, Tables 7A and 7B. demand derives from those with deficits. An increase in the international supply of credit should therefore be associated with a swing into deficit of the receiving country’s current account balance. In addition, an increase in the international supply of credit should relax any pre-existing credit constraint, domestic or international. Another set of linkages is with the exchange rate and interest rates. With an increase in the international supply of credit, one should in principle observe a fall in the price of these funds. In practice, while the exchange rate should appreciate in response to an increase in capital inflows, short-term interest rates might go in different directions. If market forces dominate, interest rates might fall in response to an increase in the supply of capital, possibly also reflecting lower default and credit risk. Interest rates could also fall if the central bank reacts to an exchange rate appreciation by loosening the monetary policy stance 14 accordingly, the more so the stronger the commitment to the exchange rate and the degree of international capital mobility. However, if the central bank were to react to the increased level of economic activity and inflation triggered by the capital inflow by tightening its stance, interest rates could also increase. As a result, the response of short-term interest rates to a capital inflow shock will depend on which of these effects dominates. The last link in the chain is between the international supply of credit and house prices. Abstracting from tax considerations and credit constraints, house prices depend on interest rates, price-to-rent ratios, and expected appreciation. Global liquidity can affect house prices via all these channels, lowering interest rates, inducing expected appreciation, and pushing up rents due to increased overall level of economic activity. In addition, a global liquidity shock may relax credit constraints directly or indirectly by increasing the value of collateral for domestic and international lending, enabling previously constrained households and financial intermediaries to increase their effective demand for housing. Note here that both house prices and the exchange rate can affect the value of collateral and therefore amplify an initial shock via the relaxation of a credit constraint. 6 Global Liquidity, House Prices and Consumption Dynamics In Sections 3and 4we provided evidence that the unconditional volatility of real house price inflation and the correlation between house prices and capital flows is stronger in emerging markets than in advanced economies. In this section, we will condition the analysis on a particular shock to capital flows. To investigate the causal link from capital flows to house prices and the broader macroeconomy, we specify and estimate a panel-vector autoregression model (PVAR) that embeds both “pull” and “push” factors, as usually assumed in the capital flows literature (e.g., Calvo, Leiderman, and Reinhart,1996). Next we identify a shock to a particular push factor, i.e., a shock to global liquidity defined as a shift in the international supply of credit. We then trace its impact on house prices, consumption, interest rates, the exchange rate and the current account. We now present the model that we use and then report the empirical results. 6.1 The Empirical Model Empirical models of international capital flows typically include “push” (i.e., external) and “pull” (i.e., domestic) drivers. They are often expressed and summarized in terms of cross-country differences: interest rate differentials and growth differentials, as well as competitiveness measures reflecting differences in productivity and costs between the home and the foreign economy. The PVAR model that we specify includes three external variables and three domestic variables. In addition to the measure of global liquidity discussed in the previous section, the external variables that we include are the real effective exchange rate and the current account to GDP. The real effective exchange rate is a measure of relative competitiveness that reflects movements in inflation rates, production costs, and nominal exchange rates of all trade partners. The current account is the gap between investments and savings, and hence also reflects the differences in investment opportunities at home and abroad. Both 15 variables are affected by other domestic and external shocks, but we do not identify other shocks separately in our analysis. The domestic variables that we include in our PVAR model are a real (ex post) short-term interest rate, real private consumption, and real house prices. Real private consumption is the measure of economic activity that we focus on. House prices affect activity primarily through consumption and residential investments. As we do not have data on residential investments for all the emerging economies in our sample, an alternative specification would include both consumption and GDP. To keep the size of the VAR model as small as possible, we include only consumption. For the same reason, we do not include inflation and nominal interest rate separately. Thus, the real ex post short-term interest rate is meant to reflect the monetary policy stance. A stabilizing monetary policy response should manifests itself with a change in the real interest rate. While real house prices are the focus of our analysis, they can be thought as the relative price of durable goods in the general equilibrium models with housing of Iacoviello (2005), Monacelli (2009), and Liu, Wang, and Zha (2013). All real variables considered enter the VAR in log-levels, except the interest rate and the current account to GDP, which enter in levels. Following Sims, Stock, and Watson (1990), we estimate the VAR systems in levels without explicitly modeling the possible cointegration relations among them.17 But the specification is balanced, in the sense that all series have the same expected order of integration. In addition to a constant, we include a linear and a quadratic time trend to capture any long-term tendency in real consumption and house prices, and the exponential increase in global liquidity from 2001 to 2007 (Figure 4). Nonetheless, for robustness, we also estimate a specification in first differences with a constant and a linear trend. The model is the same for all countries to avoid introducing differences in country responses due to different specifications, and because it would be difficult to find a perfectly data-congruent specification for all countries in the sample. In particular, somewhat arbitrarily, but mindful of the shorter sample period for some of the emerging economies in the sample, we include two lags of each variable in every system. 6.1.1 Estimation To estimate the model, we use the mean group estimator of Pesaran and Smith (1995) and Pesaran, Smith, and Im (1996). This is because pooled estimators may be inconsistent in a dynamic panel data model with heterogeneous slope coefficient (i.e., slope coefficients that vary across countries).18 This technique involves estimating country-by-country the VAR model above, with ordinary least squares, and then taking simple averages of the impulse responses across countries. One could also compute weighted averages, weighting by the inverse of the standard error of the individual estimate, or by the size of the unit in the cross-section, usually yielding similar results. Econometric 17Sims, Stock, and Watson (1990) show that if cointegration among the variables exists, the system’s dynamics can be consistently estimated in a VAR in levels. 18The literature that extends this estimation approach to PVAR models is surveyed by Coakley, Fuertes, and Smith (2006) and Canova and Ciccarelli (2013). Rebucci (2010) provides Monte Carlo evidence on the performance of this estimator relative to a fixed effect estimator and a simple instrumental variable estimator. 16 theory suggests that the weights should not matter as the size of the cross-section increases (as long as no unit dominates). For reasons that we discuss below, we prefer to use equal weighting, censoring the impulse responses, rather than weighting with the size of the economy. As we will see, however, even censoring the estimates to eliminate the effects of outliers, has a negligible impact on the results. The variance of the mean group estimator can be calculated by taking the cross-section variance of the point estimates (e.g., the variance across countries, for each time horizon, of the impulse response) and dividing it by (N−1), where Nis the number of countries. As Pesaran and Smith (1995) and Pesaran, Smith, and Im (1996) prove, this adjustment yields a consistent estimate of the true cross-section variance of the mean group estimate. If a country VAR has unstable roots or fewer than 30 observations, the country is dropped from the sample. 6.1.2 Identification While the shock that we want to identify is a shift in the international supply of credit, empirically crossborder banking credit is affected by both demand and supply factors. Therefore we need a way to isolate an innovation to this variable that reflects shifts in the supply curve. In order to identify a global liquidity shock from these data, we take two steps. First we attenuate the influence of country-specific factors by aggregating lending to all receiving countries in our sample as in Figure 4. As long as countries are not too large, innovations to these variables should not be contaminated by domestic shocks. Given this assumption, a global liquidity shock and associated impulse responses of all the other variables in the system can then be derived easily from the Cholesky decomposition of the variance covariance matrix of the estimated reduced-form residuals of each countryspecific VAR, with global liquidity ordered first in the system. Note that this is equivalent to assuming that all the variation in the reduced-form residuals of the global liquidity equation are driven by external supply factors. Second, to rule out that demand factors common among all countries in the sample or that any particular country affects the aggregate measure, we also use the external instruments identification approach proposed by Stock and Watson (2012) and Mertens and Ravn (2013).19 This identification strategy (whose details are reported in Appendix C) uses standard instrumental variable techniques to isolate the variation of the VAR reduced-form residuals that are due to the structural shock of interest. In this way it is possible to identify the contemporaneous response of all endogenous variables in the VAR system to the shock of interest. To obtain the impulse responses at longer horizons, one can then simply simulate the VAR system forward as many steps as needed. Consistent with the empirical literature on global liquidity discussed before, the candidate instruments that we consider are the U.S. effective federal funds rate, the log difference of U.S. M2, the log-level and the log-difference of U.S. broker-dealers’ leverage, the slope of the U.S. yield curve, the log-level and 19For recent applications of this identification strategy see also Gertler and Karadi (2015). 17 the log difference of the VIX, and the TED spread.20 The specific instruments used are then selected with a simple procedure that chooses the combination of variables (among all the possible combinations) that gives the highest F-Statistic in the first-stage regression. Focusing on global liquidity, therefore, allows us to specify a small VAR that embeds information on a wider set of global factors affecting the international financing conditions, which is an important modeling consideration. Given that our set of instruments is made up of U.S. variables, it is hard to isolate the “foreign” component of the shock to the U.S. system. One possibility would be to take the residual rather than the fitted value of the first-stage regression for this country. This should isolate, albeit crudely, the portion of the reduced-form residual of the global liquidity equation in the U.S. VAR system that is orthogonal to the U.S. drivers of global liquidity (i.e., movements in global bank-to-bank cross-border flows “pushed” by non-U.S. conditions). However, recognizing that this is a rough way to treat the problem in the case of the United States, we opt for excluding the United States from the PVAR for the computation of the mean group estimate.21 6.2 Estimation Results The model is first estimated separately for all countries for which we have more than 30 observations, using quarterly data for the period 1995:Q4 to 2012:Q4. The choice of the starting date stems from the availability of the bank-to-bank data that we use to construct our global liquidity measure, which starts in 1995. Morocco and Serbia have very short house price series and current account to GDP series, respectively, and need to be dropped from the conditional analysis, leaving us with 31 emerging economies in the sample. We then check the stability of the system and drop countries that display unstable dynamics. The countries dropped after this second step are Austria, Brazil, and Japan. This leaves us with 30 EMs and 21 AEs in the sample. Equipped with the reduced-form residuals from the OLS estimation of the VAR system countryby-country, we can run the first-stage regressions described by equation (C.6) in Appendix C. Table 2summarizes the results. The R2of these regressions are relatively low, but the F-statistics are reasonably high, especially taking into account that the procedure is applied to a large set of countries, averaging 0.05 and 3.7for advanced economies and 0.06 and 3.7for emerging economies, respectively.22 The coefficients of these regressions are not reported, but generally have the expected sign. We are now ready to discuss the impulse response functions to our global liquidity shock. We first look at impulse responses in which the shock is identified only with the first step discussed above, i.e., the Cholesky decomposition of the reduced-form residuals with global liquidity ordered first. We use a simple average of the country-specific estimates to construct the mean group estimates. We also censor the 20We enter leverage both in log-levels and log-differences following Bruno and Shin (2015). In the case of the VIX index, we found that entering both levels and first difference improves the performance of the first-stage regressions. 21Results including the United States are very similar and available from the authors on request. 22Note that the R2are well below 0.1 also in the regressions of (see Cerutti, Claessens, and Ratnovski,2014), who model this variable with a much larger set of co-variates. 18 responses included in this average at the 10 percent level (5 percent each side) to eliminate the possible influence of any outlier on the averages. Table 2: INSTRUMENTAL VARIABLE ESTIMATION – FIRST-STAGE STATISTICS (a) Advanced Economies R2 F-Statistic R2 F-Statistic Australia 0.06 5.10 Japan – – Austria – – Luxembourg 0.01 1.52 Belgium 0.03 3.14 Malta 0.02 2.42 Canada 0.05 2.59 Netherlands 0.02 2.41 Denmark 0.02 2.38 New Zealand 0.01 1.60 Finland 0.02 2.36 Norway 0.13 10.66 France 0.06 2.98 Portugal 0.06 4.94 Germany 0.06 5.02 Spain 0.04 3.44 Greece 0.05 4.56 Sweden 0.05 4.45 Iceland 0.18 4.53 Switzerland 0.05 2.66 Ireland 0.05 4.78 United Kingdom 0.05 4.67 Italy 0.02 2.66 United States – – (b) Emerging Markets R2 F-Statistic R2 F-Statistic Argentina 0.06 5.37 Malaysia 0.14 9.43 Brazil – – Mexico 0.11 2.80 Bulgaria 0.06 5.51 Morocco – – Chile 0.03 3.19 Peru 0.02 1.90 China 0.00 1.17 Philippines 0.05 4.61 Colombia 0.08 6.97 Poland 0.11 3.78 Croatia 0.02 2.34 Russia 0.14 8.16 Czech Republic 0.06 4.44 Serbia – – Estonia 0.04 3.35 Singapore 0.01 1.17 Hong Kong 0.05 4.35 Slovakia 0.04 1.35 Hungary 0.03 2.72 Slovenia 0.05 4.55 India 0.02 1.87 South Africa 0.01 1.95 Indonesia 0.06 4.99 Taiwan 0.03 2.67 Israel 0.06 2.08 Thailand 0.02 1.57 Korea 0.08 6.64 Ukraine 0.04 2.96 Latvia 0.03 2.55 Uruguay 0.12 2.89 Lithuania 0.10 4.01 Note. Selected results from the first stage regression as in equation (C.6) in the appendix. For each country we report the R2of the regression and the F-Statistics associated with the null hypothesis that all estimated coefficients are equal to zero. Figure 5reports these results for the typical (i.e., average) advanced and emerging economy— see panel (a) and panel (b), respectively. The dark and light shaded areas represent the 1 and 2 standard deviations confidence intervals, respectively. The dashed line is the uncensored impulse response function. 19 Global Liquidity Percent Quarters 5 10 15 20 0 0.2 0.4 0.6 0.8 1 Consumption Percent Quarters 5 10 15 20 0 0.02 0.04 0.06 0.08 House Price Percent Quarters 5 10 15 20 0 0.05 0.1 0.15 Real Int. Rate Percent Quarters 5 10 15 20 0 0.01 0.02 0.03 0.04 0.05 Real Eff. Exch. Rate Percent Quarters 5 10 15 20 0 0.05 0.1 0.15 0.2 0.25 (a) Advanced Economies Current Account / GDP Percent Quarters 5 10 15 20 −0.06 −0.04 −0.02 0 0.02 0.04 Global Liquidity Percent Quarters 5 10 15 20 0 0.2 0.4 0.6 0.8 1 Consumption Percent Quarters 5 10 15 20 0 0.05 0.1 0.15 House Price Percent Quarters 5 10 15 20 −0.2 0 0.2 0.4 Real Int. Rate Percent Quarters 5 10 15 20 −0.05 0 0.05 Real Eff. Exch. Rate Percent Quarters 5 10 15 20 0 0.05 0.1 0.15 0.2 (b) Emerging Economies Current Account / GDP Percent Quarters 5 10 15 20 −0.1 −0.05 0 0.05 Figure 5: GLOBAL LIQUIDITY SHOCK: CHOLESKY IDENTIFICATION. Censored impulse responses to a 1 percent shock to global liquidity in advanced and emerging economies, panel (a) and panel (b) respectively. The dark and light shaded areas are the one and two standard deviation confidence intervals. The dashed line reports the uncensored impulse responses. 20 In the typical advanced economy (panel (a) of Figure 5), both real consumption and house prices increase in response to the global liquidity shock in a statistically significant manner, but the effect is relatively short-lived, losing statistical significance within 5-6 quarters. Specifically, consumption and house prices peak at 0.06% and about 0.1% above their long-term levels within two-three quarters. The response of the short-term real interest rate is initially mute. It then increases slowly but steadily for 3-4 quarters tracking consumption and house prices, peaking at about 4basis points above its long-term level. This is consistent with a monetary policy authority reacting to the acceleration of economic activity triggered by the capital inflows. But it could also reflect reverse causation from domestic monetary policy to capital inflows not adequately addressed by the first step of our identification strategy. The real effective exchange appreciates on impact, arguably driven by the nominal exchange rate, peaking at about 0.2% above its long-term level, and then reverts to its equilibrium level over time. The current account displays a delayed but persistent decline, with a deficit close to 0.05% of GDP at the trough of the response. The response of the typical emerging market economy to the same shock is qualitatively and quantitatively different (panel (b) from Figure 5). The peak responses of house prices and consumption are much stronger in emerging economies, about twice as large as in advanced economies. The interest rate response is initially negative, possibly reflecting the impact of the increased supply of credit or the desire of the monetary authority to avoid making the domestic currency even more attractive to foreign investors. It then increases gradually to the same level reached in advanced economies, reverting to its long-term level much more slowly. The exchange rate response is mute on impact, consistent with the lower degree of flexibility of the nominal exchange rate in this group of countries. But then it appreciates over time, peaking only slightly below its level in advanced economies. The current account balance displays a different boom-bust response. It first swings into a deficit twice as large as in advanced economies, and then reverts sharply into a sizable surplus. By assuming that the reduced-form residual of the global liquidity equation is entirely driven by international supply factors, the Cholesky identification might distort its estimated effects. When we refine the identification strategy by instrumenting the residual of the global liquidity equation, we actually find remarkably similar impacts and, in a few cases, even stronger effects (panel (a) and (b) of Figure 6). The estimated impact of the shock on consumption in Figure 6, in particular, is twice as large as in Figure 5, in both advanced and emerging economies. Other variables’ responses are also now slightly stronger in advanced economies. In emerging economies, the interest response becomes even more negative and less precisely estimated, reflecting the heterogeneity in the country sample. The current account swing into deficit is now much larger than in advanced economies. Similarly, emerging economies’ house price response is now 3 times larger than in advanced economies. These results are intuitive, as advanced economies are those for which domestic pull factors might invalidate the use of the Cholesky decomposition for the identification of our global liquidity shock. 21 into house prices and supports the international borrowing capacity of the economy. 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We consider 57 countries in our empirical analysis: 24 advanced economies (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Malta, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, UK, and US) and 33 emerging economies (Argentina, Brazil, Bulgaria, Chile, China, Colombia, Croatia, Czech Republic, Estonia, Hong Kong, Hungary, India, Indonesia, Israel, Korea, Latvia, Lithuania, Malaysia, Mexico, Morocco, Peru, Philippines, Poland, Russia, Serbia, Singapore, Slovakia, Slovenia, South Africa, Taiwan, Thailand, Ukraine, and Uruguay). We collect data over the 1990:Q1 – 2012:Q4 (subject to data availability) for the following variables: •House prices. Nominal house prices deflated by consumer price inflation. Source: OECD house price database, BIS Residential property price statistics, Dallas FED International House Price Database, National Central Banks, National Statistical Offices, academic and policy publications. More details on the definitions and the sources are reported in Table A.1. •Total cross-border banking flows. Foreign claims (all instruments, in all currencies) of all BIS reporting banks vis-` a-vis all sectors deflated by US consumer price inflation. Source: BIS. •Global liquidity. Foreign claims (loans and deposits, in all currencies) of all BIS reporting banks vis-` a-vis the banking sector deflated by US consumer price inflation. Source: BIS. •GDP. Real index. Source: OECD, IMF IFS, Bloomberg. •Consumption. Real private final consumption index. Source: OECD, IMF, IFS, Bloomberg. •Consumer prices. Consumer price index. Source: OECD, IMF IFS, Bloomberg. •Short-term interest rates. Short-term nominal market rates. A real ex-post interest rate is obtained by subtracting consumer price inflation. Source: OECD, IMF, IFS, Bloomberg. •Equity prices. Equity price index deflated by consumer price inflation. Source: OECD, IMF IFS, Bloomberg. •Exchange rate vis-` a-vis US dollar. US dollars per unit of domestic currency. A real exchange rate is obtained with US and domestic consumer price inflation. Source: Datastream. •Real effective exchange rate. Index (such that a decline of the index is a depreciation). Source: IMF IFS, BIS, Bloomberg. •Current account to GDP ratio. Current account balance divided by nominal GDP. Source: OECD, IMF IFS, Bloomberg. 31 Table A.1: HOUSE PRICE DATA: DEFINITIONS AND SOURCES Country Definition Source Argentina House Apartments in Buenos Aires City, average price per sqm (US Dollars). Arklems Australia House Price Indexes: Eight Capital Cities. OECD Austria Residential property prices, new and existing dwellings. OECD Belgium Residential property prices, existing dwellings, whole country. OECD Brazil Residential Real Estate Collateral Value Index. Central Bank Bulgaria Residential property price, existing flats (big cities), per sqm. BIS Canada Average existing home prices. OECD Chile HPI general, houses and apartments. Central Bank China House price index. OECD Colombia House Price Index. Central Bank Croatia House price index Dallas FED Czech Rep. Residential property prices, existing dwellings, whole country. OECD Denmark Price index for sales of property. OECD Estonia Residential property prices, all dwellings, per sqm. BIS Finland Prices of dwellings. OECD France Indice trimestriel des prix des logements anciens. OECD Germany Residential property prices in Germany. OECD Greece Prices of dwellings. OECD Hong Kong Residential property price, all dwellings, per sqm. BIS Hungary Residential property price, all dwellings, per sqm. BIS Iceland Residential property price, all dwellings (Reykjavk), per sqm. BIS India Residex. Nat. Hous. Bank Indonesia Residential property prices, new houses (big cities), per dwelling. BIS Ireland Residential property price index. OECD Israel Prices of dwellings. OECD Italy Residential property prices, existing dwellings, whole country. OECD Japan Urban Land Price Index. OECD Korea House price index. Dallas FED Latvia Residential property prices, new and existing flats, whole country. ECB Lithuania Residential property price, all dwellings, per sqm. BIS Luxembourg House price index. Dallas FED Malaysia Residential property prices, all dwellings, per sqm. BIS Malta Property Prices Index (based on advertised prices). Central Bank Mexico Residential property prices, all dwellings, per dwelling. BIS Morocco Residential property prices, existing dwellings, per sqm. BIS Netherlands House Price Index for existing own homes. OECD New Zealand House price index. OECD Norway House price index. OECD Peru Residential property prices, per sqm. BIS Philippines Residential and commercial property prices, flats (Makati), per sqm. BIS Poland Residential property prices, (big cities), per sqm. BIS Portugal Residential property prices, new and existing dwellings. BIS Russia Residential property prices, existing dwellings, per sqm. BIS Serbia Average prices of dwellings in new construction, per sqm. Nat. Stat. Office Singapore Average prices of dwellings in new construction, per sqm. BIS Slovak Rep. Residential property prices, existing dwellings. OECD Slovenia House price index. OECD South Africa Residential property price. BIS Spain Precio medio del m2 de la vivienda libre (>2anos de antiguedad). OECD Sweden Real estate price index for one and two dwelling buildings for permanent living. OECD Switzerland Real estate price indices. OECD Taiwan National House Price Index. Synyi Thailand Residential property prices, average of all detached houses, per sqm. BIS Ukraine Average Price of Apartments, Kiev, per sqm (US Dollars). Blagovest UK Mix-adjusted house price index. OECD US Purchase and all-transactions indices. OECD Uruguay Precio promedio del metro cuadrado de compraventas, Montevideo (US Dollars). Nat. Stat. Office Note. See the extended appendix on the sources of house price series extended with historical data. 32 B Appendix. Robustness Table B.1: SUMMARY STATISTICS 1990-2006 (a) Advanced Economies House Prices Equity Prices Consumption GDP Mean 0.7% 1.4% 0.7% 0.7% Median 0.8% 1.8% 0.7% 0.7% St. Dev. 1.8% 8.3% 1.0% 1.0% Auto Corr. 0.5 0.4 0.1 0.2 Pairwise Corr. 0.1 0.6 0.1 0.2 (b) Emerging Markets House Prices Equity Prices Consumption GDP Mean 1.2% 2.0% 1.2% 1.0% Median 1.0% 2.4% 1.3% 1.3% St. Dev. 4.8% 14.1% 2.7% 2.0% Auto Corr. 0.3 0.2 0.1 0.2 Pairwise Corr. 0.1 0.3 0.1 0.1 Note. Averages of country-specific statistics within group. All variables are in log-difference and seasonally adjusted. House prices and equity prices are deflated with CPI (also seasonally adjusted). Real private consumption and real GDP are from the national accounts. Sample period is 1990:Q1 – 2006:Q4. 33 Table B.2: SUMMARY STATISTICS 1990-2012 (NO INTERPOLATED DATA) (a) Advanced Economies House Prices Equity Prices Consumption GDP Mean 0.4% 0.1% 0.5% 0.5% Median 0.5% 1.3% 0.6% 0.6% St. Dev. 1.9% 10.1% 1.1% 1.1% Auto Corr. 0.6 0.4 0.2 0.3 Pairwise Corr. 0.2 0.7 0.2 0.3 (b) Emerging Markets House Prices Equity Prices Consumption GDP Mean 0.6% 0.5% 1.1% 0.9% Median 0.5% 1.4% 1.2% 1.2% St. Dev. 4.8% 15.0% 2.4% 2.1% Auto Corr. 0.3 0.3 0.1 0.3 Pairwise Corr. 0.1 0.5 0.1 0.2 Note. Averages of country-specific statistics within group. All variables are in log-difference and seasonally adjusted. House prices and equity prices are deflated with CPI (also seasonally adjusted). Real private consumption and real GDP are from the national accounts. Sample period is longest series available for house prices in 1990:Q1. House price series obtained from interpolation of annual data are dropped. 34 Table B.3: SUMMARY STATISTICS 1995-2012 (a) Advanced Economies House Prices Equity Prices Consumption GDP Mean 0.5% 0.3% 0.5% 0.5% Median 0.7% 1.6% 0.6% 0.6% St. Dev. 1.8% 10.4% 1.1% 1.0% Auto Corr. 0.6 0.4 0.2 0.3 Pairwise Corr. 0.2 0.7 0.2 0.4 (b) Emerging Markets House Prices Equity Prices Consumption GDP Mean 0.6% 0.1% 1.1% 1.0% Median 0.6% 1.2% 1.2% 1.2% St. Dev. 4.8% 14.1% 2.1% 1.9% Auto Corr. 0.3 0.3 0.2 0.3 Pairwise Corr. 0.1 0.5 0.1 0.2 Note. Averages of country-specific statistics within group. All variables are in log-difference and seasonally adjusted. House prices and equity prices are deflated with CPI (also seasonally adjusted). Real private consumption and real GDP are from the national accounts. Sample period is longest series available for house prices in 1995:Q1. 35 C Appendix. Identification Consider the following reduced form VAR (with only one lag and no constant or trend for simplicity): xt=Fxt−1+ut,(C.1) where xtis a (m×1) vector of endogenous variables; Fis a (m×m)matrix of coefficients; and utis a (m×1) vector of residuals with variance-covariance matrix Σu. The objective is to recover the structural form of the above VAR, i.e.: Axt=Bxt−1+εt,(C.2) where Aand Bare (m×m)matrices of coefficients; and εtis an (m×1) vector of structural residuals with variance-covariance matrix Σε=I. Note that the reduced form residuals are a linear combination of the structural residuals. Specifically, letting ˜ A=A−1, we have that ut=˜ Aεt. If we partition the vector of endogenous variables xtas (GL0 t, x0 p,t)0—where GLtis global liquidity and xp,t is the (m−1×1) vector of remaining endogenous variables— we can re-write the reduced-form VAR as: GLt xp,t =f11 f12 f21 f22  GLt−1 xp,t−1+˜a11 ˜a12 ˜a21 ˜a22  εGL t εxp t,(C.3) where f11 and ˜a11 are scalars; f12 and ˜a12 are (1 ×m−1) vectors; f21 and ˜a21 are (m−1×1) vectors; f22 and ˜a22 are (m−1×m−1) matrices; and εGL tand εxp tare the structural residuals associated to global liquidity and the remaining endogenous variables, respectively. For the sake of argument, let’s assume that the structural matrix ˜ Ais known. Then, we would be able to compute the impulse response to a global liquidity shock. Specifically, the contemporaneous responses of GL and xpto a unit shock to εGL would be given by: IRFGL 0 IRFxp 0=˜a11 ˜a21 , which, since the model is linear, can be normalized to: IRFGL 0 IRFxp 0=1 ˜a21 ˜a11 .(C.4) Finally, the impulse response functions at longer horizons can be computed as: IRFn=Fn−1·IRFn−1for n= 2, ..., N. (C.5) Note that if we are interested in computing the impulse responses to the global liquidity shock only we do not need to know all the coefficients of ˜ A, but rather only the elements of the first column of ˜ A, namely ˜a1. We now consider the case of ˜ Aunknown. To achieve identification, we follow the external instrument identification approach pioneered by Stock and Watson (2012) and Mertens and Ravn (2013). Let uGL and uxpbe the OLS estimates of the reduced form residuals in (C.1). Also, let Ztbe a (z×1) vector of instrumental variables that satisfy: EεGLZ0 t=φ, E[εxpZ0 t]=0, 36 i.e., the instruments are correlated with the global liquidity shock (εGL) but are orthogonal to all the other domestic shocks (the elements of εxp). We can obtain consistent estimates of ˜a1from the two-stage least squares regression of uxpon uGL using Ztas instruments. In other words, since the reduced form residuals of the global liquidity equation (uGL t) are an imperfect measure of true structural shock (εGL), in the first stage we regress them on the set of instruments (Zt): uGL t=βZt+ξt,(C.6) to construct the fitted values ˆuGL t. Then we regress the reduced form residuals of the domestic equations (uxp t) on the fitted values (ˆuGL t) to get a consistent estimate of the ratio ˜a21/˜a11: uxp t=˜a21 ˜a11 ˆuGL t+ζt,(C.7) where note that ˆuGL tis orthogonal to ζtunder the assumption that E[εxpZ0 t]=0. Finally, we can use the OLS estimates of the matrix Fto compute the impulse response functions of all variables to a global liquidity shock using the formula in (C.5). 37