Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Foreign-exchange reserves wikipedia , lookup
Bank for International Settlements wikipedia , lookup
Bretton Woods system wikipedia , lookup
Exchange rate wikipedia , lookup
Fixed exchange-rate system wikipedia , lookup
Nouriel Roubini wikipedia , lookup
International status and usage of the euro wikipedia , lookup
Temi di discussione del Servizio Studi Monetary policy shocks in the euro area and global liquidity spillovers by João Sousa and Andrea Zaghini Number 629 - June 2007 The purpose of the Temi di discussione series is to promote the circulation of working papers prepared within the Bank of Italy or presented in Bank seminars by outside economists with the aim of stimulating comments and suggestions. The views expressed in the articles are those of the authors and do not involve the responsibility of the Bank. Editorial Board: D OMENICO J. M ARCHETTI , M ARCELLO B OFONDI , M ICHELE C AIVANO , S TEFANO I EZZI , ANDREA LAMORGESE, FRANCESCA LOTTI, MARCELLO PERICOLI, MASSIMO SBRACIA, ALESSANDRO SECCHI, PIETRO TOMMASINO. Editorial Assistants: ROBERTO MARANO, ALESSANDRA PICCININI. MONETARY POLICY SHOCKS IN THE EURO AREA AND GLOBAL LIQUIDITY SPILLOVERS by João Sousa* and Andrea Zaghini** Abstract We analyse the international transmission of monetary policy shocks with a focus on the effects of foreign liquidity on the euro area. We estimate two domestic structural VAR models for the euro area and then we introduce a global liquidity aggregate. The impulse responses show that a positive shock to foreign liquidity leads for the euro area to a permanent increase in M3 and in the price level, a temporary rise in real output and a temporary appreciation of the euro real effective exchange rate. Moreover, we find that innovations in global liquidity play an important role in explaining price and output fluctuations. JEL Classification: E52; F01. Keywords: monetary policy, structural VAR, international spillovers. Contents 1. Introduction.......................................................................................................................... 3 2. Two benchmark models for the euro area ........................................................................... 4 3. Global liquidity spillovers ................................................................................................. 12 4. Conclusion ......................................................................................................................... 19 Appendix ................................................................................................................................ 21 References .............................................................................................................................. 24 _______________________________________ * Banco de Portugal, Research Department * Bank of Italy, Economic Research Department. 1 Introduction1 Recently, there has been an increasing interest in the sources of international business ‡uctuations on the one hand, and in the role played by international spillovers of monetary policy shocks on the other hand. Mounting evidence suggest that the cross-country transmission of shocks plays an important role in international business ‡uctuations, but so far only a limited number of studies have examined the role of shocks to monetary aggregates in driving business ‡uctuations or, more generally, in in‡uencing the behaviour of macroeconomic and …nancial variables in other countries.2 The weak performance of money demand models in many countries in terms of stability and the generally low explanatory power of monetary models of exchange rate determination partly explain this circumstance. This contrasts with a recent research strand, which focuses on the role of money as an indicator of macroeconomic development in closed-economy models (Trecroci and Vega; 2000, Amato and Swansson; 2001, Dotsey and Hornstein; 2003). This paper provides an attempt to …ll this gap by studying the international transmission of monetary shocks with a special focus on the e¤ects of foreign money (“global liquidity”) on the euro area economy. There are several reasons why monetary developments abroad should be taken into account by an open economy. Given the high level of integration attained in …nancial markets, cross-country capital ‡ows may have non-negligible e¤ects on domestic asset prices or monetary aggregates. In a …rst channel of transmission (the “push”channel), high monetary growth in one area may lead to capital ‡ows into foreign countries, thus resulting in stronger monetary growth and higher asset returns abroad; while according to the “pull”channel, high domestic monetary growth may lead to domestic asset price in‡ation and, as a result, attract foreign capital, thereby depressing asset prices in the countries where the capital ‡ows originated (Baks and 1 We are grateful to Alessandro Calza, José Luis Escrivá, Michael Ehrmann, Leonardo Gambacorta, Hans-Joachim Klöckers, Livio Stracca and Larry D. Wall for useful comments and helpful discussions, as well as to participants to the XII International Tor Vergata Conference on Banking and Finance and the XIX European Economic Association Congress. This paper does not necessarily re‡ect the views of Banco de Portugal and Banca d’Italia. 2 After an early attempt by McKinnon (1982), only recently the literature has recorded contributions in this …eld (Kim and Roubini; 2000, Kim; 2001, Holman and Neumann; 2002, Canova; 2005, Ehrmann and Fratzscher 2006). 3 Kramer; 1999). These e¤ects operate not only at times of stress in …nancial markets (as witnessed for instance by the quick spreading of the Asian crisis in many countries of the South-East Asia and other emerging markets economies in 1997), but also in “normal times”. Furthermore, in the absence of capital ‡ows between regions, the very existence of common international exogenous shocks may lead to co-movements of monetary aggregates in di¤erent countries. From a single country perspective, such co-movements can be exploited to reveal information about the source of the shocks hitting the domestic economy. For instance, shocks associated to international stock price volatility may lead to increases in both domestic and foreign monetary aggregates due to a worldwide increased preference for liquid assets. In this case information on foreign monetary developments may help to con…rm that such liquidity preference shock was the likely cause of the observed ‡uctuation in domestic monetary aggregates. The aim of this paper is thus to study the international spillover e¤ects on the euro area economy due to changes in foreign monetary aggregates. We choose to do so within the context of structural vector autoregressions (SVARs). We …rst propose two models that are taken as a benchmark for the euro area using only domestic variables (i.e. prices, output, money, the short-term interest rates and the exchange rate). From these models it is possible to identify the “true” exogenous monetary policy shocks over the period 1980-2001. Then, following a marginal approach (Kim; 2001), we add to the block of endogenous variables a global liquidity aggregate (i.e. an aggregation of broad monetary aggregates of major economies expressed in the same currency) and analyse how euro area variables respond to shocks to foreign money. The paper is organised as follows: Section 2 proposes two benchmark models for the euro area, Section 3 introduces a cumulative monetary aggregate and studies the impact on euro area macroeconomic variables of a global liquidity shock, Section 5 concludes. 2 Two benchmark models for the euro area In this section we propose two benchmark models to analyze the monetary policy transmission mechanism within the euro area. We rely on the following Structural VAR scheme: 4 Kut = et ; (1) where K is the matrix of the contemporaneous relations among variables, ut is the vector of the reduced form residuals and et is the vector of the “true” structural shocks.3 The SVAR methodology allows us to impose restrictions only on the contemporaneous structural parameters, so that reasonable economic structures might be derived. The standard Cholesky decomposition imposes a lower triangular K matrix, while a more general set of relations might be introduced given that the same number of restrictions are maintained. Thus we have to rely on a set of assumptions on the structural model of the economy. In particular, the reaction function of the monetary authority has to be speci…ed. This feedback rule explains the endogenous response of the monetary authority to changes in a given set of variables and thus relates policy-makers’ actions to the state of the economy. This in turn implies making assumptions about which variables the monetary authority looks at when setting its operational instrument. However, the basic idea underlying the model is that not all changes in the central bank policy stance re‡ect the systematic response to variations in the state of the economy: the unaccounted alteration is formalized with the notion of monetary policy shock. The most common interpretation of a policy shock is an exogenous change in the preferences of the monetary authority, due, for instance, to a shift in the relative weight given to in‡ation and unemployment.4 The …rst scheme (Model 1) we propose to identify monetary policy shocks derives from Kim (1999). Kim’s model is an ideal starting point for euro area aggregate analysis, since it is based on a common set of identifying restrictions that worked well for the G7 countries. It shows the following 3 This speci…cation scheme is probably the most used in the monetary policy analysis: see among others Gordon and Leeper (1994), Sims and Zha (1998), Leeper and Roush (2003) for the US and Kim and Roubini (2000), Dedola and Lippi (2005), Mojon and Peersman (2003) for other countries. For a comprehensive text-book reference see Amisano and Giannini (1997). 4 See Christiano, Eichenbaum and Evans (1999) for possible alternative explanations. 5 non-recursive structure: 2 1 0 0 0 0 6 a21 1 0 0 0 6 6 a31 a32 1 a34 0 6 4 0 0 a43 1 a45 a51 a52 a53 a54 1 32 76 76 76 76 54 uYt R uPt I 3 uM t SR ut uER t 3 2 7 6 7 6 7=6 7 6 5 4 eYt R ePt I 3 eM t SR et eER t 3 7 7 7 7 5 (2) where Y R is the real GDP, P I is the consumer price index, M 3 is the broad monetary aggregate, SR is the short-term rate, which we assume the monetary authority can freely adjust, and ER is the real e¤ective exchange rate. Both the reduced form and the structural residuals are assumed to follow a standard normal distribution and have a zero mean and a constant variance. The model is estimated by maximum likelihood. The …rst two equations indicate that the real sector reacts sluggishly to shocks in the …nancial variables. The general assumption is that GDP and prices respond to …nancial signals (money, interest rate and exchange rate) only with a lag. For instance, within the quarter …rms do not change their output and prices in response to unexpected changes in …nancial variables or monetary policy due to adjustment costs. The third equation is a money demand function. The demand for money balances depends on real income, the price index and the short-term interest rate, so that only the exchange rate does not enter contemporaneously in the money demand equation. The fourth relationship models the reaction function of the monetary authority, which sets the interest rate after observing the current value of money and the exchange rate. As in Sims and Zha (1998), the choice of this monetary policy feedback rule is based on the assumption of information delays that do not allow the monetary policy to respond within the same period to price level and output developments. That is: published data on money and the exchange rate are available within the period but reliable data on output and prices are not. Finally, in the …fth equation the exchange rate, being an asset price, reacts immediately to changes in all the other variables. The second speci…cation (Model 2) is based on a recursive identi…cation scheme based on the Cholesky decomposition: 6 2 6 6 6 6 4 1 0 0 0 a21 1 0 0 a31 a32 1 0 a41 a42 a43 1 a51 a52 a53 a54 0 0 0 0 1 32 76 76 76 76 54 uYt R uPt I 3 uM t SR ut uER t 3 2 7 6 7 6 7=6 7 6 5 4 eYt R ePt I 3 eM t SR et eER t 3 7 7 7: 7 5 (3) The Cholesky scheme (3) implies, in particular, that monetary policy shocks have no contemporaneous e¤ect not only on output and prices as in model (2), but also on money. They a¤ect the exchange rate within the same quarter, but the policy interest rate does not respond to contemporaneous changes in the exchange rate.5 Estimations are based on quarterly data for the euro area from 1980 Q1 to 2001 Q4 (see the data annex for the details about de…nitions and sources). Data are expressed in logarithmic form and are seasonally adjusted, except the interest rates which are in levels. A constant and a linear trend are added to both models. Standard information tests suggest to adopt a 4-lag length for both VARs. As in the reference studies, in this paper we do not perform an explicit analysis of the long run behaviour of the economy. Nevertheless, the speci…cation in levels allows for implicit cointegrating relationships in the data (Sims, 1990), i.e. we are implicitly assuming that the variables are jointly covariance stationary.6 Figures (1) and (2) display the estimated impulse responses to an unexpected temporary monetary policy shock in both models.7 A 1-time standard deviation increase in the short-term rates is followed by a real appreciation of the exchange rate and a temporary fall in the real GDP. The e¤ect on output reaches the peak after 4 to 6 quarters and returns to baseline afterwards. Prices respond much more sluggishly, and the e¤ect of the shock is only signi…cant in the case of Model 2. Within the …rst year the impact on 5 The Cholesky approach has been followed by Peersman and Smets (2003) in their analysis of euro area monetary transmission mechanism. The main di¤erence with the VAR model used in this study is that they include also a vector of exogenous variables containing a commodities price index and the real GDP and short-term nominal interest rate of the US. 6 In fact, the examination of the residuals of the VARs reveals no evidence of nonstationarities. The same applies for the other VAR models including global liquidity used in this study. For an explicit modelling of the euro area money demand in the long-run see Coenen and Vega (2001) and Calza et al. (2001). 7 The con…dence bands are obtained through a standard bootstrap procedure with 1000 draws. 7 Figure 1: Impulse responses from Model 1 (including 90% con…dence bands) 8 Figure 2: Impulse responses from Model 2 (including 90% con…dence bands) M3 is negative, even though it becomes signi…cant only from the end of the second/beginning of the third year. A typical monetary policy shock is 30 basis points in both models. The maximum impact of the shock on GDP is just above 0.2%, slightly larger than in Peersman and Smets (2003), which estimated a drop in GDP of 0.15%, but smaller than in Monticelli and Tristani (1999), for which the decline was 0.4%. All in all, the estimated responses are very close to expected movements of macro-variables in a monetary policy tightening setting. Thus, the results support the validity of the identifying assumptions for both models. 9 Table 1. Forecast error variance decomposition 1 year Model 1 2 year 3 year 4 year 1 year Model 2 2 year 3 year 4 year Variability of M3 YR PI M3 SR ER 9.5 0.2 89.7 0.5 0.1 33.6 1.9 62.6 1.3 0.5 53.1 4.4 37.5 3.3 1.7 57.7 4.6 30.0 4.6 3.2 9.5 0.2 89.8 0.2 0.3 33.6 1.9 62.8 0.5 1.1 53.1 4.5 37.3 4.5 0.7 57.6 4.6 29.6 7.4 0.8 2.7 75.5 16.0 0.4 5.4 10.7 62.9 22.6 0.4 3.4 23.4 48.9 23.8 1.4 2.4 76.1 2.2 6.0 15.1 0.5 74.3 2.0 6.3 14.3 3.1 73.3 1.9 5.9 14.4 4.4 Variability of prices YR PI M3 SR ER 0.2 86.0 6.3 0.4 7.1 2.7 75.5 16.0 0.8 5.0 10.7 62.9 22.6 0.6 3.1 23.5 48.8 24.0 0.9 2.7 0.2 86.0 6.4 0.9 6.5 Variability of real GDP YR PI M3 SR ER 88.0 3.2 2.1 5.5 1.3 76.2 2.1 6.6 11.0 4.0 74.4 1.9 6.8 9.6 7.2 73.4 1.9 6.4 9.2 9.1 87.9 3.2 1.9 6.5 0.5 The forecast error variance decompositions of the monetary aggregate, prices and real GDP are reported in Table 1. Focusing on the e¤ects of a shock to the short-term interest rate (the policy instrument) we can see that, as in most of the VAR literature, the contribution to the volatility of money, prices and output is rather limited.8 For instance, reading by row the lower panel, Table 1 reports that for both models the contribution of an innovation in interest rates to output ‡uctuation is at most 15% at any horizon. This result is close to that reported by Peersman and Smets (2003) and consistent with the …ndings of Kim (1999) for single G7 countries. Almost negligible 8 See Canova and De Nicoló (2002) for the opposite result. 10 Figure 3: Historical decomposition of SR from Model 1 (including 1-time standard deviation con…dence band) is the contribution to price dynamics both in the short- and long-run (see middle panel). As for the impact of a shock to M3, its e¤ect on prices is somehow stronger: after 4 years the relative contribution to price ‡uctuation is 24% for both models (see upper panel). Figures (3) and (4) depict the historical contribution of the monetary policy shocks to the short-term interest rate in the euro area as identi…ed by the two models. Even though the magnitude of the swings are sometimes di¤erent, the overall picture provided by the recursive and the structural approach is indeed similar.9 Over the period from 1981 to 2001 the two models signal contemporaneously a “tight” stance of the euro area monetary policy on three occasions. In fact, the contribution of monetary policy is above one time the standard deviation for at least two consecutive quarters in the episode of 1987, 1989-90 and 1992-93.10 There are as well four episodes of “easy” monetary policy: 9 The correlation coe¢ cient between the two series is 0.89; the standard deviation is slightly larger for Model 2: 0.59 versus 0.56. 10 Only the recursive approach signals a breaching of the 1-time standard deviation threshold in 1983 Q3-Q4 and in 1998 Q1-Q2. 11 Figure 4: Historical decomposition of SR from Model 2 (including 1-time standard deviation con…dence band) 1984-85, 1991, 1993-94 and 1999.11 Again, the …nding is consistent with the results from Peersman and Smets (2003): they report positive and negative contribution to the short-term rates in the same periods as in this study even though the oscillations seems to be less pronounced in the second half of the 1990s. 3 Global liquidity spillovers In order to investigate the possible e¤ect of foreign liquidity on euro area variables, we follow the “marginal”approach (Kim; 2001) and introduce a sixth variable taking into account the development of extra-euro area monetary aggregates in both benchmark speci…cations of the previous section. This exercise, to our knowledge, is new in the empirical literature on monetary policy.12 11 In this case the non-recursive approach signals an additional episode in 1988 Q1-Q2. Instead, the use of cross-country aggregated data in the econometric analysis of international spillovers is not new in the literature. For recent applications see Kwark (1999), Kim (2001) and Lumsdaine and Prasad (2003). 12 12 The variable used (GL4Y ) is a measure of liquidity outside the euro area corrected for the e¤ect of foreign output (assuming therefore a unit elasticity of money demand with respect to real output in these countries). It is obtained by subtracting the logarithm of real GDP of the remaining four G7 non-euro area countries (US, Japan, UK and Canada) from the logarithm of the weighted sum of monetary aggregates of the same countries (GL4 ). In particular, the global liquidity aggregate is constructed as the simple sum of the reference monetary aggregates using exchange rates vis-à-vis the euro based on purchasing power parities to convert them into a common currency (see the data annex for further details). The choice of the marginal approach instead of a full VAR including other relevant foreign variables (foreign output, interest rates and prices) was dictated by the relatively small size of the sample used (84 observations).13 . By using money per output, we assume that only the part of global liquidity not linked to foreign output can potentially generate spillover e¤ects on the euro area. We order the extra variable GL4Y in the models of the previous section as the most exogenous variable in the system. Under this assumption, we are implicitly assuming that developments in the euro area do not have a contemporaneous e¤ect on global monetary developments but only a delayed one.14 13 See Sousa and Zaghini (2007) for a SVAR model based on aggregate G5 data including the euro area. 14 The choice of including global liquidity in the euro area benchmark VARs as a fully exogenous variable could also be considered. However, the results of exclusion tests in the extended six-variable VAR show that it is possible to reject the null that global liquidity is exogenous to euro area variables. In addition, it is also possible to reject the hypothesis that the euro area block is exogenous to global liquidity. Therefore, we have opted to keep global liquidity endogenous. On the other hand, we have added a total commodities cost variable as an exogenous variable, to take account of movements in global commodities prices. The inclusion of this variable therefore controls for a further source of external shocks that may distort the link between global liquidity and in‡ation and output in the euro area. 13 When Model 1 is used, the identi…cation scheme (Model 1a) 2 3 2 GL4Y 3 2 GL4Y 1 0 0 0 0 0 ut et 6 a21 1 7 6 uYt R 7 6 eYt R 0 0 0 0 6 7 6 PI 7 6 PI 6 a31 a32 1 6 t 7 6 et 0 0 0 7 6 7 6 uM 7 6 3 6 a41 a42 a43 1 a45 0 7 6 ut 3 7 = 6 eM 6 7 6 SR 7 6 tSR 4 a51 0 5 4 et 0 a54 1 a56 5 4 ut ER a61 a62 a63 a64 a65 1 ut eER t becomes: 3 7 7 7 7: 7 7 5 (4) The plot of the impulse responses is shown in Figure 5. A positive shock to global liquidity results in a permanent rise in the levels of both euro area M3 and prices. As regards the e¤ect on real GDP, there is a temporary upward e¤ect of a positive shock to global liquidity on the output level of the euro area, with GDP returning to baseline after a period of about …ve years. Therefore, shocks to global liquidity seem to have only nominal e¤ects in the long-run. Also for the identi…cations based on the Cholesky decomposition, we introduce GL4Y as the most exogenous variable in the system, following the chain GL4Y ! Y R ! P I ! M 3 ! SR ! ER (Model 2a). The impulse response functions are shown in Figure 6. The dynamics are indeed similar to those from Model 1a: a positive shock to global liquidity leads to a signi…cant rise in euro area M3 and to an upward e¤ect on prices, suggesting that there is a transmission from global monetary developments to the euro area over time. In particular, these developments point to a positive spillover e¤ect into the euro area as predicted by the “push”channel. An unexpected increase in money abroad gives rise to capital ‡ows into the euro area in the mid-term determining an upward pressure on M3, which in turn leads to an increasing price pressure. As regards output, an exogenous increase in global liquidity leads to a signi…cant upward e¤ect on euro area output after two quarters. The e¤ect peaks at around two years and then declines becoming insigni…cant in the longer-run. The short-term interest rate does not appear to react much in the short-run but it rises signi…cantly after a period of about one year. One possible interpretation is that the upward movement of the interest rate re‡ects a monetary policy reaction to the increase in the price index associated to the positive spillover of global liquidity. Finally, a positive shock to global liquidity leads to a temporary upward e¤ect on the euro exchange rate.15 15 As a robustness check we looked at the impulse responses when global liquidity is 14 Figure 5: Impulse responses from Model 1a (including 90% con…dence bands) 15 Figure 6: Impulse responses from Model 2a (including 90% con…dence bands) 16 Overall, these …ndings are consistent with the existence of a push channel, through which high monetary growth abroad determines an increase in the demand for assets in domestic markets and leads to stronger M3 growth and higher returns. Next we analyze the forecast error variance decomposition of M3, prices and real GDP. Starting with Model 1a the variance decomposition for M3 suggests that, besides shocks to M3 itself, shocks to the short-term interest rate are the most important source of ‡uctuations in the monetary aggregate over the 1-year horizon. However, their importance declines over time (see the Table 2, upper panel). By contrast, global liquidity has a small contribution to the variability of M3 in the short-run but it gradually increases over time becoming the most important variable in the longer-run, after shocks to M3 itself. As regards Model 2a, the results are somewhat di¤erent as in this case innovations to the short-term interest rate do not play an important role in explaining M3 ‡uctuations. Instead, global liquidity plays a strong role also in the short-run, being the most important variable in explaining the variability of M3 at any horizon, again excluding M3 itself. The middle panel of Table 2 shows the forecast error decomposition for prices. M3 plays an important role in explaining the forecast error variance in both models, particularly in Model 1a. Again, the main di¤erence between the two models concerns the importance of shocks to the short-term interest rate. In fact, in Model 1a shocks to short-term rates are important in explaining the variability of the price level, even in the longer-run. In addition, their contribution is always larger than that of shocks to global liquidity. By contrast, in Model 2a global liquidity appears to be the most important contributor to the variability of price level in the long-run, with a share of 36.6% at a horizon of four years. introduced in the model as the most endogenous variable. The shape and the size of the responses do not change signi…cantly, both in the non-recursive and in the Cholesky identi…cation scheme. 17 Table 2. Forecast error variance decomposition 1 year Model 1a 2 year 3 year 4 year 1 year Model 2a 2 year 3 year 4 year Variability of M3 GL4Y YR PI M3 SR ER 2.4 0.6 4.9 79.5 10.4 2.1 15.4 1.3 3.1 67.1 9.8 3.4 26.8 11.0 2.4 49.7 6.3 3.9 33.7 16.6 1.9 36.2 4.4 7.3 14.4 1.0 4.8 79.1 0.5 0.2 44.6 1.4 5.4 47.3 1.0 0.3 58.8 5.9 4.8 26.8 2.8 0.8 64.8 6.5 3.8 17.2 5.6 2.1 8.2 1.2 61.4 17.2 0.8 11.2 20.0 1.2 48.9 19.7 0.5 9.7 36.6 2.6 35.1 16.8 1.0 7.8 40.6 36.8 2.7 1.1 17.2 1.6 55.7 26.8 2.1 1.1 13.2 1.1 57.9 24.3 1.9 1.2 13.0 1.8 Variability of prices GL4Y YR PI M3 SR ER 6.0 0.2 52.6 14.3 18.3 8.5 4.6 0.2 32.8 31.6 24.1 6.8 4.3 2.4 23.6 40.5 23.1 6.1 12.4 8.4 17.0 38.7 16.6 6.9 4.1 1.6 73.4 8.6 0.7 11.5 Variability of real GDP GL4Y YR PI M3 SR ER 5.7 74.0 7.6 0.6 4.8 7.2 19.2 54.6 5.1 3.8 8.2 9.1 38.4 41.0 3.9 3.4 6.5 6.7 40.8 37.8 3.4 3.2 6.2 8.6 18.1 65.2 4.4 0.4 9.9 2.0 Finally, the decomposition of the forecast error variance for output is shown in the lower panel of Table 2. As in the case of the euro area models, in Model 1a the contribution of the short-term rate to the variability of real output is relatively limited. In Model 2a shocks to the short-term rate explain a share of 17.2% of GDP variability after two years and remain well above 10% thereafter. As for international money, both models suggest that while in the short-run shocks in global liquidity play a small role in in‡uencing 18 output ‡uctuations in the euro area, the importance increases over time with global liquidity becoming relevant in the long-run.16 Overall, the analysis suggests that the impulse responses to shocks to global liquidity are quite robust to the type of speci…cation that is chosen. The results highlights that a positive shock to global liquidity leads to a rise in euro area M3 and in the price level in the euro area. The e¤ect on euro area real GDP is found to be positive and temporary, with a return to baseline four years after a shock to global liquidity. As regards the forecast error variance decompositions, the results suggest that global liquidity plays an important role in explaining ‡uctuations in M3, prices and output in the euro area. However, as regards prices, the evidence is not conclusive on the relative importance of global liquidity and interest rates. In particular, while in Model 1a global liquidity plays a limited role, it is quite important in Model 2a. 4 Conclusion The paper relied on the SVAR approach to construct two benchmark models of the euro area that seem to properly identify exogenous monetary policy shocks. The behaviour of GDP, prices, money and the exchange rate derived from the impulse response functions is consistent with the transmission of a monetary policy impulse. By introducing in the models a further endogenous variable, we could check the e¤ects of a global liquidity aggregate on euro area macroeconomic developments. Our results show that a positive shock to extra-euro area global liquidity leads to a permanent rise in M3 and the price level and determines temporary increases in euro area output and a temporary appreciation of the real e¤ective exchange rate of the euro. The relevance of the inclusion of foreign variables in the empirical models analysed here relates to the broad economic integration across-countries already achieved and to the speed at which capital markets are currently 16 The result that global liquidity per output is the main cause of the euro area output volatility in the longer-run is somewhat above what would be expected. One possible explanation for this …nding is that shocks to global liquidity per output may capture also shocks to global demand. However, when other international variables are introduced in the benchmark models (global GDP and global interest rate outside the euro area), the contributions to the variance are always rather limited, thus suggesting that global liquidity is indeed an important source of variability for some euro area macroeconomic variables. 19 able to move funds worldwide. The literature on international business cycle shows that the cross-country transmission of shocks is an important element in explaining domestic output ‡uctuations. This paper suggests that a similar channel is at work when dealing with monetary aggregates. In fact, our contribution to the current empirical literature hinges also on the …nding that shocks to global liquidity play an important role in explaining price and output ‡uctuations in the euro area. The size of the impact of the development in the extra-euro area monetary aggregate is to some extent sensitive to the speci…cation implemented. When a recursive scheme is used, both M3 and the foreign liquidity have important explanatory power for the variability of euro area prices. In addition, in the longer-run shocks to global liquidity seem to have a higher importance for the variability of prices than shocks to M3 itself. On the other hand, when a non recursive scheme is at work, global liquidity plays a somewhat smaller role in the short-run in explaining price ‡uctuations. Nevertheless, also in this model the global monetary aggregate still contributes signi…cantly to price variability at longer horizons. As for GDP ‡uctuations, the contribution of global liquidity shocks is increasing over time and soon becomes the most important source of GDP variability. In particular, when the recursive approach is implemented, the portion of output variability explained by foreign money shocks is very large. However, comparing our results with those of Canova and De Nicoló (2002) we can note that the share of the output ‡uctuation after two years attributable to a global liquidity shock (20-40%) is even smaller than what they report for some European G7 countries due to a “standard”monetary policy innovation. Concluding, the main contribution of this work is that the evolution of foreign variables and in particular of monetary aggregates is relevant for the economic policy management of a country. The evidence reported suggest a possible channel of transmission of global liquidity shocks: robust monetary growth abroad may lead to capital ‡ows into the domestic economy due to the search of di¤erent sources of investment, thus resulting in stronger monetary growth and higher asset returns in the recipient country. 20 A Data annex The table below provides an overview of the series used in this study. All series are seasonally adjusted except interest rates and the real e¤ective exchange rate of the euro. Variable Broad monetary aggregates Real GDP, GDP de‡ator and CPI Short-term interest rates Total commodity prices Real (CPI-based) e¤ective exchange rate of the euro De…nition Sources Euro area: M3 US: M2 ECB. US Federal Reserve Board (press release H6). Bank of Japan. Japan: M2 plus certi…cates of deposit UK: M4 Canada: M2+ Euro area (HICP) US Japan Canada UK Euro area: three-month interbank rate (until 29 December 1998); three-month EURIBOR (thereafter) US Japan Canada UK Commodity Price Index Aggregation of the bilateral exchange rate of the euro against 12 partner countries. Bank of England. Bank of Canada. Eurostat. OECD Main Econ. OECD Main Econ. OECD Main Econ. OECD Main Econ. ECB. OECD Main OECD Main OECD Main OECD Main HWWA. Econ. Econ. Econ. Econ. Indicators. Indicators. Indicators. Indicators. Indicators. Indicators. Indicators. Indicators. ECB. The criterion used for the selection of the broad aggregates for each country was that are the key broad monetary aggregates in the di¤erent countries from a monetary policy point of view. The global monetary aggregate (GL4) 21 is constructed by converting each national aggregate into euros using PPP exchange rates. The formula used is the following: GL4 = 4 X i;eur Mi Eppp i=1 i;eur where Mi represents each national monetary aggregate and Eppp is the corresponding country’s PPP exchange rate vis-à-vis the euro. The PPP exchange rate is based on relative PPP. It is computed by taking the nominal exchange i;eur ) as the rate of January 1999 of the several countries against the euro (E1999 basis and using the consumer price indices of the several countries to construct the PPP exchange rate for the other periods. More precisely, the PPP i;eur exchange rate for country i (Eppp ) at time t is constructed as: i;eur i;eur Eppp;t = E1999 eur Pti P1999 i Pteur P1999 eur i where P1999 , P1999 are the consumer price indices in the euro area and in country i in January 1999, respectively, and Pteur , Pti are the corresponding consumer price indices at time t. Thus, this procedure does not guarantee that absolute PPP holds. However, for the purpose of this study, the level of the exchange rate used to construct the global liquidity is relatively not important as only the changes over time of the global liquidity aggregate will matter in the estimation of the model. One possible limitation in the construction of the global liquidity aggregate as done above, is that the resulting aggregate will be rather sensitive to the de…nition of the monetary aggregate used to construct it. As there are problems of comparability between the aggregates used for the di¤erent countries, given the di¤erent de…nitions of monetary aggregates, the weights may not re‡ect appropriately the di¤erences in the importance of each country. This is particularly the case for Japan and the US, with the former country having over same periods a larger share in the global liquidity aggregate than the latter. Therefore, we have also constructed a di¤erent measure of global liquidity using GDP weights. The formula is the following: GL4 = 4 X M Indexi i=1 22 GDPi i;eur E eur ppp GDPall Figure 7: Global liquidity computed with di¤erent weights where GDPi represents nominal GDP of country i expressed in national eur currency and GDPall is the aggregate GDP of the whole set of countries obtained as the sum of each country’s GDP converted into euros with PPP exchange rates. M Indexi is the index of the monetary aggregate in country i. For each country this index equals 100 in January 1999 and grows at the same rate as the monetary aggregates denominated in national currency used for each country. Figure 7 shows the di¤erence between the two series. As can be seen in the chart, most of the time they are quite limited. In the case of the other variables used, namely the short-term interest rate, real GDP and the GDP de‡ator, the computation of global aggregates was done by relying on GDP weights obtained using PPP exchange rates to convert each national nominal GDP into euro. 23 References [1] Amato, J.D. and Swanson, N.R. (2001), “The real-time predictive content of money for output”, Journal of Monetary Economics, Vol. 48, pp. 3-24. [2] Amisano, G. and C. Giannini (1997), Topics in Structural VAR Econometrics, Springer, Heidelberg and New York. [3] Baks K. and C. Kramer (1999) “Global Liquidity and Asset Prices: Measurement, Implications and Spillovers”, IMF Working Paper No.168. [4] Calza A., D. Gerdesmeier and J. Levy (2001), “Euro area money demand: Measuring the opportunity costs appropriately”, IMF Working Paper No.179. [5] Canova, F. (2005). “The Transmission of US Shocks to Latin America”. Journal of Applied Econometrics, Vol.20, pp.229-251. [6] Canova, F. and G. De Nicoló (2002), “Monetary Disturbances Matter for Business Fluctuations in the G-7”, Journal of Monetary Economics, Vol.49, pp.1131-1159. [7] Christiano, L., M. Eichenbaum and C. Evans (1999), “Monetary Policy Shocks: What Have we Learned and to What End?”, in J. Taylor and M. Woodford (eds.), Handbook of Macroeconomics, North Holland. [8] Coenen, G. and J.L. Vega (2001), “The Demand for M3 in the Euro Area”, Journal of Applied Econometrics, Vol.16, pp.727–748 [9] Dedola, L. and F. Lippi (2005), “The monetary transmission mechanism: evidence from the industries of …ve OECD countries”, European Economic Review, Vol 49, pp.1543-1570. [10] Dotsey, M. and Hornstein, A. (2003), “Should a Monetary Policy Maker Look at Money?”, Journal of Monetary Economics, Vol. 50, pp. 547-579. [11] Ehrmann, M. and M Fratzscher (2006), “Global Financial Transmission of Monetary Policy Shocks”, ECB Working Paper No.616. 24 [12] Gordon, D.B. and E.M. Leeper (1994), “The Dynamic Impacts of Monetary Policy: an Exercise in Tentative Identi…cation”, Journal of Political Economy, Vol.102, pp.1228-1247. [13] Holman, J. A. and Neumann, R. M. (2002), “Evidence on the crosscountry transmission of monetary shocks”, Applied Economics, Vol. 34, No. 15, pp. 1837-1857. [14] Kim, S. (1999), “Do Monetary Policy Shocks Matter in the G-7 Countries? Using Common Identifying Assumptions about Monetary Policy Across Countries”, Journal of International Economics, Vol. 48, pp.387412. [15] Kim, S. (2001), “International Transmission of US Monetary Policy Shocks: Evidence from VARs”, Journal of Monetary Economics, Vol. 48, pp.339-372. [16] Kim, S. and N. Roubini (2000), “Exchange Rate Anomalies in the Industrial Countries: a Solution with a Structural VAR Approach”, Journal of Monetary Economics, Vol.45, pp.561-586. [17] Kwark, N. (1999), “Sources of International Business Fluctuations: Country-speci…c Shocks or Worldwide Shocks?”, Journal of International Economics, Vol.48, pp.367-385. [18] Leeper, E.M. and J.E. Roush (2003), “Putting ’M’ back in Monetary Policy”, Journal of Money, Credit, and Banking, Vol.35, No.6, pp.12171256. [19] Lumsdaine, R.L. and E.S Prasad (2003), “Identifying the Common Component of International Economic Fluctuations: a New Approach”, Economic Journal, Vol.113, pp.101-127. [20] McKinnon, R.I. (1982), “Currency Substittion and Instability in the World Dollar Standard”, American Economic Review, Vol.72, No.3, pp.320-333. [21] Mojon, B. and G. Peersman (2003), “A VAR Description of the E¤ects of Monetary Policy in the Individual Countries of the Euro Area”’ in Angeloni, I., A Kashyap and B Mojon (eds), Monetary Policy Transmission in the Euro Area, Cambridge University Press. 25 [22] Monticelli, C. and O. Tristani (1999), “What does the Single Monetary Policy Do? A SVAR Benchmark for the European Central Bank”, ECB Working Paper No. 2. [23] Peersman, G. and F. Smets (2003), “The Monetary Transmission Mechanism in the Euro Area: More Evidence from VAR Analysis”, in Angeloni, I., A Kashyap and B Mojon (eds), Monetary Policy Transmission in the Euro Area, Cambridge University Press. [24] Sims, C.A. (1990), “Inference for Multivariate Time Series Models with Trend”, Econometrica, Vol.58, No.1, pp.113-144. [25] Sims, C.A. and T.A. Zha (1998), ”Does Monetary Policy Generate Recessions?”, Federal Reserve Bank of Atlanta, Working Paper No.12. [26] Sousa J. and A. Zaghini (2007), “Global Monetary Policy Shocks in the G5: a SVAR Approach”, Journal of International Financial Markets, Institutions and Money, Vol.17, No.4. [27] Trecroci, C. and Vega, J. L. (2000), “The information content of M3 for future in‡ation”, Weltwirtschaftliches Archiv, Vol.138, No.1, pp 22-53. 26 RECENTLY PUBLISHED “TEMI” (*) N. 605 – Job search in thick markets: Evidence from Italy, by Sabrina Di Addario (December 2006). N. 606 – The transmission of monetary policy shocks from the US to the euro area, by S. Neri and A. Nobili (December 2006). N. 607 – What does a technology shock do? A VAR analysis with model-based sign restrictions, by L. Dedola and S. Neri (December 2006). N. 608 – Merge and compete: Strategic incentives for vertical integration, by Filippo Vergara Caffarelli (December 2006). N. 609 – Real-time determinants of fiscal policies in the euro area: Fiscal rules, cyclical conditions and elections, by Roberto Golinelli and Sandro Momigliano (December 2006). N. 610 – L’under-reporting della ricchezza finanziaria nell’indagine sui bilanci delle famiglie, by Leandro D’Aurizio, Ivan Faiella, Stefano Iezzi, Andrea Neri (December 2006). N. 611 – La polarizzazione territoriale del prodotto pro capite: un’analisi del caso italiano sulla base di dati provinciali by Stefano Iezzi (December 2006). N. 612 – A neural network architecture for data editing in the Bank of Italy’s business surveys by Claudia Biancotti, Leandro D’Aurizio and Raffaele Tartaglia-Polcini (February 2007). N. 613 – Outward FDI and Local Employment Growth in Italy, by Stefano Federico and Gaetano Alfredo Minerva (February 2007). N. 614 – Testing for trend, by Fabio Busetti and Andrew Harvey (February 2007). N. 615 – Macroeconomic uncertainty and banks’ lending decisions: The case of Italy, by Mario Quagliariello (February 2007). N. 616 – Entry barriers in italian retail trade, by Fabiano Schivardi and Eliana Viviano (February 2007). N. 617 – A politicy-sensible core-inflation measure for the euro area, by Stefano Siviero and Giovanni Veronese (February 2007). N. 618 – Le opinioni degli italiani sull’evasione fiscale, by Luigi Cannari and Giovanni D'Alessio (February 2007) N. 619 – Memory for prices and the euro cash changeover: An analysis for cinema prices in Italy, by Vincenzo Cestari, Paolo Del Giovane and Clelia Rossi-Arnaud (February 2007). N. 620 – Intertemporal consumption choices, transaction costs and limited participation in financial markets: Reconciling data and theory, by Orazio P. Attanasio and Monica Paiella (April 2007). N. 621 – Why demand uncertainty curbs investment: Evidence from a panel of Italian manufacturing firms, by Maria Elena Bontempi, Roberto Golinelli and Giuseppe Parigi (April 2007). N. 622 – Employment, innovation and productivity: Evidence from Italian microdata, by Bronwyn H. Hall, Francesca Lotti and Jacques Mairesse (April 2007). N. 623 – Measurement of Income Distribution in Supranational Entities: The Case of the European Union, by Andrea Brandolini (April 2007). N. 624 – Un nuovo metodo per misurare la dotazione territoriale di infrastrutture di trasporto, by Giovanna Messina (April 2007). N. 625 – The forgone gains of incomplete portfolios, by Monica Paiella (April 2007). N. 626 – University drop-out: The case of Italy, by Federico Cingano and Piero Cipollone (April 2007). N. 627 – The sectoral distribution of money supply in the euro area, by Giuseppe Ferrero, Andrea Nobili and Patrizia Passiglia (April 2007). N. 628 – Changes in transport and non-transport costs: Local vs global impacts in a spatial network, by Kristian Behrens, Andrea R. Lamorgese, Gianmarco I.P. Ottaviano and Takatoshi Tabuchi (April 2007). (*) Requests for copies should be sent to: Banca d’Italia – Servizio Studi – Divisione Biblioteca e pubblicazioni – Via Nazionale, 91 – 00184 Rome (fax 0039 06 47922059). They are available on the Internet www.bancaditalia.it. "TEMI" LATER PUBLISHED ELSEWHERE 2004 P. ANGELINI and N. CETORELLI, Gli effetti delle modifiche normative sulla concorrenza nel mercato creditizio, in F. Panetta (eds.), Il sistema bancario negli anni novanta: gli effetti di una trasformazione, Bologna, il Mulino, TD No. 380 (October 2000). P. CHIADES and L. GAMBACORTA, The Bernanke and Blinder model in an open economy: The Italian case, German Economic Review, Vol. 5 (1), pp. 1-34, TD No. 388 (December 2000). M. BUGAMELLI and P. PAGANO, Barriers to investment in ICT, Applied Economics, Vol. 36 (20), pp. 2275-2286, TD No. 420 (October 2001). F. BUSETTI, Preliminary data and econometric forecasting: An application with the Bank of Italy quarterly model, CEPR Discussion Paper, 4382, TD No. 437 (December 2001). A. BAFFIGI, R. GOLINELLI and G. PARIGI, Bridge models to forecast the euro area GDP, International Journal of Forecasting, Vol. 20 (3), pp. 447-460,TD No. 456 (December 2002). D. AMEL, C. BARNES, F. PANETTA and C. SALLEO, Consolidation and efficiency in the financial sector: A review of the international evidence, Journal of Banking and Finance, Vol. 28 (10), pp. 24932519, TD No. 464 (December 2002). M. PAIELLA, Heterogeneity in financial market participation: Appraising its implications for the C-CAPM, Review of Finance, Vol. 8, 3, pp. 445-480, TD No. 473 (June 2003). F. CINGANO and F. SCHIVARDI, Identifying the sources of local productivity growth, Journal of the European Economic Association, Vol. 2 (4), pp. 720-742, TD No. 474 (June 2003). E. BARUCCI, C. IMPENNA and R. RENÒ, Monetary integration, markets and regulation, Research in Banking and Finance, (4), pp. 319-360, TD No. 475 (June 2003). G. ARDIZZI, Cost efficiency in the retail payment networks: first evidence from the Italian credit card system, Rivista di Politica Economica, Vol. 94, (3), pp. 51-82, TD No. 480 (June 2003). E. BONACCORSI DI PATTI and G. DELL’ARICCIA, Bank competition and firm creation, Journal of Money Credit and Banking, Vol. 36 (2), pp. 225-251, TD No. 481 (June 2003). R. GOLINELLI and G. PARIGI, Consumer sentiment and economic activity: a cross country comparison, Journal of Business Cycle Measurement and Analysis, Vol. 1 (2), pp. 147-170, TD No. 484 (September 2003). L. GAMBACORTA and P. E. MISTRULLI, Does bank capital affect lending behavior?, Journal of Financial Intermediation, Vol. 13 (4), pp. 436-457, TD No. 486 (September 2003). F. SPADAFORA, Il pilastro privato del sistema previdenziale: il caso del Regno Unito, Economia Pubblica, 34, (5), pp. 75-114, TD No. 503 (June 2004). F. LIPPI. and S. NERI, Information variables for monetary policy in a small structural model of the euro area, Journal of Monetary Economics, v. 54, 4, pp. 1256-1270, TD No. 511 (July 2004). C. BENTIVOGLI and F. QUINTILIANI, Tecnologia e dinamica dei vantaggi comparati: un confronto fra quattro regioni italiane, in C. Conigliani (eds.), Tra sviluppo e stagnazione: l’economia dell’Emilia-Romagna, Bologna, Il Mulino, TD No. 522 (October 2004). G. GOBBI and F. LOTTI, Entry decisions and adverse selection: An empirical analysis of local credit markets, Journal of Financial services Research, Vol. 26 (3), pp. 225-244, TD No. 535 (December 2004). E. GAIOTTI and F. LIPPI, Pricing behavior and the introduction of the euro: Evidence from a panel of restaurants, Giornale degli Economisti e Annali di Economia, 2004, Vol. 63, (3/4), pp. 491-526, TD No. 541 (February 2005). A. CICCONE, F. CINGANO and P. CIPOLLONE, The private and social return to schooling in Italy, Giornale degli economisti e annali di economia, v. 63, 3-4, pp. 413-444, TD No. 569 (January 2006). 2005 L. DEDOLA and F. LIPPI, The monetary transmission mechanism: Evidence from the industries of 5 OECD countries, European Economic Review, 2005, Vol. 49, (6), pp. 1543-1569, TD No. 389 (December 2000). D. Jr. MARCHETTI and F. NUCCI, Price stickiness and the contractionary effects of technology shocks. European Economic Review, v. 49, pp. 1137-1164, TD No. 392 (February 2001). G. CORSETTI, M. PERICOLI and M. SBRACIA, Some contagion, some interdependence: More pitfalls in tests of financial contagion, Journal of International Money and Finance, v. 24, 8, pp. 1177-1199, TD No. 408 (June 2001). GUISO L., L. PISTAFERRI and F. SCHIVARDI, Insurance within the firm. Journal of Political Economy, 113, pp. 1054-1087, TD No. 414 (August 2001) R. CRISTADORO, M. FORNI, L. REICHLIN and G. VERONESE, A core inflation indicator for the euro area, Journal of Money, Credit, and Banking, v. 37, 3, pp. 539-560, TD No. 435 (December 2001). F. ALTISSIMO, E. GAIOTTI and A. LOCARNO, Is money informative? Evidence from a large model used for policy analysis, Economic & Financial Modelling, v. 22, 2, pp. 285-304, TD No. 445 (July 2002). G. DE BLASIO and S. DI ADDARIO, Do workers benefit from industrial agglomeration? Journal of regional Science, Vol. 45, (4), pp. 797-827, TD No. 453 (October 2002). R. TORRINI, Cross-country differences in self-employment rates: The role of institutions, Labour Economics, V. 12, 5, pp. 661-683, TD No. 459 (December 2002). A. CUKIERMAN and F. LIPPI, Endogenous monetary policy with unobserved potential output, Journal of Economic Dynamics and Control, v. 29, 11, pp. 1951-1983, TD No. 493 (June 2004). M. OMICCIOLI, Il credito commerciale: problemi e teorie, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 494 (June 2004). L. CANNARI, S. CHIRI and M. OMICCIOLI, Condizioni di pagamento e differenziazione della clientela, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 495 (June 2004). P. FINALDI RUSSO and L. LEVA, Il debito commerciale in Italia: quanto contano le motivazioni finanziarie?, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 496 (June 2004). A. CARMIGNANI, Funzionamento della giustizia civile e struttura finanziaria delle imprese: il ruolo del credito commerciale, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 497 (June 2004). G. DE BLASIO, Credito commerciale e politica monetaria: una verifica basata sull’investimento in scorte, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 498 (June 2004). G. DE BLASIO, Does trade credit substitute bank credit? Evidence from firm-level data. Economic notes, Vol. 34 (1), pp. 85-112, TD No. 498 (June 2004). A. DI CESARE, Estimating expectations of shocks using option prices, The ICFAI Journal of Derivatives Markets, Vol. 2, (1), pp. 42-53, TD No. 506 (July 2004). M. BENVENUTI and M. GALLO, Il ricorso al "factoring" da parte delle imprese italiane, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 518 (October 2004). L. CASOLARO and L. GAMBACORTA, Redditività bancaria e ciclo economico, Bancaria, v. 61, 3, pp. 19-27, TD No. 519 (October 2004). F. PANETTA, F. SCHIVARDI and M. SHUM, Do mergers improve information? Evidence from the loan market, CEPR Discussion Paper, 4961, TD No. 521 (October 2004). P. DEL GIOVANE and R. SABBATINI, La divergenza tra inflazione rilevata e percepita in Italia, Bologna, Il Mulino, TD No. 532 (December 2004). R. TORRINI, Quota dei profitti e redditività del capitale in Italia: un tentativo di interpretazione, Politica economica, v. 21, pp. 7-42, TD No. 551 (June 2005). M. OMICCIOLI, Il credito commerciale come “collateral”, in L. Cannari, S. Chiri, M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, il Mulino, TD No. 553 (June 2005). L. CASOLARO, L. GAMBACORTA and L. GUISO, Regulation, formal and informal enforcement and the development of the household loan market. Lessons from Italy, in Bertola G., Grant C. and Disney R. (eds.) The Economics of Consumer Credit: European Experience and Lessons from the US, Boston, MIT Press, TD No. 560 (September 2005). P. ANGELINI and F. LIPPI, Did inflation really soar after the euro changeover? Indirect evidence from ATM withdrawals, CEPR Discussion Paper, 4950, TD No. 581 (March 2006). S. DI ADDARIO, Job search in thick markets: Evidence from Italy, Oxford Discussion Paper 235, Department of Economics Series, TD No. 605 (December 2006). 2006 F. BUSETTI, Tests of seasonal integration and cointegration in multivariate unobserved component models, Journal of Applied Econometrics, v. 21, 4, pp. 419-438, TD No. 476 (June 2003). C. BIANCOTTI, A polarization of inequality? The distribution of national Gini coefficients 1970-1996, Journal of Economic Inequality, v. 4, 1, pp. 1-32, TD No. 487 (March 2004). L. CANNARI and S. CHIRI, La bilancia dei pagamenti di parte corrente Nord-Sud (1998-2000), in L. Cannari, F. Panetta (a cura di), Il sistema finanziario e il Mezzogiorno: squilibri strutturali e divari finanziari, Bari, Cacucci, TD No. 490 (March 2004). M. BOFONDI and G. GOBBI, Information barriers to entry into credit markets, Review of Finance, Vol. 10 (1), pp. 39-67, TD No. 509 (July 2004). LIPPI F. and W. FUCHS, Monetary union with voluntary participation, Review of Economic Studies, 73, pp. 437-457 TD No. 512 (July 2004). GAIOTTI E. and A. SECCHI, Is there a cost channel of monetary transmission? An investigation into the pricing behaviour of 2000 firms, Journal of Money, Credit, and Banking, v. 38, 8, pp. 2013-2038 TD No. 525 (December 2004). A. BRANDOLINI, P. CIPOLLONE and E. VIVIANO, Does the ILO definition capture all unemployment?, Journal of the European Economic Association, v. 4, 1, pp. 153-179, TD No. 529 (December 2004). A. BRANDOLINI, L. CANNARI, G. D’ALESSIO and I. FAIELLA, Household wealth distribution in Italy in the 1990s, In E. N. Wolff (ed.) International Perspectives on Household Wealth, Cheltenham, Edward Elgar, TD No. 530 (December 2004). P. DEL GIOVANE and R. SABBATINI, Perceived and measured inflation after the launch of the Euro: Explaining the gap in Italy, Giornale degli economisti e annali di economia, v. 65, 2 , pp. 155-192, TD No. 532 (December 2004). M. CARUSO, Monetary policy impulses, local output and the transmission mechanism, Giornale degli economisti e annali di economia, v. 65, 1, pp. 1-30, TD No. 537 (December 2004). L. GUISO and M. PAIELLA, The role of risk aversion in predicting individual behavior, In P. A. Chiappori e C. Gollier (eds.) Competitive Failures in Insurance Markets: Theory and Policy Implications, Monaco, CESifo, TD No. 546 (February 2005). G. M. TOMAT, Prices product differentiation and quality measurement: A comparison between hedonic and matched model methods, Research in Economics, No. 60, pp. 54-68, TD No. 547 (February 2005). F. LOTTI, E. SANTARELLI and M. VIVARELLI, Gibrat's law in a medium-technology industry: Empirical evidence for Italy, in E. Santarelli (ed.), Entrepreneurship, Growth, and Innovation: the Dynamics of Firms and Industries, New York, Springer, TD No. 555 (June 2005). F. BUSETTI, S. FABIANI and A. HARVEY, Convergence of prices and rates of inflation, Oxford Bulletin of Economics and Statistics, v. 68, 1, pp. 863-878, TD No. 575 (February 2006). M. CARUSO, Stock market fluctuations and money demand in Italy, 1913 - 2003, Economic Notes, v. 35, 1, pp. 1-47, TD No. 576 (February 2006). S. IRANZO, F. SCHIVARDI and E. TOSETTI, Skill dispersion and productivity: An analysis with matched data, CEPR Discussion Paper, 5539, TD No. 577 (February 2006). M. BUGAMELLI and A. ROSOLIA, Produttività e concorrenza estera, Rivista di politica economica, 3, TD No. 578 (February 2006). R. BRONZINI and G. DE BLASIO, Evaluating the impact of investment incentives: The case of Italy’s Law 488/92. Journal of Urban Economics, vol. 60, n. 2, pag. 327-349, TD No. 582 (March 2006). A. DI CESARE, Do market-based indicators anticipate rating agencies? Evidence for international banks, Economic Notes, v. 35, pp. 121-150, TD No. 593 (May 2006). L. DEDOLA and S. NERI, What does a technology shock do? A VAR analysis with model-based sign restrictions, Journal of Monetary Economics, v. 54, 2, pp. 512 - 549, TD No. 607 (December 2006). R. GOLINELLI and S. MOMIGLIANO, Real-time determinants of fiscal policies in the euro area, Journal of Policy Modeling, v. 28, 9, pp. 943-64, TD No. 609 (December 2006). P. ANGELINI, S. GERLACH, G. GRANDE, A. LEVY, F. PANETTA, R. PERLI,S. RAMASWAMY, M. SCATIGNA and P. YESIN, The recent behaviour of financial market volatility, BIS Papers, 29, QEF No. 2 (August 2006). 2007 S. DI ADDARIO and E. PATACCHINI, Wages and the city. Evidence from Italy, Development Studies Working Papers 231, Centro Studi Luca d’Agliano, TD No. 570 (January 2006). F. LOTTI and J. MARCUCCI, Revisiting the empirical evidence on firms' money demand, Journal of Economics and Business, v. 59, 1, pp. 51-73, TD No. 595 (May 2006). L. MONTEFORTE, Aggregation bias in macro models: Does it matter for the euro area?, Economic Modelling, 24, pp. 236-261, TD No. 534 (December 2004). FORTHCOMING P. ANGELINI, Liquidity and announcement effects in the euro area, Giornale degli economisti e annali di economia, TD No. 451 (October 2002). S. MAGRI, Italian households' debt: The participation to the debt market and the size of the loan, Empirical Economics, TD No. 454 (October 2002). G. FERRERO, Monetary policy, learning and the speed of convergence, Journal of Economic Dynamics and Control, TD No. 499 (June 2004). M. PAIELLA, Does wealth affect consumption? Evidence for Italy, Journal of Macroeconomics, v. 29, 1, TD No. 510 (July 2004). A. ANZUINI and A. LEVY, Monetary policy shocks in the new EU members: A VAR approach, Applied Economics TD No. 514 (July 2004). S. MOMIGLIANO, J. Henry and P. Hernández de Cos, The impact of government budget on prices: Evidence from macroeconometric models, Journal of Policy Modelling, TD No. 523 (October 2004). D. Jr. MARCHETTI and F. Nucci, Pricing behavior and the response of hours to productivity shocks, Journal of Money Credit and Banking, TD No. 524 (December 2004). R. BRONZINI, FDI Inflows, Agglomeration and host country firms’ size: Evidence from Italy, Regional Studies, TD No. 526 (December 2004). A. NOBILI, Assessing the predictive power of financial spreads in the euro area: does parameters instability matter?, Empirical Economics, v. 31, 4, pp. , TD No. 544 (February 2005). P. ANGELINI and A. Generale, On the evolution of firm size distributions, American Economic Review, TD No. 549 (June 2005). A. DALMAZZO and G. DE BLASIO, Production and consumption externalities of human capital: An empirical study for Italy, Journal of Population Economics, TD No. 554 (June 2005). R. FELICI and M. PAGNINI,, Distance, bank heterogeneity and entry in local banking markets, The Journal of Industrial Economics, TD No. 557 (June 2005). R. BRONZINI and G. DE BLASIO, Una valutazione degli incentivi pubblici agli investimenti, Rivista Italiana degli Economisti , TD No. 582 (March 2006). P. CIPOLLONE and A. ROSOLIA, Social interactions in high school: Lessons from an earthquake, American Economic Review, TD No. 596 (September 2006). F. BUSETTI and A. HARVEY, Testing for trend, Econometric Theory TD No. 614 (February 2007).