Commonalities and cross-country spillovers in macroeconomic-financial linkages

2.1 The data

The empirical model is estimated for the G7 economies and for some non-G7 European countries, using core variables of the real business cycle as well as a set of financial variables.

The sample period is the first quarter of 1980 to the fourth quarter of 2011. This span of data includes several business cycles and, notably, a high number of quarters both before and after the introduction of the euro. Thus, with this model we are not only able to capture any possible time variation around business cycle phases, but also time variation caused by (possibly lengthy) structural changes (see Canova, Ciccarelli, and Ortega 2012).

The real variables included are the growth rates of (i) GDP, (ii) private consumption, (iii) gross fixed capital formation, (iv) the real effective exchange rate and (v) trade – defined as the average of export and import growth – which best capture the real business cycle and competitiveness. We include two types of financial variables: one representing financial prices, the other reflecting the situation in the lending market. More specifically, as regards financial prices we use: (vi) the term spreads in order to account for country risk, and the growth rates of (vii) real stock prices and (viii) real house prices. Finally, for loans we have used the growth rates of (ix) the loans-to-deposits ratio as a measure of bank leverage and (x) total outstanding nominal loans to the private sector deflated by the CPI, which we believe are most appropriate for representing the flow of lending into the economy.^[1]

Most data come from the OECD and IMF databases; detailed sources for each variable can be found in the data Appendix. We use annual growth rates (except for the term spreads, taken in levels), which are further standardized in order to obtain meaningful aggregations of these series given our model (see next section).

Our sample of ten countries covers the bulk of the developed world, namely the G7, which includes the biggest economies in the euro area, as well as other relevant European economies. More precisely, the five euro countries included are France, Germany, Ireland, Italy and Spain. Beyond the euro area, two other EU countries (Sweden and the UK) are included, as well as the three non-European G7 economies, namely the US, Canada and Japan.

2.2 The empirical model

The variables described interact in a panel VAR framework as developed by Canova and Ciccarelli (2009) and Canova, Ciccarelli, and Ortega (2007). The model has the following form:

where i=1, …, N indicates countries, t=1, …, T indicates time and L is the lag operator; y_it is a G×1 vector of variables for each i and Yt=(y′1t, y′2t, …, y′nt)′; Dit,j are G×NG matrices for each lag j=1, …, p; e_it is a G×1 vector of random disturbances. As we use demeaned variables in this analysis we can omit the constant term.

Model (1) displays three important features, which makes it ideal for our study. First, the coefficients of the specification are allowed to vary over time. Without this feature, it would be difficult to study the evolution of cyclical fluctuations; smooth changes in business cycle characteristics could be attributed to once-and-for-all breaks, which given the historical experience would be hard to justify. Second, the dynamic relationships are allowed to be country-specific. This avoids heterogeneity biases, which could easily distort economic conclusions. Third, whenever the NG×NG matrix D_t(L)=[D_1t(L), …, D_Nt(L)]′ is not block diagonal for some L, cross-unit lagged interdependencies matter. This makes dynamic feedback across countries possible and greatly expands the type of interactions our empirical model can account for.

Model (1) can be re-written in a simultaneous-equation form:

where Y_t and E_t are NG×1 vectors of endogenous variables and of random disturbances, respectively, while Zt=ING⊗X′t; X′t=(Y′t−1, Y′t−2, …, Y′t−p), δt=(δ′1t, …, δ′Nt)′ and δ_it are Gk×1 vectors containing, stacked, the G rows of matrix D_it. Since δ_t varies in different time periods for each country-variable pair, it would be difficult to estimate it using unrestricted classical methods. And even if δ_t were time invariant, its sheer dimension (there are k=NGp parameters in each equation) could prevent any meaningful unconstrained estimation.

To cope with the problem of dimensionality, we adapt the framework in Canova and Ciccarelli (2009) and assume δ_t has a factor structure:

where Ξ is a matrix of zeros and ones, dim(θ_t)<<dim(δ_t), and u_t is a vector of disturbances with mean zero, capturing unmodeled features of the coefficient vector δ_t. The vector θ_t is partitioned in 2 sub-blocks. The first sub-block accounts for elements in the coefficient vector δ_t, which are common across countries (global factors) but specific for groups of (i) real, (ii) credit and (iii) price variables. The second sub-block accounts for elements in the coefficient vector δ_t, which are country specific. Formally, we consider the following specification:

where Ξ₁ is a matrix of dimensions NGk×3, and Ξ₂ is a matrix of dimensions NGk×N. The components in θ_1t and θ_2t are mutually orthogonal factors of dimensions 3×1 and N×1, respectively.

Factoring δ_t as in (3) reduces the problem of estimating NGk coefficients into the one of estimating for example, N+3 factors characterizing their dynamics. The factorization (3) transforms an overparameterized panel VAR into a parsimonious SUR model, where the regressors are averages of certain right-hand side VAR variables. In fact, using (3) in (2) we have

where Ƶ_t=Z_tΞ and υ_t=E_t+Z_tu_t.

From an economic point of view, the decomposition in (5) is convenient since it allows us to measure the relative importance of global and country-specific influences in explaining fluctuations in Y_t and provides their evolution over time. In fact, Z_tΞ₁θ_1t is a vector of global indicators and, specifically, if θ₁≡(θ₁₁, θ₁₂, θ₁₃)′ and Ξ₁=(Ξ₁₁, Ξ₁₂, Ξ₁₃), then Z_tΞ₁₁θ_11t is a global real indicator, Z_tΞ₁₂θ_12t is a global loan indicator, Z_tΞ₁₃θ_13t is a global financial price indicator; whereas Z_tΞ₂θ_2t is a vector of country specific indicators. Note that these indicators are correlated by construction, as Z_t enters in all of them, but should become uncorrelated as the number of countries and variables in the panel becomes large. Since they are smooth linear functions of the lagged endogenous variables, such indices are in fact leading indicators of global and country trends.

Note that if the factorization (3) is not exact (that is, the variance of u_t is not zero) the error term v_t contains a heteroschedasticity of known form. as is common practice in this context (Canova and Ciccarelli 2009), we impose an exact factorization but assume that the error term E_t has a Student’s-t distribution with α degrees of freedom and scale matrix Ω:

This assumption is convenient because a Student’s-t distribution on the error term is a robustification of the usual Normal distribution with fatter tails. In fact, it can be shown that a Student’s-t is a mixture of a Normal and an inverse gamma. In other words we can re-write (6) as

Et|σt∼N(0, σtΩ)σt∼IG(α2, α2)

Therefore, we can introduce a general form of heteroschedasticity by simply replacing the Normal assumption with a Student’s-t on the error term (see e.g. Koop 2003). This simple assumption makes our model specification robust to time heteroscedasticity and is consistent with the findings of widespread literature that suggests that the volatility of shocks has changed numerous times over the last 40 years (see, e.g. Cogley, Primiceri, and Sargent 2010). Note that large values of α makes the Student’s-t converge to a Normal distribution and σ_t collapse to its prior mean of 1. This parameter therefore determines the degree of heteroschedasticity in the data: with small posterior values of α the data would support a departure from normality and therefore a high degree of heteroschedasticity.

To complete the specification, we assume that θ_t evolves over time as a random walk

and specify B̅ as a block diagonal matrix. We also assume, as is customary, that E_t and η_t are independent. The random-walk assumption is very common in the time-varying VAR literature and has the advantage of focusing on permanent shifts and reducing the number of parameters in the estimation procedure.^[2] The block diagonality of B̅ guarantees orthogonality of the factors, which is preserved a-posteriori, hence their identifiability.

The model is estimated with Bayesian techniques. In the Appendix we illustrate it with a simple example and provide the estimation details by describing the chosen priors and deriving the posterior distributions.

3.1 What role do global and country-specific factors play in explaining macro-financial linkages?

In this section, we use the empirical model to check if macro-financial linkages differ across countries and over time. In particular, we check whether there are significant common components in the macro-financial interactions across the main developed economies and/or whether country-specific heterogeneities matter more in explaining fluctuations in domestic variables.

As discussed above (see Section 2.2), three distinct global components are identified: one common to all financial prices (real stock prices, house prices and term spreads); one common to all real variables (GDP, private consumption, gross fixed capital formation, the real effective exchange rate and trade); and one common to lending markets (ratio of total loans to deposits and credit growth).

From a visual inspection of the three global factors, reported in Figure 1, several observations can be made.

Figure 1:

Evolution of global factors over time.

The charts plot the variable factors across all countries expressed in standard deviations from the historical average of annual growth rates. The solid black line represents the posterior median of the estimated distribution for the common factor at each point in time. The two dotted lines limit the 68% Bayesian credible interval. This corresponds to Z_tΞ₁θ_1t in the paper (Section 2.2). “Loans” is a common factor for bank leverage (loans to deposit ratio) and the flow of credit into the economy (measured as the y-o-y growth of total outstanding nominal loans to the private sector deflated by the CPI). The “Real variables” are growth rates of GDP, private consumption, gross fixed capital formation, real effective exchange rates and trade (average of export and import). “Financial prices” are bonds spreads, and the prices of stocks and of real estate deflated by CPI.

First, and as found elsewhere in the literature (see, e.g. Kose, Otrok, and Prasad 2008; Claessens, Kose, and Terrones 2011), we confirm the existence of statistically significant global factors (real and financial) across all countries throughout several cycles. The panels in Figure 1 show the evolution of these common components, expressed as standard deviations from the historical averages of annual growth rates. Each of these components is statistically significant for most of the sample, that is, the 68% posterior confidence interval is above or below zero, which means that each type of variable features a significant common movement across countries, and their fluctuations are most pronounced in the 2008–2009 recession.

Second, the common financial prices component is also found to be significant throughout the whole sample despite the inclusion of the evolution over time of the term spread. The term spread may be counter-cyclical, while the other two variables – real house and real stock prices – are mostly pro-cyclical. This component is particularly significant during recessions and from the year 2000 until the financial crisis. This may be a reflection of deeper financial integration across developed economies, related perhaps to the introduction of the common currency in Europe.

Third, the global real component appropriately captures both a slack in the early 1980s and the recession of the early 1990s and also identifies a recession in the years 2001–2002. It is noteworthy that the most recent global crisis appears to be by far the largest common fluctuation across countries and variables. Moreover, the posterior uncertainty is remarkably low towards the end of the sample, including the 2008–2009 recession as well as the incipient signs of a double dip recession in 2011. This finding of a significant global real component is in line with the international business cycle literature, which often finds stronger co-movement among real aggregates both within and across countries (e.g. Crucini, Kose, and Otrok 2011).

Fourth, the global lending component is also very significant throughout the sample, in recessions as well as in expansions, showing a lending cycle that mimics the business cycle. In fact, unconditional lead-lag cross-correlations among the two estimated components confirm that the global real component leads the global lending component with a correlation peak of 0.62 at a three-quarter lead. This is a remarkable result which confirms and qualifies the common understanding that amplification mechanisms between macroeconomic fluctuations and financial constraints are in place at a global level (see Kiyotaki 1998; Claessens, Kose, and Terrones 2011). This pattern between real and lending variables changed during the last recession, when, unlike in previous recessions, the loans component started falling as early as in 2007 – that is, 1 year before the real component – coinciding with the tightening of credit supply as documented by, for example, the euro area Bank Lending Survey (BLS). It then dropped further after 2009, consistent with both the credit demand and supply reductions reported in the BLS, and thus contributing to the dramatic fall in the real component.^[3]

Fifth, both the loans and the real component also show the double dip recession that the world economy experienced in 2011, coinciding with the worsening European sovereign debt crisis. The charts confirm how, from a historical perspective, the last recession was particularly unique for both the financial and the real sectors of the world economy. It produced a much larger fluctuation compared with those observed in the preceding three decades for real variables, and a more persistent one for lending.

Finally, the analysis by global variable types confirms the leading nature of financial prices. We find that the common factor of financial prices leads fluctuations in both real and credit variables.^[4] In fact, in most recessions financial prices are usually the first to recover, followed by real variables, while the lending market is the last to recover, which is in line with stylised facts of the business cycle. Simple lead-lag cross-correlations among the three estimated factors suggest that financial prices lead real activity by three to four quarters and the loan market by five quarters, with a correlation peak at 0.5 in both cases.

Despite these findings at the global level, the country-specific component in fluctuations of real and financial variables remains significant. Figure 2 shows the country-specific components in our sample, which are very precisely estimated. The charts show that countries differ substantially in terms of the intensity and duration of their cycles and also, in some cases, in the timing of the phases. There are countries in which the fluctuations common to their own real and financial series, as shown by the 68% confidence intervals, lie well above zero in a particular period. In other countries, they are zero or even negative for the same period. The differences in the joint evolution of real and financial series across countries could be an indication of periods of non-synchronized business cycles across countries. The existence of such heterogeneity could be caused, for example, by the presence in one country of a financial bubble otherwise absent in another, while in other countries the business cycle is only being driven by real economic developments.

Figure 2:

Evolution of country factors over time.

The charts plot the country factors of all macroeconomic and financial variables expressed in standard deviations from the historical average of annual growth rates. The solid black line represents the posterior median of the estimated distribution for the common factor at each point in time. The two dotted lines limit the 68% Bayesian credible interval. These factors correspond to Z_tΞ₂θ_2t in the paper (Section 2.2).

It is also interesting to note the difference in behavior of national factors relative to common factors. For instance, the intensity of the crisis during the early 1990s was very strong in Sweden, which was suffering a banking crisis that not only lasted longer than the recessions in the UK, US and Canada, but also started earlier than the European Exchange Rate Mechanism (ERM) crisis. On the other hand, the recession in 2001 was strong in Japan and France compared to other countries. As with the previous recession, the US and Sweden also experienced this recession earlier than euro area member countries.

Another point of interest is the long period of almost uninterrupted growth (financial and real) in Ireland and Spain prior to the sharp fall in both their economies during the Great Recession. This contrasts with the stagnation of the Italian economy during most of that same period and with the clear underperformance of the Japanese economy throughout the last two decades.

3.2 How important is the cross-country transmission of shocks?

In this section, we aim to look further into these linkages. We also consider whether these co-movements reflect significant spillovers between financial and real variables across countries or whether they are just the coincidence of simultaneous shocks. Also of interest is examining whether the spillovers of shocks across countries are greater if they are financial or real in origin. Finally, we look at whether the international transmission of shocks has changed during the great recession.

Modeling both real and financial series for several countries within the same empirical model makes it possible to understand better the role of cross-country spillovers in the linkages between financial and real variables. Thus, within the same panel VAR framework used to estimate the global and country factors, we compute some counterfactual impulse responses. This allows to determine how changes in a particular variable in a given country affect other variables in other countries.

To work out the impact of these spillovers, we compute impulse response functions as the difference between a conditional and an unconditional projection of, for example, GDP growth for each country in a given period. The unconditional projection is the model projection for GDP growth for that period based only on historical information and consistent with a model-based forecast path for the other variables. The conditional projection for GDP growth is the one the model would have obtained over the same period conditionally on the actual path of another variable, such as US stock prices, for that period. The difference between an unconditional forecast of US stock prices and their actual path over that horizon defines a shock to US stock prices.^[5]

Clearly, the notion of shock here must be taken cum grano salis, for there is no identification of structural shocks as is often the case in the VAR literature. Furthermore the actual movement of the conditioning variables over the forecast horizon could be due to a variety of reasons. Nonetheless, this counterfactual exercise is helpful in answering certain questions. For example, based on actual US stock price developments as compared to a prediction based on the historical path of US stock prices, what rate of GDP growth would the model have predicted? Thus, the method provides a measure of the shock based on what actually occurred, with the defined shock starting at the observed peak of the series (US stock prices in this example) and lasting until its observed trough. The dating is somewhat arbitrary and differs across variables and countries. We report only the median impulse responses to highlight the heterogeneity across countries. The same responses with 90% Bayesian credible intervals are also reported in the Appendix for each country and time period (Figures A2–A7).

We first investigate whether negative shocks in the US, both in the financial and real sectors, were transmitted to real activity in the other countries in our sample. Moreover, by looking at three different periods, we can observe whether the extent of the spillover has changed over time.

Figure 3 shows the counterfactual responses of GDP growth in all countries to shocks that moved GDP in the US at three different points in time, which coincide with the start of the last three recessions in the US. The extent of cross-country interdependence is clear from the chart, as the fall in US GDP growth beyond the unconditional forecast (units are standard deviations of the demeaned series) causes a substantial fall in the real economy in every other country, although the reaction is always less pronounced than it is in the US. As expected, the spillover varies in intensity across countries, with Canada and the UK showing a larger response, reflecting their deeper economic linkages to the US, while Spain, Germany and Ireland show the smallest responses.^[6]

Figure 3:

GDP responses to US GDP shock.

The charts report counterfactual responses computed as the difference between a conditional and an unconditional projection of GDP growth for each country in a given period. The unconditional projection is the one the model would have obtained for GDP growth for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection for GDP growth is the one the model would have obtained over the same period conditionally on the actual path of US GDP growth for that period. The upper panel shows the responses of GDP growth in all countries to a US GDP shock at three different points in time. The lower panel shows the same responses re-scaled by the size of the “shock” to US GDP in each of the three recession episodes.

It is also interesting to note that the size of the shock and the responses vary over time, with the latest recession showing both the largest shock and the largest responses. But neither the ranking of the spillovers of the US shock to other economies nor the proportionality of the responses to the shock have changed much over time. Indeed, the lower panel of Figure 3 shows the same responses as in the upper panel re-scaled by the size of the movement in US GDP (peak to trough) in each of the three periods of recession. As can be clearly seen, the intensity of the spillovers does not seem to have changed over time: however, it is the shock that has been more intense in the 2008–2009 recession. This result is consistent, for example, with Stock and Watson (2012) who, using a large scale dynamic factor model for the US, find that the last recession could be characterized by a larger version of shocks previously experienced, to which the economy responded in a predictable way.

Figure 4 shows the GDP responses to a shock that negatively affected the stock market in the US at different points in time. The country responses are in general on a smaller scale than those to a US real shock (see Figure 4). However, the difference between the US response and those of the other countries is smaller than in the previous case. This could be taken as an indication that US financial shocks generate larger international spillovers than do real ones. Rescaling the GDP responses by the size of the shock as before, the lower panel of Figure 4 seems to show that there is no discernible change in the pattern of international transmission over time, with UK and Sweden reacting slightly more than all other countries.

Figure 4:

GDP responses to US stock price shock.

The charts report counterfactual responses computed as the difference between a conditional and an unconditional projection of GDP growth for each country in a given period. The unconditional projection is the one the model would have obtained for GDP growth for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection for GDP growth is the one the model would have obtained over the same period conditionally on the actual path of US stock prices for that period. The upper panel shows the responses of GDP growth in all countries to a US stock price shock at three recessive episodes. The lower panel shows the same responses re-scaled by the size of the “shock” to US stock prices in each of the three periods.

3.2.1 How important are financial factors compared with real factors in the transmission of shocks?

In what follows, we study the spillover effects during selected episodes of intense deviations observed in the growth rate of real or financial series in other countries. The aim is to determine whether there is a pattern in the spillovers, such as increased intensity if the shock originates in a particular type of variable (e.g. financial versus real) or a particular country.

As for the real shocks, Figure 5 reports the GDP responses to the very country-specific downturn in Japan at the end of the 1990s, while Figure 6 shows the spillovers of the German recession in 2002. As expected, in both cases the country in which the shock originated transpires to be the one showing the largest response. However, in all other countries there is a significant and negative response to the unexpected contraction in economic activity in Japan or Germany and, as would be expected given our sample, the response to a German shock is slightly greater. The same partial transmission is observed for a positive real shock, such as the strong growth observed in private consumption in the UK in 1987–1988, which shows a similar (positive) response in all other countries (not reported). Nevertheless, the response is of course much smaller in the other countries. The response in the US to these shocks is surprisingly large and mostly significant, suggesting that our model also captures indirect effects (see Figures A4 and A5 in the Appendix).

Figure 5:

GDP responses to Japanese GDP shock.

The chart reports countefactual responses computed as the difference between a conditional and an unconditional projection of GDP growth for each country over the period 1997:4–2000:1. The unconditional projection is the one the model would have obtained for GDP growth for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection for GDP growth is the one the model would have obtained over the same period conditionally on the actual path of Japan’s GDP growth for that period.

Figure 6:

GDP responses to German GDP shock.

The chart reports counterfactual responses computed as the difference between a conditional and an unconditional projection of GDP growth for each country over the period 2001:2–2003:4. The unconditional projection is the one the model would have obtained for GDP growth for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection for GDP growth is the one the model would have obtained over the same period conditionally on the actual path of Germany’s GDP growth for that period.

We turn now to different periods of shocks to financial variables observed in different countries. Figure 7 reports the counterfactual responses of GDP growth and of total credit growth across countries to the unexpected credit contraction in Sweden in the early 1990s. Although the spillover of this particular shock to real activity is not long-lasting and mostly insignificant even in the originating country, the shock had significant negative spillovers to credit markets in other countries.

Figure 7:

Responses to Swedish credit shock.

The charts report counterfactual responses computed as the difference between a conditional and an unconditional projection of GDP growth (upper panel) and credit growth (lower panel) for each country over the period 1990:1–1992:3. The unconditional projection is the one the model would have obtained for each variable for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection is the one the model would have obtained over the same period conditionally on the actual path of Sweden’s credit growth for that period.

Similar responses are obtained with regard to a positive shock to the stock market in Spain that occurred when the country joined the European Community in 1986Q2 (see Figure 8). While GDP responses are not economically significant, even in Spain, the temporary boom in the Spanish stock market was transmitted to stock markets of other developed economies, although with less intensity than might have been expected.

Figure 8:

Responses to Spanish stock price shock.

The charts report counterfactual responses computed as the difference between a conditional and an unconditional projection of GDP growth (upper panel) and stock prices (lower panel) for each country over the period 1985:3–1987:4. The unconditional projection is the one the model would have obtained for each variable for that period based only on historical information, and consistent with a model-based forecast path for the other variables. The conditional projection is the one the model would have obtained over the same period conditionally on the actual path of Spain’s stock prices for that period.

We find more or less the same pattern across the countries in our sample for other periods of financial shocks, such as housing price booms (e.g. the UK in 1986–1987) or busts (e.g. in Spain and Ireland from 2007 onwards). In all cases, we find partial but more economically and statistically significant spillovers to the same financial variable in other countries than to their real economy, even in the country of origin.

In sum, and as expected, we find not only that spillovers matter, but also that the international transmission of a shock may be faster and deeper between financial variables than between real variables. On the other hand, it seems that for a shock to a financial variable to affect significantly the real economy elsewhere, that shock needs to be either common to all countries or to have originated in a systemic country, as could be seen in the case of shocks to the US stock market. Thus, shocks to financial variables in small countries do not affect the real economy much, whereas a financial shock that occurs in the US tends to affect the real economy not only in the US but elsewhere, too. This is in line with, for example, Eickmeier and Ng (2011), who find that negative credit supply shocks in the US have stronger negative effects on domestic and foreign GDP than do those which originated in other countries or areas such as the euro area and Japan.

Finally, we also find that, concerning the spillovers or transmission of shocks, the last recession has been very similar to past recessions except in terms of the size of the shock. These results are obtained with a non-structural model which allows for a general form of stochastic volatility – hence, one should not go too far in interpreting them. However, this last finding could indicate that larger co-movements or macro-financial linkages observed worldwide in the last recession could, relative to previous recessions, be more closely related to the size of the shocks than to the intensification of their international transmission (see Stock and Watson 2012).

3.3 What mattered more in the last recession?

The sections above provide evidence of the existence of both real and financial factors that are common across a selection of developed economies. To complement this analysis, this section aims to gage the relative weight of real and financial common factors in explaining real fluctuations across countries and over time. This is carried out by means of auxiliary historical decompositions, which answer the question of whether the real component of this common evolution matters more than the financial component in explaining GDP developments.

In order to answer this question, we estimate a country-by-country factor-augmented VAR for GDP growth and the three variable-type components displayed in Figure 1, these capture the common movements of financial prices, real activity and lending markets across countries. We then compute the dynamic contributions of the different common components to GDP growth for each country in the sample. Thus, we show how much of the unexpected GDP growth in a given country is explained by the variable-type components that are common to all countries. For the identification in this auxiliary VAR we use a simple Choleski and order the common factors first, followed by the country GDP. Among the factors, the ordering is consistent with the finding discussed in section 3 with financial prices placed first, followed by the real variables and the loans.^[7]

Figure 9 shows this historical decomposition exercise for the 2005–2011 period. It is worth noting the large size of the common component, as captured by the sum of the contributions of the three variable-type factors to GDP growth fluctuations. Obviously, the relative weight of each of the three common factors changes across countries and over time. In particular, at the beginning of the last recession, the financial factors (see the bar in green bar and the bar in red, which represent financial prices loan markets, respectively) dominated, while the real component (the bars in blue) became more important as the recession deepened. Nevertheless Stock and Watson (2012), when using a different methodology, also find that the shocks in the last recession were mainly associated with financial disruptions and heightened uncertainty. The real common downturn is very relevant in explaining the most recent recession, especially in the case of Japan, Germany, Italy or Canada, but gained momentum towards the end of the recession in all other countries, too. Despite all this, the common financial factors are also of relevance, confirming the widespread belief that GDP growth would have been much higher (that is, less negative) without the financial crisis. Comparing the role of the financial prices factor to the loan markets factor, we see that asset prices in most countries are more relevant in explaining the downturn, while the loan market factor played a role at the beginning of the recession, especially in Ireland, and was fairly relevant in explaining strong GDP growth pre-crisis in some countries such as Spain and Ireland, as would be expected.

Figure 9:

Historical decomposition.

Sample 2005:1–2011:4.

The charts report historical decompositions based on the estimation of a country by country factor-augmented VAR for GDP growth and the three variable-type components displayed in Figure 1, which capture the common movements of financial prices, real activity and lending markets across countries. The decomposition shows the dynamic contributions of the different common components to GDP growth for each country in the sample over the period 2005:1–2011:4 and illustrates how much of the unexpected GDP growth in a given country is explained by the variable-type components that are common to all countries.

Figures 10 and 11 show the same dynamic contributions for the periods that include the two previous recessions. Compared to the last recession, previous recessions had a smaller common component, be it of a real or financial nature, as can be seen by the larger size of the idiosyncratic components (see the bars in purple). This is not surprising, since the downturns of the early 1990s and of the early 2000s were much less synchronized than the 2008–2009 recession. Still, the common components undoubtedly played a role in previous recessions, especially the financial factors (see red and green bars), while the common real component was less relevant than in the 2008–2009 recession. A distinctive feature of the great recession, thus, seems to have been the large common real downturn across developed economies.

Figure 10:

Historical decomposition.

Sample 1999:1–2004:4.

The charts report historical decompositions based on the estimation of a country by country factor-augmented VAR for GDP growth and the three variable-type components displayed in Figure 1, which capture the common movements of financial prices, real activity and lending markets across countries. The decomposition shows the dynamic contributions of the different common components to GDP growth for each country in the sample over the period 1999:1–2004:4 and illustrates how much of the unexpected GDP growth in a given country is explained by the variable-type components that are common to all countries.

Figure 11:

Historical decomposition.

Sample 1990:1–1994:4.

The charts report historical decompositions based on the estimation of a country by country factor-augmented VAR for GDP growth and the three variable-type components displayed in Figure 1, which capture the common movements of financial prices, real activity and lending markets across countries. The decomposition shows the dynamic contributions of the different common components to GDP growth for each country in the sample over the period 1990:1–1994:4 and illustrates how much of the unexpected GDP growth in a given country is explained by the variable-type components that are common to all countries.