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After the recent banking crisis in 2008, financial market conditions have turned out to be a relevant factor for economic fluctuations. This paper provides a quantitative assessment of the impact of financial frictions on the Spanish economy. We augment the model of Christiano et al. (2010, 2014) to a small economy model with a banking sector able to diversify portfolio choices between loans and risk-free German bonds (Bunds). Our model also includes, the inflation differential between Spain and the European Monetary Union (EMU) in order to quantify the influence of the implementation of the single monetary policy in Spain. We find that structural features of the Spanish economy as well as the increase in the volatility of inflation differentials played an important role during the 2009-2013 crisis. Our results show that inflation differentials as well as anticipated risk shocks, measured as the volatility of idiosyncratic uncertainty in the financial sector, are key in the evolution of the economic crisis in Spain.


The global financial crisis of 2007, originated by the sub-prime crisis in the US, had a major impact worldwide. The influence on the Spanish economy has been deeper and more long-lasting due to the state of the financial system when the crisis began. With the establishment of the single currency in 2002, the exchange rate risk disap- peared and Spain started to receive capital inflows from abroad. This produced a dramatic fall in the Spanish interest rates. The integration of Spain into the European Union led to a higher economic growth in Spain relative to the Euro Area (2.3 points on average). During this period of economic boom, credit flowed and created a process of excessive indebtedness of public and private sectors and a high dependence on credit to generate economic activity. As a consequence the Spanish inflation was on average higher than the EMU’s inflation.

At the time the financial crisis struck, Spain was vulnerable to monetary and financial shocks. The Spanish economy entered into a recession and the number of firms that went bankrupt rapidly escalated. As a result, the number of non-performing loans increased dramatically during this period. With the increase in the number of non-performing loans, banks tightened their collateral constraints and started lending less to the private sector.

Due to the increase in loan risk, the banks started allocating their investments to other assets. As a consequence, the international demand for German bonds, rated as minimum risky assets (AAA rating), increased.

The Model

This section provides a brief overview of the model. The model includes households, intermediate good firms, final good firms, a government, a bank and a foreign unlimited supplier of risk-free assets.

The model belongs to the class of DSGE models with real and nominal rigidities developed by Smets and Wouters (2003) augmented to include a financial accelerator mechanism à la BGG. The economy is populated by identical households. Each household contains a unitary continuum of workers and a large number of entrepreneurs. The source of funds for households are labor earnings, deposit yields, revenues of capital which are accumulated by households, and other lump-sum transfers. The household allocates resources to consumption purchases, short-term deposits, and the purchase of investment goods and existing capital in the economy. The representative household maximizes the expected value of the discounted utility of its members derived from leisure time, deposits holdings and from consumption with habit formation.The final good is produced using a continuum of intermediate goods according to a Dixit-Stiglitz technology. The elasticity of substitution among intermediate goods is stochastic to account for markup fluctuations. The producers of intermediate goods use the services of physical capital and labor, technology described by a stochastic Cobb-Douglas production function subject to transitory shocks on the total factor productivity and (permanent) shocks on the trend of labor technological progress. The second source of growth of the model is the investment specific technology growth, which decreases the price of investment.

Prices and wages are subject to nominal rigidities `{a} la Calvo. Monopoly suppliers of labor and of intermediate goods can reoptimize their wage and price, respectively, only periodically (with an exogenous probability). Households accumulate raw capital by purchasing the existing undepreciated capital of the economy and investment goods, which are subject to adjustment costs. Adjustment costs are stochastic because there is a shock on the marginal efficiency of investment in producing capital. Raw capital cannot be directly used in the production sector that uses effective capital. Households sell raw physical capital to entrepreneurs who transform it into effective capital. To buy raw capital, entrepreneurs use their personal wealth as well as loans obtained from a financial intermediary. The loan contract is characterized by agency problems subject to financial shocks: the entrepreneurs can observe their shock realization, but the bank needs to verify the state of the entrepreneur and pay the implied verification cost.

At the beginning of the period the households make their optimal choices: provide deposits to the bank, supply labor to the intermediate good firms and consume. The monopolistic intermediate firms produce intermediate goods using the labor of the households and the rented capital of the entrepreneurs. They sell their production to a unique final good producer. This competitive final good producer aggregates the intermediate goods and converts the output into consumption goods, investment goods, goods used up in capital utilization and in bank monitoring. The capital producers use the investment goods to invest in capital. They sell the new capital to the entrepreneurs. The entrepreneurs finance their capital acquisition by loans provided by the bank as their wealth is not enough to self-finance. The bank is a competitive bank that makes portfolio investment decisions. It allocates the savings across loans to entrepreneurs and foreign bonds.

The monetary authority sets the nominal interest rate of the European Monetary Union (EMU hereon) given its past value, the deviations of inflation respect to their steady-state values, and a stochastic disturbance, which is referred as the monetary policy shock. Following Fernandez et al. (2010), the deviation of Spanish inflation from EMU’s inflation is described as a zero mean idiosyncratic shock.

The Small Economy

Priors and Posteriors

We partition the model parameters into two sets. The first set contains parameters that are calibrated using data of Spain between the first quarter of 2000 and the fourth quarter of 2016. The second set contains parameters that have been estimated using Bayesian techniques.



Parameter Type Mean SD Mean 90% CI
\(ξ_w\): Calvo wages Beta 0.75 0.1 0.785 0.768-0.809
\(b\): Habit parameter Beta 0.5 0.1 0.844 0.828-0.868
\(F(ω)\): Steady-state prob. of default Beta 0.008 0.004 0.028 0.026-0.032
\(μ_p\): Monitoring cost Beta 0.275 0.15 0.137 0.128-0.141
\(σ_a\): Capacity utilization Normal 1 1 0.314 0.162-0.498
\(S\): Investment adjust. cost Normal 5 3 21.259 20.161-22.266
\(α_{π}\): Weight on inflation in Taylor rule Normal 1.5 0.25 1.779 1.625-2.019
\(ρ_{τ^{oil}}\): Oil price shock Beta 0.75 0.1 0.888 0.882-0.898
\(ι_p\): Weight on steady state inflation Beta 0.5 0.15 0.935 0.904- 0.97
\(ι_w\): Weight on steady state inflation Beta 0.5 0.15 0.789 0.687-0.882
\(ι_{μ}\): Wage weight on persist. tech. growth Beta 0.5 0.15 0.903 0.883-0.921
\(ρ_{λ_{f,t}}\): Price mark-up shock Beta 0.5 0.2 0.937 0.928-0.947
\(ρ_{μ_t}\): Equity shock Beta 0.5 0.2 0.925 0.914-0.935
\(ρ_{g_t}\): Government consumption shock Beta 0.5 0.2 0.962 0.943-0.974
\(ρ_{μ_{z}^{*}}\): Persistent. product. shock Beta 0.5 0.2 0.076 0.063-0.095
\(ρ_{ε_t}\): Transitory product. shock Beta 0.5 0.2 0.818 0.793-0.856
\(ρ_{π}\): Idiosyn. infla. innov. Beta 0.5 0.2 0.207 0.14-0.267
\(ρ_{σ_t}\): Riskiness shock Beta 0.5 0.2 0.856 0.843-0.881
\(ρ_{ζ_c}\): Demand shock Beta 0.5 0.2 0.912 0.893-0.927
\(ρ_{ζ_i}\): Margin. effi. of invest. shock Beta 0.5 0.2 0.967 0.96-0.971
\(σ_{σ,π}\): Std. dev. of the anticipated risk shock Inv. gamma 0.001 0.001 0.149 0.136-0.156
\(σ_{σ,0}\): Std. dev. of the unanticipated risk shock Inv. gamma 0.002 0.003 0.001 0.001-0.001
\(σ_{λ_{f,t}}\): Price markup shock Inv. gamma 0.002 0.003 0.016 0.014-0.017
\(σ_{μ_t}\): Equity shock Inv. gamma 0.002 0.003 0.007 0.007-0.007
\(σ_{g_t}\): Government consumption shock Inv. gamma 0.002 0.003 0.024 0.024-0.025
\(σ_{μ_{z}^{*}}\): Persistent. product. shock Inv. gamma 0.002 0.003 0.012 0.011-0.012
\(σ_{γ_t}\): Financial wealth shock Inv. gamma 0.002 0.003 0.003 0.003-0.003
\(σ_{ε_t}\): Transitory product. shock Inv. gamma 0.002 0.003 0.011 0.01-0.011
\(σ_{ε_t}\): Monetary policy shock Inv. gamma 0.583 0.825 1.273 1.214-1.329
\(σ_{π}\): Std. dev. of idiosyn. infla. innov. Inv. gamma 0.583 0.825 0.032 0.031-0.033
\(σ_{ζ_c}\): Demand shock Inv. gamma 0.002 0.003 0.056 0.052-0.058
\(σ_{ζ_i}\): Margin. effi. of invest. shock Inv. gamma 0.002 0.003 0.048 0.035-0.056
Meas. error of Real Net Worth Growth Weibull 0.01 5 0.08 0.077-0.085

Variance Decomposition

The next table displays the forecast error variance decomposition of observable variables at business cycle frequencies considering periodic components with cycles of 8-to-32 quarters, obtained using the model spectrum.

We find three important results related to the explanatory power of the shocks over the business cycle. First, the anticipated risk shock and the idiosyncratic inflation in novation dominate all other shocks. Together they explain half of the variation in GDP and more than three quarters of the variation in investment and credit at the businessvcycle frequencies. Second, the anticipated risk shock affects the economy through the financial variables. This shock is able to explain 35% of the variations in GDP, 72% in investment, 63% in the net worth and 98% in the premium. However, it falls short to explain fluctuations in consumption and inflation. Third, these results suggest that the unanticipated risk shock has no explanatory power in our model. This result further highlights the importance of anticipated risk shocks during the financial crisis. Before the crisis began, Spain was immersed in a credit bubble created in the housing market. A few economic pundits started pointing out that the possibility of a housing bubble. Therefore, the crisis was somewhat anticipated due to deep imbalances in the real estate sector as well as the fears on a global financial recession hitting hardly the local banking sector. The consumption demand shock is able to explain half of the variations in consumption and it explains 12% of the changes in GDP. The markup shock helps explaining 13% of the variations in GDP and 14% in consumption. The monetary policy shock helps explaining the fluctuations of interest rates, but it plays a minor role in explaining the fluctuations in the financial variables. The rest of the shocks do not show a high explanatory power. In particular, the effciency of the investment shock has low explanatory power when compared with the results reported in Christiano et al. (2010) for the US.

In sum, our estimation results suggest that the idiosyncratic inflation innovation and the anticipated risk shocks play a crucial role in explaining the fluctuations of the Spanish economy in both the short and the long run.

Transitory Technology Exogenous Spending Financial Wealth Markup Persistent Technology Monetary Policy Demand M.E.I Idiosyncratic inflation innovation Unanticipated Risk Anticipated Risk
Consumption 1 0 0 5 2 5 47 0 37 0 1
Credit 1 0 2 2 0 0 0 1 3 0 91
GDP 0 4 0 4 3 5 13 3 33 0 34
Working hours 16 3 0 4 1 4 11 4 29 0 29
Inflation 15 0 0 48 0 2 12 3 18 0 1
EU Inflation 1 0 0 2 0 0 1 0 97 0 0
Investment 0 0 0 1 0 2 0 8 15 0 73
Net Worth 0 0 0 0 0 4 0 2 25 0 70
Price Invest. 0 0 0 0 0 0 0 0 0 0 0
Premium 0 0 0 0 0 0 0 0 1 0 99
Interest Rate 1 0 0 3 0 13 1 0 81 0 0
Real Interest 1 0 0 4 0 13 1 0 80 0 0
Wages 4 0 0 19 75 0 0 0 1 0 0