Money and monetary policy in the Eurozone: an empirical analysis during crises

Money and monetary policy in the Eurozone: an empirical analysis during crises Money Macro and Finance Research Group 46 th Annual Conference Jonathan Benchimol 1 and André Fourçans 2 This presentation does not necessarily reflect the views of the Bank of Israel September 2014 1 Bank of Israel 2ESSEC Business School Jonathan and Benchimol THEMA-Université Bank of Israel de Cergy-Pontoise

Money or no money? New Keynesian models Literature review What do we do? Layout - The question of money - New Keynesian models - Literature review - Model 1 : Baseline model (Galí, 2008) - Model 2 : Non-separable model (Benchimol and Fourçans, 2012) - - Estimations - Simulations - Further research - Miscellaneous 2 / 50

Money or no money? New Keynesian models Literature review What do we do? The question of money In the current New Keynesian literature, the role of monetary aggregates is generally neglected. The main economic variables of this kind of models are: the output gap, inflation and the interest rate. Yet it s hard to imagine money completely passive to the rest of the system! 3 / 50

Money or no money? New Keynesian models Literature review What do we do? Brunner and Meltzer As individuals re-allocate their portfolio of assets, the behavior of real money balances induces relative price adjustments on financial and real assets. In the process, aggregate demand changes, and thus output. By affecting aggregate demand, real money balances become part of the transmission mechanism. The interest rate alone is thus not suffi cient to explain the impact of monetary policy and the role played by credit and financial markets. This monetarist transmission process may also imply a specific role to real money balances when dealing with uncertainty*. 4 / 50

Money or no money? New Keynesian models Literature review What do we do? Money and new Keynesian models Most of studies about New Keynesian models ignore money because of separable utilities, such as [ C 1 σ E t β i t+i 1 σ + γ ( ) ] 1 ϑ Mt+i χ N1+η t+i 1 ϑ 1 + η i=0 P t+i Solving this problem makes money completely recursive to the rest of the system of equations. Yet, real money holdings could affect household s consumption under high uncertainty. In other words, real money balances are supposed to affect the marginal utility of consumption. We have to assume non-separable utility between consumption and real money balances. 5 / 50

Money or no money? New Keynesian models Literature review What do we do? Selected papers Andrés, López-Salido and Vallés, 2006, Money in an Estimated Business Cycle Model of the Euro Area, Economic Journal + JEDC with Nelson (2009). Barthélemy, Clerc, and Marx, 2011, A two-pillar DSGE monetary policy model for the euro area, Economic Modelling. Galí, 2008. Monetary Policy, Inflation and the business cycle: An introduction to the new Keynesian framework. Princeton University Press. Ireland, 2004, Money s Role in the Monetary Business Cycle, Journal of Money, Credit and Banking. Smets and Wouters, 2003, An Estimated Dynamic Stochastic General Equilibrium Model for the Euro Area, Journal of the European Economic Association. 6 / 50

Money or no money? New Keynesian models Literature review What do we do? Selected similarities and differences Compared to the literature, we have: 1 Almost the same utility function 2 Long run variance decomposition analysis (almost same results) However, in the literature, we do not have: 3 Micro-founded NKDSGE analysis à la Galí (flexible price economy) 4 Money considerations + [3] : money in flexible-price. 5 Price-markup shock + [4] 6 Short sample analysis + [3] 7 Rolling window estimations + [3] 8 Bayesian analysis + [4] 9 Forecasting accuracy analysis + [4] 10 Short run variance decomposition analysis + [4] 10 [k] leads to different results k=1 7 / 50

Money or no money? New Keynesian models Literature review What do we do? What do we do? We compare two types of NK models in a DSGE framework. We test the models by using Bayesian techniques on Eurozone data (rolling-window estimations). Over 3 different crisis periods, we analyze changes in parameters, impulse response functions and variance decompositions. We also study the forecasting performances of the two models during these periods. 8 / 50

Solving the models The baseline model The non-separable model New Keynesian framework consist of economic agents of 3 types : Households: supply labor, purchase goods for consumption, hold money and bonds, and maximize the expected present value of utility. Firms: hire labor, produce and sell differentiated products in monopolistically competitive goods markets (Dixit and Stiglitz, 1977), and maximize profits. Central bank: controls the nominal rate of interest (ad-hoc rule). NK features: sticky and flexible-price economies à la Galí (2008). 9 / 50

Solving the models The baseline model The non-separable model Separable money in the utility Preferences of the representative household are defined over a composite consumption good C t, real money balances M t P t, and leisure 1 N t, where N t is the time devoted to market employment. Galí s utility function: Budget constraint: U t = C t 1 σ 1 σ + γe εm t 1 ϑ ( Mt ) 1 ϑ P t χn 1+η t 1+η P t C t + Q t B t + M t B t 1 + M t 1 + W t N t Production function: Y t = A t N t 1 α 10 / 50

Solving the models The baseline model The non-separable model Non-separable money in the utility CES utility function: ( U t = 1 1 σ (1 b) Ct 1 ν + be εm t ( Mt P t ) 1 ν ) 1 σ 1 ν χ 1+η N1+η t Here real money balances affect the marginal utility of consumption. The budget constraint and the production function are the same as in the baseline model. 11 / 50

Solving the models The baseline model The non-separable model Solving the models By using Lagrangian method in order to optimize the utility function with respect to the budget constraint (and a solvency condition), we obtain three first-order optimal conditions. We log-linearize around the steady state these conditions. We add an ad-hoc Taylor type rule equation to close our model. Structural shocks are assumed to follow a first-order autoregressive process with an i.i.d.-normal error term such as ε k t = ρ k ε k t 1 + ω k,t where ε k,t N (0; σ k ) for k = {p, m, i, a}. ε p t is the price-markup shock, ε m t is the money shock, ε i t is the exogenous component of the interest rate and ε a t is the technology shock. 12 / 50

ŷ f t = Solving the models The baseline model The non-separable model ( 1 + η (1 α) ln ε ) σ (1 α) + η + α εa ε 1 t σ (1 α) + η + α (1) ˆπ t = βe t [ ˆπ t+1 ] + κ x,t (ŷ t ŷ f t ) (2) ŷ t = E t [ŷ t+1 ] σ 1 (î t E t [ ˆπ t+1 ]) (3) î t = (1 λ i ) mp t = σ ϑ ŷt a 2 ϑ ît ρ m ϑ + 1 ϑ εm t (4) ( )) (λ π ( ˆπ t π ) + λ x ŷ t ŷt f + λ i î t 1 + ε i t (5) where κ x,t = (1 θ)( 1 θ β)(σ(1 α)+η+α)(1+(ε 1)εp t ) 1+(ε 1)(ε p t +α) and a 2 = 1 1. e β 1 13 / 50

Solving the models The baseline model The non-separable model ŷ f t = υ y a ε a t + υ y m mp f t υ y c + υ y smε m t (6) mp f t = υ m y +1E t [ŷ f t+1 ] + υ m y ŷ f t + 1 ν εm t (7) ˆπ t = βe t [ ˆπ t+1 ] + κ x,t (ŷ t ŷ f t ) ( ) + κ m,t mp t mp f t (8) ŷ t = E t [ŷ t+1 ] κ r (î t E t [ ˆπ t+1 ]) (9) +κ mp E t [ mp t+1 ] + κ sm E t [ ε m t+1] mp t = ŷ t κ i î t + 1 ν εm t γ =0 (10) ( λπ ( ˆπ t π c ) + λ x (ŷt ŷ f ) ) t î t = (1 λ i ) ( ) +λ m mp t mp f + λ i î t 1 + ε i t t (11) 14 / 50

Solving the models The baseline model The non-separable model Micro-founded model υ y 1+η 1 a = (ν a 1 κ (ν σ))(1 α)+η+α r = ν a 1 (ν σ) υ y m = (1 α)(ν σ)(1 a 1) (ν a 1 κ (ν σ))(1 α)+η+α mp = (σ ν)(1 a 1) ν a 1 (ν σ) υ y (1 α) c = (ν a 1 (ν σ))(1 α)+η+α log ( ) ε ε 1 κ i = a 2 /ν υ y (1 α)(ν σ)(1 a sm = 1 ) ((ν a 1 κ (ν σ))(1 α)+η+α)(1 ν) sm = (1 a 1)(ν σ) (ν a 1 (ν σ))(1 ν) υ m y +1 = a 2 ν (ν a 1 (ν σ)) a 1 = 1 1+(b/(1 b)) 1/ν (1 β) (ν 1)/ν υ m y = 1 + a 2 1 ν (ν a 1 (ν σ)) a 2 = exp(1/β) 1 κ m,t = (σ ν) (1 a 1 ) (1 α)( 1 θ β)(1 θ)(1+(ε 1)εp t ) ( κ x,t = ν a 1 (ν σ) + η+α 1 α 1+(α+ε p t )(ε 1) ) (1 α)( 1 θ β)(1 θ)(1+(ε 1)ε p t ) 1+(α+ε p t )(ε 1) 15 / 50

Estimation Simulations As in Smets and Wouters (2003), An and Schorfheide (2007) and Barthélemy, Clerc and Marx (2011), we apply Bayesian techniques to estimate our DSGE model. We use Eurozone data like Andrès et al. (2006) and Barthélemy et al. (2011) from the Euro Area Wide Model database (AWM) of Fagan, Henry and Mestre (2001). We use the M3 monetary aggregate from the Eurostat database. 16 / 50

Estimation Simulations Data ˆπ t is the inflation rate, measured as the yearly log difference of the GDP deflator from one quarter to the same quarter of the previous year; ŷ t is the output per capita, measured as the difference between the log of the real GDP per capita and its linear trend; î t is the short-term (3-month) nominal interest rate; mp t is the real money balances per capita, measured as the difference between the real money per capita and its linear trend, where real money per capita is measured as the log difference between the money stock per capita and the GDP deflator; ŷt f, the flexible-price output, and mp f t, the flexible-price real money balances, are entirely determined by structural shock. 17 / 50

Estimation Simulations Following standard conventions, we calibrate beta distributions for parameters that fall between zero and one, inverted gamma distributions for parameters that need to be constrained to be greater than zero, and normal distributions in other cases. The calibration of the micro parameters is inspired by Rabanal and Rubio-Ramírez (2007), Casares (2007), Galí (2008) and Benchimol and Fourçans (2012). 18 / 50

Estimation Simulations We define three different periods in order to analyze three different crises: 1990Q1 to 1994Q1, during the speculative attacks on currencies in the European Exchange Rate Mechanism (Black Wednesday crisis); 1999Q1 to 2003Q1, during the burst of the Dot-com bubble (Dot-com crisis); and 2007Q1 to 2011Q1, during the Subprime crisis. Rolling window estimations: for every quarter, we run a Bayesian estimation using the 48 observations before each respective quarter This sample size is validated by Fernandez-Villaverde and Rubio-Ramirez (2004). 19 / 50

Estimation Simulations and results These estimates provide the values of micro and macro parameters (explaining the dynamics of the models) over time. Following Iskrev (2010), all estimated parameters are identified for both models. We compute variance decompositions of variables with respect to shocks (technology, price-markup, money, and monetary policy). Finally, we run DSGE forecasts after each estimation in order to compare the forecasting performances of the two models over four out-of-sample periods (one year). 20 / 50

Estimation Simulations [ERM crisis] Variance decompositions of output with respect to the money shock (in percent) from Model 2. Whatever the value of γ, money has no role in Model 1. 6 Short run 5.5 5 4.5 4 3.5 3 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 Long run 1.1 1 0.9 0.8 0.7 0.6 0.5 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 21 / 50

Estimation Simulations [ERM crisis] Variance decompositions of output with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). Short run 34 32 30 28 26 24 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 Long run 3 2.5 2 1.5 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 Model 1 Model 2 22 / 50

Estimation Simulations [ERM crisis] Variance decompositions of inflation with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). 35 Short run 30 25 20 15 10 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 4.5 Long run 4 3.5 3 2.5 2 1.5 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 Model 1 Model 2 23 / 50

Estimation Simulations [ERM crisis] Comparison of output and inflation DSGE forecast errors. Model 2 is better when the bar is positive, Model 1 is better otherwise. 2% 1.5% Output Inflation 1% 0.5% 0% 0.5% 1% 1.5% 2% 90Q1 90Q2 90Q3 90Q4 91Q1 91Q2 91Q3 91Q4 92Q1 92Q2 92Q3 92Q4 93Q1 93Q2 93Q3 94Q1 24 / 50

Estimation Simulations [Dot-com crisis] Variance decompositions of output with respect to the money shock (in percent) from Model 2. 6 Short run 5 4 3 2 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 Long run 1.4 1.2 1 0.8 0.6 0.4 0.2 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 25 / 50

Estimation Simulations [Dot-com crisis] Variance decompositions of output with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). 28 Short run 27 26 25 24 23 22 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 2.4 2.2 2 1.8 1.6 1.4 Long run 1.2 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 Model 1 Model 2 26 / 50

Estimation Simulations [Dot-com crisis] Variance decompositions of inflation with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). Short run 30 25 20 15 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 Long run 3 2.5 2 1.5 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 Model 1 Model 2 27 / 50

Estimation Simulations [Dot-com crisis] Comparison of output and inflation DSGE forecast errors. Model 2 is better when the bar is positive, Model 1 is better otherwise. 0.6% 0.4% 0.2% 0% 0.2% 0.4% 0.6% Output Inflation 0.8% 1% 99Q1 99Q2 99Q3 99Q4 00Q1 00Q2 00Q3 00Q4 01Q1 01Q2 01Q3 01Q4 02Q1 02Q2 02Q3 03Q1 28 / 50

Estimation Simulations [Subprime crisis] Variance decompositions of output with respect to the money shock (in percent) from Model 2. 11 Short run 10 9 8 7 6 5 4 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 4 Long run 3.5 3 2.5 2 1.5 1 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 29 / 50

Estimation Simulations [Subprime crisis] Variance decompositions of output with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). Short run 30 25 20 15 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 Long run 2 1.5 1 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 Model 1 Model 2 30 / 50

Estimation Simulations [Subprime crisis] Variance decompositions of inflation with respect to the monetary policy shock (in percent) from Model 1 (solid lines) and Model 2 (dashed lines). 50 Short run 45 40 35 30 25 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 Long run 5 4.5 4 3.5 3 2.5 2 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 Model 1 Model 2 31 / 50

Estimation Simulations Subprime crisis Interpretation The role of the money shock on output increases in 2007 to reach a peak in 2007Q3 and another in 2008Q3. It explained around 8% of the variance in 2007Q1, reaches 11% in 2007Q3 and goes back to 7% in 2009Q4. The impact of money on the flexible price output follows about the same dynamic path. Contrary to other studies (Ireland, 2004; Andrès and al., 2006;), this result shows that money had a significant role to play during the financial crisis. It is interesting to notice that the role of the monetary policy shock is also in the same vein, but it reaches its peak in 2008Q3 (Lehman Brothers collapse). Monetary policy explains most of the inflation variance, but changes along the same lines as above with the crisis. 32 / 50

Estimation Simulations [Subprime crisis] Comparison of output and inflation DSGE forecast errors. Model 2 is better when the bar is positive, Model 1 is better otherwise. 1% Output Inflation 0.5% 0% 0.5% 1% 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 33 / 50

Estimation Simulations Subprime crisis Interpretation Measurement: the difference in the RMSD of the 2 models with respect to the actual values. The non-separable model provides the best forecasting performance for output during the subprime crisis, especially at the top of the financial crisis (2007Q4 to 2009Q4). The forecasting performance of the two models is not different as far as inflation is concerned. 34 / 50

Short run role of money on output (%) Spread EURIBOR Bubill (%) Estimation Simulations [Subprime crisis] Comparison between the role of money on output (short run variance decomposition, Model 2) and the spread Euribor-Bubill 11 2 10 1.5 9 1 8 0.5 7 0 Role of money on output (ST) Spread Euribor Bubill 6 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 0.5 35 / 50

Short run role of monetary policy on output (%) Spread Euribor Bubill (%) Estimation Simulations [Subprime crisis] Comparison between the role of monetary policy on output (short run variance decomposition, Model 2) and the spread Euribor-Bubill 30 2 20 0 Role of monetary policy on output (ST) Spread Euribor Bubill 10 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 2 36 / 50

Short run role of monetary policy on inflation (%) Spread Euribor Bubill (%) Estimation Simulations [Subprime crisis] Comparison between the role of monetary policy on inflation (short run variance decomposition, Model 2) and the spread Euribor-Bubill 50 2 45 1.5 40 Role of monetary policy on inflation (ST) Spread Euribor Bubill 1 35 0.5 30 0 25 07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 10Q2 10Q3 11Q1 0.5 37 / 50

Estimation Simulations [Money shock] Impulse response function over the three crises 0.35 Output 0.06 Output gap 0.035 Inflation 0.3 0.05 0.03 0.25 0.2 0.15 0.1 0.04 0.03 0.02 0.025 0.02 0.015 0.01 0.05 0.01 0.005 0 0 20 40 0 0 20 40 0 0 20 40 1990Q4 1992Q3 2000Q3 2002Q2 2007Q3 2008Q3 38 / 50

Estimation Simulations [Monetary policy shock] Impulse response function over the three crises 0 Output 0 Output gap 0 Inflation 0.1 0.1 0.05 0.2 0.2 0.1 0.3 0.3 0.15 0.4 0.4 0.2 0.5 0.5 0.25 0.6 0.6 0.3 0.7 1 2 3 0.7 1 2 3 0.35 1 2 3 1990Q4 1992Q3 2000Q3 2002Q2 2007Q3 2008Q3 39 / 50

Estimation Simulations [Monetary policy shock] Impulse response function over the three crises and the two models 0 Output 0 Output gap 0 Inflation 0.1 0.1 0.05 0.2 0.2 0.1 0.3 0.3 0.15 0.4 0.4 0.2 0.5 0.5 0.25 0.6 0.6 0.3 0.7 1 2 3 0.7 1 2 3 0.35 1 2 3 1992Q3 (1) 1992Q3 (2) 2002Q2 (1) 2002Q2 (2) 2008Q3 (1) 2008Q3 (2 40 / 50

Comments Further research Miscellaneous Comments We compared 2 DSGE models, one baseline model with separable preferences (Galí, 2008) and another with non-separable preferences (Benchimol and Fourçans, 2012), during 3 crisis periods: ERM crisis, Dot-com crisis and Subprime crisis. We tested the two models by using successive (rolling window) Bayesian estimations, so as to shed light on the evolution of parameters, of variance decompositions and of forecasting performances of the two models over the 3 crises.) 41 / 50

Comments Further research Miscellaneous Tests This study was also conducted by using: a preference shock instead of a price markup shock, shorter sample sizes (16, 20, 24 obs), model-based detrending methods and measurement equations, Taylor rules without money-related variable, an ad-hoc demand shock, other monetary aggregates (M1, M2). All these studies lead to similar results. 42 / 50

Comments Further research Miscellaneous Reminder (BF, 2012) Under a standard risk aversion: money plays a minor role in explaining output variability, as in the literature. Under a higher risk aversion: money plays a non-negligible role in explaining output and flexible-price output fluctuations. The explicit money variable does not appear to have a notable direct role in explaining inflation variability. Our results suggest that a nominal or real money growth variable does not improve the estimated ECB monetary policy rule. Yet, a real money gap variable significantly improves the estimated Taylor rule. One may infer that by changing economic agents perception of risks, the last financial crisis may have increased the role of money in the transmission mechanisms and in output changes. 43 / 50

Comments Further research Miscellaneous Our analysis shows that money has a significant role to play in explaining output during crises. The role of money related variables increases during these periods. Inflation does not seem to be affected directly by money variables. It is mainly explained by monetary policy, but its impact also varies during crises. During crisis periods, New Keynesian DSGE models with non-separability between consumption and real money balances should be preferred to separable models as far as macroeconomic forecasting is concerned. 44 / 50

Comments Further research Miscellaneous Further research Introduce habits (in consumption, money and leisure), capital, investment and government. Introduce micro-foundation of the central bank behavior. Risk aversion parameter and its implication in the process should be analyzed through a non-linear model and at least through second-order approximation of the overall model. Differentiate between risk aversion and uncertainty. Use other monetary aggregates measures such as Divisia Monetary Aggregates (Barnett, 1980). Just a minute, what about Israel? 45 / 50

% Comments Further research Miscellaneous Impulse response function of output with respect to a money shock for Israel Response of Output to a Money Shock 0.4 0.3 0.2 0.1 0 12Q3 10Q1 07Q3 05Q1 02Q3 Estimations 2 4 6 Quarters 8 10 46 / 50

% Comments Further research Miscellaneous On impact impulse response function of output with respect to a money shock for Israel Response of Output to a Money Shock 0.4 Model 2 0.39 0.38 0.37 0.36 0.35 0.34 0.33 0.32 0.31 0.3 02Q1 04Q1 06Q1 08Q1 10Q1 12Q1 Estimations 47 / 50

Role of money demand on output variance in the short run (%) Financial and credit conditions index Comments Further research Miscellaneous Comparison of the role of money on output variance and the financial condition index for Israel (Michelson and Suhoy, 2013). 10 4 8 2 6 0 4 01Q4 02Q2 02Q4 03Q3 04Q1 04Q3 05Q1 05Q3 06Q1 06Q3 07Q1 07Q3 08Q2 08Q4 09Q2 09Q4 10Q2 10Q4 11Q2 11Q4 12Q2 13Q1 2 48 / 50

Comments Further research Miscellaneous Distance correlations between the role of money on output and the FCI and its components Money shock s contribution to output variance FCI Bank Debt Forex Equities Resid. 0.299 0.425 0.292 0.336 0.263 0.293 Our indicator, the contribution of a money shock to output variance, is not linearly or non-linearly independent of the FCI or its components. Our indicator causes bank and debt components, at 0.89% (F-test: 7.52) and 7.80% (F-test: 3.25), respectively. Our indicator is not caused by the FCI or by one of its components. Our indicator seems to be a good predictive indicator of bank and debt risks. 49 / 50

Comments Further research Miscellaneous Thank you! More questions, remarks or ideas to improve this paper, send me an email: jonathan@benchimol.name Website: JonathanBenchimol.com 50 / 50