# «MICHAEL W. MADSEN THESIS FOR THE DEGREE MASTER OF ECONOMIC THEORY AND ECONOMETRICS DEPARTMENT OF ECONOMICS UNIVERSITY OF OSLO MAY 2012 © Michael W. ...»

Below are the graphs showing the recursive graphics. They present estimated time-path results when running the regression with different lead lengths for the financial stress index.

34 Figure 6: Recursive graphics, panels a- c. The plots show the time paths of the coefficients in the Taylor-type rule. In each panel, the top left graph is inflation, top right graph is the output gap, the bottom left graph is the exchange rate and the bottom right graph is the financial stress index.

35 Figure 7: Recursive graphics, panels d- f. The plots show the time paths of the coefficients in the Taylor-type rule. In each panel, the top left graph is inflation, top right graph is the output gap, the bottom left graph is the exchange rate and the bottom right graph is the financial stress index.

36 Once more, all the coefficients are somewhat unstable in the early parts of the sample, for all different leads and lags for the financial stress index. However, the plots suggest that the time-paths of the coefficients become more stable after the initial unstable phase, with the exception of a sudden drop in the coefficient measuring financial instability’s impact on the interest setting decisions of the monetary authority.

**Time- varying coefficients estimation**

In estimating the model using the varying coefficients method, I will assume that most of the coefficients are time-invariant. As the plots above suggest convergence toward a steady-state for all parameters but the one measuring the impact of financial stress on the interest rate, I assume that all coefficients but the latter are time-invariant. Furthermore, I will utilize the

**specification given by eqs. (18) – (19), namely:**

(18) (19) The time-varying results for the financial stress index coefficient are presented below, for different leads and lags imposed on the financial stress index.

In the time-varying plots in Figure 8 above, it is evident that the occurrence of financial distress in late 2008 led the central bank to decrease the interest rate. Specifically, for all specifications of leads and lags on the financial stress index, in late 2008, the monetary authority responded by cutting its interest rate by at least 0.25 basis points per unit increase in the stress index. The largest cut is seen when the model is estimated under the assumption that the central bank was rather sluggish in its response, which can be seen looking at the plots for δ_-1, δ_-2, and δ_-3 in Table 8 above. In particular, the plot named δ_-3 in the figure above show that three months after the initial shock, the interest rate was reduced by more than 0.7 basis points by the monetary authority, either by lowering its policy rates, through open market operations, or both, ultimately resulting in a drop in the interbank rate. As the stress index increased by more than three units at its peak, this result means that the reduction in the interest rates were roughly 2.1 basis points three months after the increase in the stress index.

It can also be seen from the figure above that the expansionary effect on monetary policy decreases as the time elapsed since the financial stress occurred tends to zero. Additionally, 38 the evidence from the figures suggests that there is a double-dip in the central bank’s response to the financial distress in 2008, except for in the specifications where the central bank responds three periods after the initial shock and two periods prior to an expected shock. This suggests that the initial slashing of the interest rates was not enough, and that a second cut in the interest rates was needed.

Moreover, it can also be seen, by conferring the plots dubbed δ_2 and δ_1, that the interest rate was increased in response to an increase in financial stress in 2003. These results are especially evident when assuming that the central bank adjusts its interest rate when there is an expected increase in financial instability. That is, plot δ_2 shows that there is a monetary contraction when financial stress is expected to increase two periods ahead. The increase in the interest rate as a result of an increase in the financial stress index is, however, at its peak in the case where it is assumed that the central bank adjusts the interest rate to an expected increase in the financial stress index one period ahead. As an increase in the stress index might lead to a contraction in the real economy, through higher unemployment and lower inflation, an increase in the interest rate may act so as to propagate a possible economic downturn.

39 6 Conclusion In this thesis, I have looked at the impact of increased financial stress on monetary policy in Norway. Periods of financial instability affect the real economy through their negative impact on the supply of credit. A situation with credit rationing tends to slow down the economy.

Hence, shocks to the economy originated in the financial sector might spread to the real economy and thereby cause deflationary pressure and setbacks in production. For that reason, I found it interesting to investigate the Norwegian Central bank’s response to such shocks.

The thesis sought to find an answer to the size and timing of the central bank’s response, i.e., by how much it reduced its interest rates and if it responded before or after the shock had occurred.

By estimating a Taylor-type rule in a model framework where the parameter measuring the effect of financial stress on interest rates was time-varying, the effect of an increase in the financial stress index was found. The augmented Taylor rule was also estimated using the two-stage least squares method. The interest rate estimated was the three month interbank rate, which also captures open market operations executed by the central bank, thus covering more of the set of instruments the monetary authority can utilize.

The results showed that there was a negative impact of a shock to the financial sector on the interest rates. That is, the estimation results implied that an increase in the financial stress index caused the central bank to decrease its interest rates. Moreover, by applying the timevarying coefficients method to the model, it was clear that the past financial crisis caused the central bank to be more expansionary in its policy than usual towards financial stress. The findings also suggested that the monetary authority reacted more on the increase in financial instability ex post than ex ante. That is, there seemed to be a more aggressive reaction towards financial stress after the impact rather than a reaction towards an expected increase in the stress index.

40 References Akram, Q. F., & Eitrheim, Ø. (2008). Flexible Inflation Targeting and Financial Stability: Is it Enough to Stabilize Inflation and Output? Journal of Banking & Finance, 32, 1242Akram, Q. F., Bårdsen, G., & Lindquist, K.-G. (2007). Persuing Financial Stability Under an Inflation-Targeting Regime. Annals of Finance, 3, 131-153.

Baxa, J., Horváth, R., & Vašíček, B. (2011). Time-Varying Monetary-Policy Rules and Financial Stress: Does Financial Instability Matter for Monetary Policy? Journal of Financial Stability.

Bernanke, B., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.

Bernanke, B., & Gertler, M. (1999). Monetary Policy and Asset Price Volatility. Federal Reserve Bank of Kansas City Economic Review, 17-52.

Bernanke, B., & Gertler, M. (2001). Should Central Banks Respond to Movements in Asset Prices. American Economic Review, 91(2), 253-257.

Bernanke, B., Gertler, M., & Gilchrist, S. (1999). The Financial Accelerator in a Quantitative Business Cycle Framework. In J. B. Taylor, & M. Woodford (Eds.), Handbook of Macroeconomics. Amsterdam, North- Holland.

Borio, C., & Lowe, P. (2004). Securing Sustainable Price Stability: Should Credit Growth Come Back From the Wilderness? BIS Working Paper no. 157.

Bulíř, A., & Čihák, M. (2008). Central Bankers’ Dilemma When Banks Are Vulnerable: To Tighten or not to Tighten? IMF Mimeo.

Cardarelli, R., Elekdag, S., & Lall, S. (2011). Financial Stress, Downturns, and Recoveries.

Journal of Financial Stability, 7, 78-97.

Cecchetti, G., S., & Li, L. (2008). Do Capital Adequacy Requirements Matter for Monetary Policy? Economic Inquiry, Western Economic Association International, 46(4).

Chadha, J. S., Sarno, L., & Valente, G. (2004). Monetary Policy Rules, Asset Prices and Exchange Rates. IMF Staff Papers, 51(3).

Christiano, L., Ilut, C., Motto, R., & Rostagno, M. (2008). Monetary Policy and Stock Market Boom-Bust Cycles. ECB Working Paper no. 955.

Clarida, R., Galí, J., & Gertler, M. (1998). Monetary policy rules in practice: Some international evidence. European Economic Review, 42, 1033-1067.

de Andrade, J. P., & Divino, J. A. (2005). Monetary Policy of the Bank of Japan- Inflation Target versus Exchange Rate Target. Japan and the World Economy, 17, 189-208.

Goodfriend, M. (1991). Interest Rates and the Conduct of Monetary Policy. CarnegieRochester Conference Series on Public Policy, 32, 7- 30.

Kim, C.-J. (2006). Time-Varying Parameter Models with Endogenous Regressors. Economics Letters, 91, 21-26.

**Kim, C.-J., & Nelson, C. R. (2006). Estimation of a Forward-Looking Monetary Policy Rule:**

A Time-Varying Parameter Model Using ex-post Data. Journal of Monetary Economics, 53, 1949-1966.

Mishkin, F. (2009). Is Monetary Policy Effective During Financial Crises? American Economic Review, 99(2), 573-577.

Norges Bank (2006). http://www.norges-bank.no/en/about/mandate-and-coreresponsibilities/. Retrieved January 19, 2012 Norges Bank (2008). http://www.norges-bank.no/en/about/financial-turbulence-and-norgesbank/steps-taken-by-norges-bank/. Retrieved January 19, 2012 Norges Bank (2011). http://www.norges-bank.no/en/about/published/press-releases/2011/keyrate-14-december/. Retrieved January 19, 2012 Norges Bank (2012). Monetary Policy Report 1/12.

Rigobon, R., & Sack, B. (2003). Measuring the Reaction of Monetary Policy to the Stock Market. The Quarterly Journal of Economics, 118(2), 639- 669.

Schlicht, E. (1981). A Seasonal Adjustment Principle and a Seasonal Adjustment Method Derived from this Principle. Journal of the American Statistical Association, 76(374), 374-378.

Schlicht, E. (2005). Estimating the Smoothing Parameter in the so-called Hodrick-Prescott Filter. Journal of the Japan Statistical Society, 35(1), 99-119.

Schlicht, E., & Ludsteck, J. (2006). Variance Estimation in a Random Coefficients Model.

IZA Discussion Paper No. 2031.

**Siklos, P. L., & Bohl, M. T. (2008). Asset Prices as Indicators of Euro Area Monetary Policy:**

An Empirical Assessment of Their Role in a Taylor Rule. Open Economics Review, 20(1), 39- 59.

42 Trecroci, C., & Vassalli, M. (2010). Monetary Policy Regime Shifts: New Evidence from Time-Varying Interest Rate Rules. Economic Inquiry, 48(4), 933-950.

Valente, G. (2003). Monetary Policy Rules and Regime Shifts. Applied Financial Economics, 13, 525-535.

A.1 Tables Table A 1: Results from two-stage least squares estimation, with a lead of 2 periods on the financial stress index, estimating using the output gap constructed from production data.

44 Table A 2: Results from two-stage least squares estimation, with a lead of 0 periods on the financial stress index, estimating using the output gap constructed from production data.

46 Table A 4: Results from two-stage least squares estimation, with a lag of 3 periods on the financial stress index, estimating using the output gap constructed from production data

48 Table A 6: Results from two-stage least squares estimation, with a lead of 0 periods on the financial stress index, estimating using the output gap proxied by the deviation of the unemployment rate from the natural rate of unemployment.

50 Table A 8: Results from two-stage least squares estimation, with a lead of 2 periods on the financial stress index, estimating using the output gap proxied by the deviation of the unemployment rate from the natural rate of unemployment.

Figure A 1: Time-paths for all lags and leads on the financial stress index, where δ_x is the time-path of the financial stress coefficient when the financial stress index is led x periods, and δ_-x when the stress index is lagged x periods. The effect is estimated using the output gap constructed from production data.

52 Figure A 2: Time-paths for all lags and leads on the financial stress index, where δ_x is the time-path of the financial stress coefficient when the financial stress index is led x periods, and δ_-x when the stress index is lagged x periods. The effect is estimated using the output gap constructed from production data.

53 Figure A 3: Time-paths for all lags and leads on the financial stress index, where δ_x is the time-path of the financial stress coefficient when the financial stress index is led x periods, and δ_-x when the stress index is lagged x periods. The effect is estimated using the output gap constructed from production data.

54 Figure A 4: Time-paths for all lags and leads on the financial stress index, where δ_x is the time-path of the financial stress coefficient when the financial stress index is led x periods, and δ_-x when the stress index is lagged x periods. The effect is estimated using the output gap proxied by the deviation of the unemployment rate from the natural rate of unemployment.

56 Figure A 6: Time-paths for all lags and leads on the financial stress index, where δ_x is the time-path of the financial stress coefficient when the financial stress index is led x periods, and δ_-x when the stress index is lagged x periods. The effect is estimated using the output gap proxied by the deviation of the unemployment rate from the natural rate of unemployment.

57