ahelegbey, ballesta, eskandari

8/9/2019 Ahelegbey, Ballesta, Eskandari

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Daniel Ahelegbey Blanca Ballesta

Ruhollah EskandariMONETARY POLICY.

Problem Set 1

We will examine the business cycle properties of real GDP.

We examine and compare the data for 2 monetary regimes: before the EMU and after deEMU.

Prior to our empirical analysis, we must extract the cyclical component from themacroeconomic time series. We use the hodrick prescot filter.

The Hodrick-Prescott (HP) filter estimates an unobserved time trend for time series

variables.

Let yt denote an observable (macroeconomic) time series. The HP filter decomposes yt into a nonstationary time trend (gt) and a stationary residual component (ct), that is:yt = gt + ct

After extracting the cyclical component we know focus in examining the length of thecycle in the two periods in consideration: before the EMU and afterwards.

We consider as a starting point of the EMU the 1 st January of 2001. the economic andmonetary union of the European Union is the currency union (built on a single market) of the European Union members who have adopted the euro as their sole legal tender. Full

economic and monetary union has been in effect since 1 January 2002 for twelvecountries, with further members joining since. For the European Union, economic andmonetary union (EMU) was established in three phases: coordinating economic policy,achieving economic convergence (that is, their economic cycles are broadly in step) andculminating with the adoption of the euro. (The initial participants were Belgium,Germany, Spain, France, Ireland, Italy, Luxembourg, the Netherlands, Austria, Portugal andFinland). The number of participating Member States increased to 12 on 1 January 2001,when Greece entered the third stage of EMU. Slovenia became the 13th member of theeuro area on 1 January 2007, followed one year later by Cyprus and Malta and by Slovakiaon 1 January 2009.


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1. Length of the cycle.

We take each country GDP series before 2001 and after 2001 and calculate the length of the cycle in the two periods.

The data we take is quarterly data from 1991Q1 to 2009Q4 (from EUROSTAT GDP and

main components - Current prices (namq_gdp_c)) . So we have 2 time series for eachcountry composed by 40 and 36 observations respectively.

Table 1Average Length (years) of Business Cycles under Different Monetary RegimesPeriod Austria Germany Spain Italy Belgium NetherlandsBEFOREEMU 5.25 5.25 5.75 5.25 4.75 4.5

AFTEREMU 4 3.5 5 4 4.75 3.75

Note: The Before EMU period is considered from 1991 to 2000 (last quarter) and the After EMU periodconsider data from 2001 until 2009 (last quarter).

We can see clearly that in every country the length of the cycle have been reduced (except for Belgium where remains constant). This can be explained due to the stabilization policythat the ECB has been practicing in order to get the price stability which is the main target of the ECB monetary policy. But, we cannot claim that the reduction of the length has beendue to the creation of the Eurozone, it can be due to many others factors.

So now, we pass to analyze the volatility of the cycle and of some monetary variables tosee whether the change of the monetary policy (introduced by the ECB after the EMU) hasreally affected the business cycles.

2. Volatility of the cycle

The study examined the volatility of the filtered data by comparing the volatility of thecyclical component of GDP and aggregate prices pre and post EMU. The table belowshows the result of the Volatility and Skewness of Hodrick -Prescott-Filtered GDP andPrices Pre and Post EMU.

Volatility and Skewness of Hodrick -Prescott-Filtered GDP and Prices Pre and PostEMU

Table 2 ENTIRE PERIOD

1995 - 2009

PRE EMU

1995 2000

POST EMU

2001 - 2009Std. Dev Skewness Std. Dev Skewness Std. Dev Skewness

Austria 0.00556 0.185685 0.002799 0.365313 0.003533 0.341445

Belgium 0.007114 0.027529 0.003125 0.651721 0.004447 -0.1249

Germany 0.005565 0.500893 0.00196 -0.18247 0.00453 0.203107

Italy 0.008288 -0.76138 0.005978 -0.74838 0.003459 -0.24827

Netherlands 0.009103 -0.22513 0.004399 0.46624 0.004426 0.14802


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3. Forma l Test for the D ecli i of the Vo la t ili ty ov er T im e

To con stru ct a fo r al t st of th h oth sis th at vola ti lit mea sur ed by th e va ri ance of th eHP f ilt er ed GD P decl ined ove r time, w e us ed a Seem ingly Un rela t ed Reg r essi on (SU R syst em with appl ied vola ti lit y ( in t er m s of cond iti onal va ri ance) of th e H P f ilt e red GD P as adependen t va ri able in this model.

Vola ti lit y, a s mea sur ed by th e st anda r d dev iati on of a seri e s is con st an t for a g iven sample,h ow eve r it ch ange s thr ou gh ti me. T ime-va r ying cond itional va ri ance s ar e mea sur ed by th eaut or eg r essi ve cond iti onal h et er os eda sti cit y model s.

For esti ma ti ng vola ti lit y w e follo w ed th e Han sen and Lunde (2001) th at compa r ed a la r gen u mbe r of vola ti lit y model s in t er m s of th eir ab ilit y t o de scri be th e cond iti onal va ri anceand fo u nd th at pe r for mance of this model seem s t o be be tt er th an th e o th ers .

Acco r d ing t o refe r ence pape r we us ed a Seem ingly Un r ela t ed Reg r essi on (SU R) syst emwith 8 eq u ati on s t o con stru ct a fo rmal t e st of th e h ypo th esis th at vola ti lit y mea sured byth e cond iti onal va ri ance of th e H P-f ilt eri ng GDP decl ined ove r ti me. Gene r ally a SU R

rep r esen t a ti on is

In this ca se independen t va ri able is a d u mmy va ri able th at h as th e val u e one fo r pe ri odbefo re in tr od u ction and ze ro af t er it . In this model, coeff icien t on th e d u mmy va ri able is amea sur e of th e cond iti onal va ri ance of th e b usi ne ss cycle. To t est wh eth er th e va ri ance is con st an t for eac h co u ntr y sepa r at ely and al so t est th e jo in t h ypo th esis th a t th e va ri ance is con st an t for all co u n tri es sim u lt aneo us ly, w e us ed Wald t ests . As Table?, sh ows th at this h ypo th esis can be r ejec t ed fo r eac h co u n tr y by its elf, that is , w e r ejec t con st an t va ri ancefor wh ole 8 ca ses and jo in t t est cove ri ng all co un tri es al so r ejec ts this h ypo th esis at ve ry

hi gh sign if ican t level s (99%). For mal t est r e su lts conf ir med p r ev ious r esu lts in th e t able 2ind ica ti ng th at vola ti lit y ( in t erm s of st anda r d dev iati on) inc r ea sed af t er E U for allco u n tri es excep t It aly (Decl ined) and Spa in (no c h ange).


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T able 4

Wald T ests of the Hy pothesis T hat the Conditional Variance of the CyclicalCom ponent of R eal G DP Is Constant before and after introduc tion.

Country by Country Belgium Germany Luxemburg Netherlands

Wald test 6 .80 21.90 66 .10 121. 63 P-value 0.0000 0.0000 0.0000 0.0000

Portugal Slovakia Slovenia SpainWald test 23 .84 17. 6 2 89. 6 8 2 6 .47P-value 0.0000 0.0000 0.0000 0.0000

All 8 countries

Wald test 279.2 6 P-value 0.0000

4. T he B ehavior of Economic Aggregates before and after EMU

To characterize the behavior of the economic aggregates for whole period and tow sub- periods we used an OLS regression model to study the effects of deviations from trendof economic aggregates on deviations from trend of real GDP. As the results shown inthe Tables 5, 6 and 6 we cannot state that cyclical component of money affects the real

business cycles in the analyzed countries of the Eurozone. Of course, applying an OLSmodel with this case couldnt satisfy classical hypothesis for correlation and multi-

colinearity but we followed the reference paper.

T able 5

Effects of HP-Filtered Economic Aggregates on Real GDP for Whole Period (1995Q1-2009Q4)

Variable C I X M M2 R2Durbin-Watsonstat

Coefficient 1.0584 3 1 1.0 6 9213 1.00 3 056 1.045010 0.000 3 16 0.9998 36 1.736 418Std. Error 0.018 66 0 0.0 3 13 11 0.02 3 46 8 0.028 6 45 0.0001 6 4Prob. 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0589** The parameter is statistically significant at the 10% significant level.*** The parameter is statistically significant at the 1% significant level.


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T able 6

Effects of HP-Filtered Economic Aggregates on Real GDP before EMU (1995Q1-2001Q4)

Variable C I X M M2 R2 DW

Coefficient 1.07 63 47 1.08787 3 0.992599 1.0415 66 0.000 6 47 0.999704 1.7095 63 Std. Error 0.028 3 53 0.0475 6 4 0.051920 0.051714 0.000787Prob. 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.4199

*** The parameter is statistically significant at the 1% significant level.

T able 7

Effects of HP-Filtered Economic Aggregates on Real GDP after EMU (2001Q1-2009Q4)

Variable C I X M M2 R2 DW

Coefficient 1.05 63 55 1.04844 6 0.99494 6 1.0579 66 1.0579 66 0.9998 6 9 1. 6 4106 4Std. Error 0.027772 0.049 3 24 0.0 3 1973 0.040877 0.00019 6 Prob. 0.0000*** 0.0000*** 0.0000*** 0.0000*** 0.0 66 5** The parameter is statistically significant at the 10% significant level.*** The parameter is statistically significant at the 1% significant level.

R eferences

1. B ergman, U.M., B ordo, m.d. and Jonung, L (1998). H ISTORI CAL EV ID ENCE O NB USI NE SS CYCLE S: T HE INTE R NA TIO NAL EXPE RI ENCE.

2. Hansen, P.R. and A. Lunde (2001). A comparison of volatility models: does anything beat aGARCH(1,1) model? Brown university working paper. Journal of Applied Econometrics 4:

S145S159.3 . Burns and Mitchell (194 6 ) Constant gain learning and business cycles4. Robert J. Hodrick, Eduard C. Prescott (1997) US Business Cycles: An empirical investigation.

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