Predictability of market interest rates. Panel data approach

0,0885

0,0054

0,000

DissV

0,2344

0,0482

0,0118

DissV2

0,1914

0,0309

0,0085

Thus, we can conclude that for k=0 Random Effect may be accepted for all measures of dissent, while for k=1 and 2, only Fixed Effect is possible. More detailed results description is in Appendix 9.

The next step is to determine whether the robust standard errors for fixed effects should be used by conducting tests on heteroskedasticity. The results of the Modified Wald test for groupwise heteroskedasticity in fixed effect regression model (k=1, 2) show that there is the evidence of heteroskedasticity due to zero p-values. The zero hypothesis of no heteroskedasticity is rejected (see Appendix 10). Thus, in order to get rid of this problem the robust standard errors should be used for each of six regressions (for k=1 and 2) (see Appendix 11).

The next vital test that should be used in order to find the most appropriate model is a Wald test, which compares the Fixed Effect with the pooled regression. The simpliest way to conduct this test is to provide a fixed effect regression (for k=1, 2) and look at its p-values. If the p-value < 0,01 then the zero hypothesis that the panel is pooled is rejected. All the p-values are equal to zero, thus, all the regressions tested are Fixed Effect models (see Appendix 12). In this Appendix results of the Random Effects are also shown.

In order to analyze the conclusions about the tests provided the table below summarizes the main indicators based on which the inferences would be made (t/z-statistics for the DissA, DissV and DissV2 coefficients):

	K=0 (RE)	K=1 (FE robust)	K=2 (FE robust)
	z-stat	R2	t-stat	R2	t-stat	R2
DissA	14,19	0,8626	38,82	0,7130	33,89	0,4969
DissV	12,03	0,8617	34,53	0,7087	33,92	0,48
DissV2	13,26	0,8622	34,44	0,7092	31,87	0,4837

From this table we can see that after all the tests made, for all the regressions at k=0 (when forecasts are made for the current quarter) the Random Effect model can be used. For k=1 and k=2 there may be used the Fixed Effect but with robust standard erroes in order to get rid of heteroskedasticity. The main conclusions drawn from this table say that for the panel data all types of dissents improve the fit of the regression. However, based on R2 it can be possible to choose the best type of dissent. From the table above it can be seen that for every value of k regressions with the DissA as coefficient have larger R2 levels then the other ones.

Overall, comparing the results from aggregated and disaggregated data we can conclude that both aggregated and disaggregated data show that adding the value of the DissA as the measure of the dissent into the regression may improve the predictability of market interest rates.



Conclusion

As it was explained above, the transparency of the FOMC meeting discussions, results and conclusions may serve as one of the most vital indicators, which influence the monetary policy effectiveness.

That is why, the problem of the Central Banks' transparency stands as one of the most topical when talking about the monetary policy.

In 2002 in the USA the Federal Reserve System began to publish the main conclusions of the meeting immediately after the session. This event is represented as the feature of transparency in this research. That is why the period considered in this paper is from 1987 (the moment, from which the accurate data is available) to 2002 (when the reports on the FOMC began to be published immediately after the meeting).

Thus, the hypothesis that if such information had been released before the 2002, then the predictability of market interest rates (in this research - prime rates) would have been improved is tested.

The statistical analysis was divided into two parts - using aggregated and disaggregated data. Three main types of individual policy preferences revealed during policy deliberations at the FOMC meetings were analyzed as the main dissents, which may make the forecasted values of interest rates more accurate.

After a large set of different analyses the dissent among all members of the meeting (including voting and non-voting members) was concluded to be the most appropriate one to improve the fit of the regression and to predict the prime rates. It is the only type of dissent, which value was found to be significant for the aggregated data analysis.

Although all three typres of dissent (dissent among all members, dissent among voting members only and dissent among those who will be voting at next meeting) were found to be significant during the panel data analysis, the regression with the dissent among all members as variable showed the highest value of R-sq.

Thus, the hypothesis that if the FOMC meeting results are released immediately after the event, then the predictability of market interest rates will be improved is proved by the statistical and econometrical analysis using a range of different instruments.



List of References

1. Andrei Sirchenko, “Policymakers votes and predictability of monetary policy”; European University Institute, Florence, University of California, San Diego December 30, 2010

2. Roman Horvбth, Kateшina Љmнdkovб, Jan Zбpal, «Central Banksґ Voting Records and Future Policy», Institute of Economic Studies, Prague, Barcelona, December 2012, International Journal of Central Banking

3. Alessandro Riboni (University of Montreal), Francisco J. Ruge-Murcia (University of Montreal and Rimini Centre for Economic Analysis), «Dissent in Monetary Policy Decisions», May 2011, The Rimini Centre for Economic Analysis

4. Alan S. Blinder (Princeton University), «Monetary Policy by Committee: Why and How?», December 2005, CEPS Working Paper No. 118

5. Ellen Meade (American University), «Dissents and Disagreement on the Fed's FOMC: Understanding Regional Affiliations and Limits to Transparency», March 2006, DNB Working Paper No. 94

6. Daniel L. Thornton and David C. Wheelock, «Making Sense of Dissents: A History of FOMC Dissents», 2014, Federal Reserve Bank of St. Louis Review

7. Ellen E. Meade, «The FOMC: Preferences, Voting, and Consensus», March/April 2005, Federal Reserve Bank of St. Louis Review

8. Petra Gerlach-Kristen (University of Cambridge), «Is the MPC's voting record informative about Future UK Monetary Policy?», 2004, The Scandinavian Journal of Economics

9. Petra M. Geraats (University of Cambridge), «Transparency of Monetary Policy: Theory and Practice», October 2005, CEsifo Working Paper No. 1597

10. Lynn S. Fox, Chair, Scott G. Alvarez, «The Federal Reserve System. Purposes and Functions», Ninth Edition, June 2005, Washington , Publications Fulfillment, Board of Governors of the Federal Reserve System

11. Frederic Mishkin(Columbia University),«The economics of Money, Banking, and Financial markets», 2004, the United States of America, The Addison-Wesley Series in Economics

12. Gabriel L мopez-Moctezuma (Princeton University), «Sequential Deliberation in Collective Decision-Making: The Case of the FOMC», 2005

13. Deborah J. Danker and Matthew M. Luecke, «Background on FOMC Meeting Minutes», 2005, Federal Reserve Bulletin

14. International Monetary Fund, «United States: Selected Issues», 2005, IMF Country Report No. 05/258

15. Alan Blinder (Princeton University), Charles Goodhart (London School of Economics) , Philipp Hildebrand (Vontobel Group), David Lipton (Moore Capital Strategy Group) , Charles Wyplosz (Graduate Institute of International Studies), «How Do Central Banks Talk?» , July 2001, Geneva Report on the World Economy 3

16. Oscar Torres-Reyna «Panel Data Analysis Fixed and Random Effects using Stata», 2007, Lecture of Princeton University

17. Ratnikova T. «Introduction to the econometric analysis of panel data», 2006

18. Kohler, Ulrich, Frauke Kreute «Data Analysis Using Stata, 2nd edition», 2005, Stata Press



Appendix 1

Source: Federal Reserve Bulletin



Appendix 2

monetary policy america

Source:

Daniel L. Thornton and David C. Wheelock, 2001, «Making Sense of Dissents: A History of FOMC Dissents»



Appendix 3

Source:

Data from the Federal Reserve Economic Data (FRED - St. Louis Fed) and the author's calculations



Appendix 4

Source:

Data from Andrei Sirchenko and the author's calculations



Appendix 5

Null Hypothesis: DISSA has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=13)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-8.666774	0.0000
Test critical values:	1% level		-3.468521
	5% level		-2.878212
	10% level		-2.575737
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DISSV has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=13)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-9.522976	0.0000
Test critical values:	1% level		-3.468521
	5% level		-2.878212
	10% level		-2.575737
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: DISSV2 has a unit root
Exogenous: Constant
Lag Length: 0 (Automatic - based on SIC, maxlag=13)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-9.382243	0.0000
Test critical values:	1% level		-3.468521
	5% level		-2.878212
	10% level		-2.575737
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: Y_A has a unit root
Exogenous: Constant
Lag Length: 47 (Automatic - based on SIC, maxlag=47)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-35.42118	0.0000
Test critical values:	1% level		-3.430448
	5% level		-2.861467
	10% level		-2.566772
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: YF0 has a unit root
Exogenous: Constant
Lag Length: 22 (Automatic - based on SIC, maxlag=35)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-11.02937	0.0000
Test critical values:	1% level		-3.431853
	5% level		-2.862089
	10% level		-2.567106
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: YF1 has a unit root
Exogenous: Constant
Lag Length: 9 (Automatic - based on SIC, maxlag=36)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-15.14779	0.0000
Test critical values:	1% level		-3.431321
	5% level		-2.861854
	10% level		-2.566979
*MacKinnon (1996) one-sided p-values.
Null Hypothesis: YF2 has a unit root
Exogenous: Constant
Lag Length: 9 (Automatic - based on SIC, maxlag=36)
			t-Statistic	Prob.*
Augmented Dickey-Fuller test statistic	-15.42860	0.0000
Test critical values:	1% level		-3.431299
	5% level		-2.861844
	10% level		-2.566974
*MacKinnon (1996) one-sided p-values.



Appendix 6

Calculated by Eviews

k=0

Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:15
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.043947	0.246984	0.177933	0.8590
Y_BAR	0.982699	0.029296	33.54344	0.0000
DISSA	0.515918	0.236377	2.182613	0.0304
R-squared	0.869000	Mean dependent var	8.220347
Adjusted R-squared	0.867459	S.D. dependent var	1.493268
S.E. of regression	0.543643	Akaike info criterion	1.636140
Sum squared resid	50.24306	Schwarz criterion	1.690821
Log likelihood	-138.5261	Hannan-Quinn criter.	1.658324
F-statistic	563.8535	Durbin-Watson stat	0.319583
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:15
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.047941	0.248117	0.193219	0.8470
Y_BAR	0.983874	0.029439	33.42039	0.0000
DISSV	0.511839	0.280367	1.825607	0.0697
R-squared	0.867918	Mean dependent var	8.220347
Adjusted R-squared	0.866364	S.D. dependent var	1.493268
S.E. of regression	0.545882	Akaike info criterion	1.644362
Sum squared resid	50.65785	Schwarz criterion	1.699043
Log likelihood	-139.2373	Hannan-Quinn criter.	1.666546
F-statistic	558.5407	Durbin-Watson stat	0.326126
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:16
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.036179	0.247998	0.145883	0.8842
Y_BAR	0.985203	0.029410	33.49873	0.0000
DISSV2	0.574039	0.286380	2.004468	0.0466
R-squared	0.868438	Mean dependent var	8.220347
Adjusted R-squared	0.866890	S.D. dependent var	1.493268
S.E. of regression	0.544807	Akaike info criterion	1.640417
Sum squared resid	50.45842	Schwarz criterion	1.695099
Log likelihood	-138.8961	Hannan-Quinn criter.	1.662601
F-statistic	561.0841	Durbin-Watson stat	0.323263
Prob(F-statistic)	0.000000

k=1

Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:20
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.331033	0.371410	0.891288	0.3740
Y_BAR	0.937799	0.044169	21.23214	0.0000
DISSA	0.997014	0.354685	2.810987	0.0055
R-squared	0.729397	Mean dependent var	8.152139
Adjusted R-squared	0.726214	S.D. dependent var	1.559226
S.E. of regression	0.815858	Akaike info criterion	2.448037
Sum squared resid	113.1563	Schwarz criterion	2.502719
Log likelihood	-208.7552	Hannan-Quinn criter.	2.470221
F-statistic	229.1135	Durbin-Watson stat	0.212217
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:20
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.338770	0.374658	0.904212	0.3672
Y_BAR	0.940272	0.044555	21.10352	0.0000
DISSV	0.942750	0.422311	2.232359	0.0269
R-squared	0.724884	Mean dependent var	8.152139
Adjusted R-squared	0.721648	S.D. dependent var	1.559226
S.E. of regression	0.822633	Akaike info criterion	2.464577
Sum squared resid	115.0434	Schwarz criterion	2.519258
Log likelihood	-210.1859	Hannan-Quinn criter.	2.486761
F-statistic	223.9610	Durbin-Watson stat	0.201497
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:21
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.322946	0.374718	0.861838	0.3900
Y_BAR	0.942254	0.044539	21.15565	0.0000
DISSV2	0.999219	0.431640	2.314936	0.0218
R-squared	0.725473	Mean dependent var	8.152139
Adjusted R-squared	0.722244	S.D. dependent var	1.559226
S.E. of regression	0.821752	Akaike info criterion	2.462433
Sum squared resid	114.7970	Schwarz criterion	2.517115
Log likelihood	-210.0005	Hannan-Quinn criter.	2.484617
F-statistic	224.6240	Durbin-Watson stat	0.199889
Prob(F-statistic)	0.000000

k=2

Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:24
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.877530	0.546002	1.607190	0.1099
Y_BAR	0.864890	0.065006	13.30476	0.0000
DISSV	1.233287	0.583853	2.112325	0.0361
R-squared	0.514475	Mean dependent var	8.081908
Adjusted R-squared	0.508763	S.D. dependent var	1.622854
S.E. of regression	1.137431	Akaike info criterion	3.112610
Sum squared resid	219.9372	Schwarz criterion	3.167291
Log likelihood	-266.2407	Hannan-Quinn criter.	3.134794
F-statistic	90.06838	Durbin-Watson stat	0.132500
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:23
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.853937	0.537756	1.587965	0.1142
Y_BAR	0.861667	0.064026	13.45809	0.0000
DISSA	1.519105	0.487190	3.118092	0.0021
R-squared	0.528687	Mean dependent var	8.081908
Adjusted R-squared	0.523142	S.D. dependent var	1.622854
S.E. of regression	1.120660	Akaike info criterion	3.082902
Sum squared resid	213.4995	Schwarz criterion	3.137584
Log likelihood	-263.6710	Hannan-Quinn criter.	3.105086
F-statistic	95.34725	Durbin-Watson stat	0.160301
Prob(F-statistic)	0.000000
Dependent Variable: Y
Method: Least Squares
Date: 06/09/16 Time: 13:25
Sample: 1987M09 2002M01
Included observations: 173
Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	0.858946	0.546054	1.573006	0.1176
Y_BAR	0.867242	0.064984	13.34540	0.0000
DISSV2	1.302677	0.596768	2.182886	0.0304
R-squared	0.515317	Mean dependent var	8.081908
Adjusted R-squared	0.509615	S.D. dependent var	1.622854
S.E. of regression	1.136444	Akaike info criterion	3.110874
Sum squared resid	219.5558	Schwarz criterion	3.165555
Log likelihood	-266.0906	Hannan-Quinn criter.	3.133058
F-statistic	90.37251	Durbin-Watson stat	0.131642
Prob(F-statistic)	0.000000



Appendix 7

Calculated by Eviews

K=0, DissA

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	209.0159	Prob. F(2,168)	0.0000
Obs*R-squared	123.4054	Prob. Chi-Square(2)	0.0000

K=0, DIssV

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	203.6430	Prob. F(2,168)	0.0000
Obs*R-squared	122.4790	Prob. Chi-Square(2)	0.0000

K=0, DissV2

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	205.2406	Prob. F(2,168)	0.0000
Obs*R-squared	122.7581	Prob. Chi-Square(2)	0.0000

K=1, DissA

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	382.4231	Prob. F(2,168)	0.0000
Obs*R-squared	141.8437	Prob. Chi-Square(2)	0.0000

K=1, DIssV

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	407.3868	Prob. F(2,168)	0.0000
Obs*R-squared	143.4266	Prob. Chi-Square(2)	0.0000

K=1, DissV2

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	409.3040	Prob. F(2,168)	0.0000
Obs*R-squared	143.5415	Prob. Chi-Square(2)	0.0000

K=2, DissA

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	577.2043	Prob. F(2,168)	0.0000
Obs*R-squared	151.0219	Prob. Chi-Square(2)	0.0000

K=2, DIssV

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	714.0443	Prob. F(2,168)	0.0000
Obs*R-squared	154.7905	Prob. Chi-Square(2)	0.0000

K=2, DissV2

Breusch-Godfrey Serial Correlation LM Test:
F-statistic	712.8043	Prob. F(2,168)	0.0000
Obs*R-squared	154.7621	Prob. Chi-Square(2)	0.0000



Appendix 8

Calculated by Eviews

K=0

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• Economic efficiency of grain production and its influence on a condition of the agricultural enterprise

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  курсовая работа [70,0 K], добавлен 02.07.2011
  

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Dependent Variable: Y
Method: Least Squares
Date: 06/10/16 Time: 16:33
Sample: 1987M09 2002M01
Included observations: 173