The Relativity Theory Revisited: Is Publishing Interest Rate Forecasts Really so Valuable? pptx

Warsaw, November 2008 The Relativity Theory Revisited: Is Publishing Interest Rate Forecasts Really so Valuable? 0LFKDï%U]R]D%U]H]LQD Adam Kot NATIONAL BANK OF POLAND WORKING PAPER No. 52 Design: Oliwka s.c. 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4 5Conclusions 17 References 18 Appendix 1 21 Appendix 2 23 /LVWRIWDEOHVDQGILJXUHV National Bank of Poland 4 /LVWRIWDEOHVDQGILJXUHV Table 1 ,QIRUPDWLRQVWUXFWXUHDSSOLHGLQWKHPRGHO  Table 2 %DVHOLQHVFHQDULRSDUDPHWUL]DWLRQ  Table 3 Average central bank loss and gains in the baseline scenario  Table 4 7D\ORUUXOHFRHIILFLHQWVLQWKHEDVHOLQHVFHQDULR  Figure 1 $YHUDJHFHQWUDOEDQNORVVIRUGLIIHUHQWYDOXHVRIhbea under various monetary policy rules  Figure 2 $YHUDJHFHQWUDOEDQNORVVIRUGLIIHUHQWYDOXHVRIe under various monetary policy rules  Figure 3 $YHUDJHFHQWUDOEDQNORVVIRUGLIIHUHQWYDOXHVRI S Z^ under various monetary policy rules   Abstract :25.,1*3$3(51R 5 Abstract ,QD1HZ.H\QHVLDQPRGHOZLWKDV\PPHWULFLQIRUPDWLRQDQGOHDUQLQJZHVKRZWKDW SXEOLFDWLRQRIPDFURHFRQRPLFSURMHFWLRQVDQGRIWKHIXWXUHLQWHUHVWUDWHSDWKE\WKH FHQWUDOEDQNFDQLPSURYHPDFURHFRQRPLFRXWFRPHV+RZHYHUWKHJDLQVIURPSXEOLVKLQJ 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Keynesian Models with Inertia Central bank transparency Forecasts 18 N a t i o n a l B a n k o f P o l a n d Decision on Interest Rates The Expected Interest Rate Path: Alignment of Expectations vs Creative Opacity transparency? Social... coefficients in the baseline scenario Case Standard Taylor rule STR rule optimized for RE (variant V3) RETR optimized rule 4 Loss nick =1.5 =1.5 =1.5 =1.16 =1.16 =1.16 =1.17 =1.16 OTR used by agents rises the central bank loss tends to be lower This is in line with the intuition and remains substantially and the remaining gains that can be achieved by additionally showing the interest rate path or period... curve Fraction : projection (additional data for estimation) IS equation Philips curve policy rule IS equation Philips curve policy rule 13 4 4.1 Simulations and 8 periods ahead since central bank loss in period is computed as: 4 =1 2 + 2 + + + 1 2 = +1 is [ and is the argmin ]= the baseline scenario it is assumed that the learning sample smpl to the perpetual learning gain smpl 4.2 Results 8 = 14 N a... a t i o n a l B a n k o f P o l a n d The third variant timing assumptions as in and As in to 3 as corresponding to publishing to as corresponding to additionally showing an interest rate path Table 1 Information structure applied in the model central bank knows: public knows: IS equation Philips curve policy rule – IS equation Philips curve policy rule ): IS equation, Philips curve Fraction : projection...3 is assumed to be conditional on what the central bank reveals 3 1 A 1 1 agents and 1 A 1 1 and +1 +1 A +1 and shocks and at periods and and arrive this can be used to generate The second variant and in that economic agents are assumed to 11 we consider two alternative solutions: and 3 and and 1 1 and 1 +1 1 +1 1 +1 1 and solution is used to obtain and where 12 N... scenario parametrization =80 =0.8 =4 versus variant where denotes average central bank vs vs This points to 4 can end up with disappointing along the central bank communication channel as support to other transmission channels To or was altered The vs 15 Table 3 Average central bank loss and gains in the baseline scenario Case Loss nick Gain (in per cent) vs vs Standard Taylor rule STR 95.85 82.83... Interest Rate Path: Alignment of Expectations vs Creative Opacity transparency? Social value of public information Knowledge Does the Federal Reserve Have an Interest Rates April Discretion versus Policy Rules in Practice Optimal Economic Transparency 19 interest rates? Interest and Prices 20 N a t i o n a l B a n k o f P o l a n d Figure 1 Average central bank loss for different values of STR 110... V3 N a t i o n a l B a n k o f P o l a n d 1 +1 1 +1 1 0 1 +1 1 1 where: • is the state vector: =[ and with and interest rate in period • • =[ =[ 1 1 2 1 1 1 where 1 2 where 2 1 1 1 2 1 1 1 where 1 where 2 where 1 2 1 1 • and the matrices +1 +2 +1 +2 +1 and 0 1 1 2 2 1 0 23 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 2 1 2 1 solved using his algorithm 24 N a t i o n a l B a n k o f P o l a n d . 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