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Money Stock Control and Inflation Targeting in Germany

Money Stock Control and Inflation Targeting in Germany

A State Space Modelling Approach to the Bundesbank’s Operating Procedures and Intermediate Strategy

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Money Stock Control and Inflation Targeting in Germany

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Beschreibung

Details

Einband

Taschenbuch

Erscheinungsdatum

27.03.2001

Verlag

Physica

Seitenzahl

174

Maße (L/B/H)

23.5/15.5/1.1 cm

Beschreibung

Details

Einband

Taschenbuch

Erscheinungsdatum

27.03.2001

Verlag

Physica

Seitenzahl

174

Maße (L/B/H)

23.5/15.5/1.1 cm

Gewicht

290 g

Auflage

Softcover reprint of the original 1st ed. 2001

Sprache

Englisch

ISBN

978-3-7908-1393-7

Weitere Bände von Contributions to Economics

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  • Money Stock Control and Inflation Targeting in Germany
  • 1. Introduction.- 1.1 Intermediate strategies for monetary policy.- 1.2 The role of the Bundesbank.- 1.3 The methodological framework.- 2. Measuring monetary policy: Operating procedures and intermediate strategies.- 2.1 The conduct of monetary policy in Germany.- 2.1.1 The practice of monetary targeting.- 2.1.2 The development of instruments for monetary control.- 2.2 Operating procedures and money supply.- 2.3 A note on the identifiability of money demand.- 2.3.1 The identification problem.- 2.3.2 Money demand and monetary policy rules.- 2.3.3 Tracing the supply side using bank reserves?.- 2.4 A note on the identifiability of autonomous monetary policy actions.- 2.4.1 Common identification problems in VARs.- 2.4.2 Identifying monetary policy regimes.- 2.4.3 Narrative approaches.- 2.4.4 Unresolved issues.- 2.5 Evidence from recent empirical analyses of German monetary policy.- 2.5.1 Was there no role for monetary aggregates in Germany?.- 2.5.2 Did the Bundesbank pursue a Lombard rate regime?.- 3. Theoretical framework.- 3.1 Basic considerations.- 3.2 Interest rate operating procedure (from 1985 on).- 3.3 An alternative approach: reserves targeting.- 3.4 Discount window borrowing procedure (1975–1984).- 3.5 Concluding remarks.- 4. Analysis of time series using linear state space models.- 4.1 Basic concepts.- 4.1.1 Common approaches to multivariate time series analysis.- 4.1.2 The system theoretic approach.- 4.1.3 Filtering and smoothing.- 4.2 The linear state space model.- 4.3 Specific properties of the linear model.- 4.3.1 Innovation state space representation.- 4.3.2 Linear state space models and time series models.- 4.3.3 Minimality.- 4.4 Signal extraction using the Kalman filter.- 4.4.1 Filtered forecasts.- 4.4.2 Smoothed forecasts.- 4.4.3 The use of the Kalman filter in econometrics.- 4.5 Structural analysis.- 4.5.1 Impulse—responses to orthogonal innovations.- 4.5.2 Impulse—responses to non—orthogonal innovations using a Wold—ordering scheme.- 4.5.3 Impulse-responses to non—orthogonal innovations: Beyond the Wold—ordering scheme.- 4.5.4 The forecast error variance decomposition.- 4.5.5 Distribution of impulse—response functions.- 4.6 Model selection and estimation.- 4.6.1 The model building algorithm.- 4.6.2 Salient features of the algorithm as compared with VARs.- 4.6.3 Model specification tests.- 4.7 Modelling nonstationary time series and common stochastic trends.- 4.7.1 Detrending and common trend representation in econometrics.- 4.7.2 Modelling trend and cycle.- 4.7.3 A useful generalisation for cointegrated time series.- 4.7.4 Properties of the decomposition into permanent and transitory components.- 5. Empirical implementation and results.- 5.1 Is a multiplier approach adequate to model German money supply?.- 5.1.1 Data.- 5.1.2 Results.- 5.2 Monetary targeting and the business cycle.- 5.2.1 Data.- 5.2.2 Results.- 5.3 A quantity theory based approach to inflation targeting.- 5.3.1 Money stock control and inflation targeting.- 5.3.2 The P* approach.- 5.3.3 Data and results.- 6. Conclusions.- 6.1 Methodological remarks.- 6.2 Money stock control and the day—to—day conduct of monetary policy.- 6.3 Money stock control and inflation targeting in Germany.- 6.4 Prospects for the ECB’s monetary policy strategy.- List of figures.- List of tables.- A. GAUSS—programs.- A.1 Estimation of balanced state space model.- A.2 Obtaining in—sample forecasts using the Kalman filter.- A.3 Calculation of impulse—responses.- A.4 Calculation of forecast—error variance decompositions.- A.6 Aoki C-Test for model specification.