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  • 1
    Online Resource
    Online Resource
    Understanding DSGE Filters in Forecasting and Policy Analysis (2013)
    Washington, D.C : International Monetary Fund
    Details
    UID:
    gbv_845818708
    Format: Online-Ressource (23 p)
    Edition: Online-Ausg.
    ISBN: 1484301358 , 9781484301357
    Series Statement: IMF Working Papers Working Paper No. 13/98
    Content: This paper introduces methods that allow analysts to (i) decompose the estimates of unobserved quantities into observed data, (ii) to better understand revision properties of the model, and (iii) to impose subjective prior constraints on path estimates of unobserved shocks in structural economic models. For instance, a decomposition of the flexible-price output gap, or a technology shock, into contributions of output, inflation, interest rates, and other observed variables' contribution is feasible. The intuitive nature and analytical clarity of the suggested procedures are appealing for policy-related and forecasting models
    Additional Edition: Erscheint auch als Druck-Ausgabe Andrle, Michal Understanding DSGE Filters in Forecasting and Policy Analysis Washington, D.C. : International Monetary Fund, 2013 ISBN 9781484301357
    Language: English
    DOI: 10.5089/9781484301357.001
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  • 2
    Online Resource
    Online Resource
    Forecasting and Monetary Policy Analysis in Low-Income Countries : Food and non-Food Inflation in Kenya (2013)
    Washington, D.C : International Monetary Fund
    Details
    UID:
    gbv_84581950X
    Format: Online-Ressource (63 p)
    Edition: Online-Ausg.
    ISBN: 1475537441 , 9781475537444
    Series Statement: IMF Working Papers Working Paper No. 13/61
    Content: We develop a semi-structural new-Keynesian open-economy model, with separate food and non-food inflation dynamics, for forecasting and monetary policy analysis in low-income countries and apply it to Kenya. We use the model to run several policy-relevant exercises. First, we filter international and Kenyan data (on output, inflation and its components, exchange rates and interest rates) to recover a model-based decomposition of most variables into trends (or potential values) and temporary movements (or gaps)—including for the international and domestic relative price of food. Second, we use the filtration exercise to recover the sequence of domestic and foreign macroeconomic shocks that account for business cycle dynamics in Kenya over the last few years, with a special emphasis on the various factors (international food prices, monetary policy) driving inflation. Third, we perform an out-of-sample forecast to identify where the economy—and therefore policy—was likely headed given the inflationary pressures at the end of our sample (2011Q2). We find that while imported food price shocks have been an important source of inflation, both in 2008 and more recently, accommodating monetary policy has also played a role, most notably through its effect on the nominal exchange rate. The model correctly predicted that a policy tightening was required, although the actual interest rate increase was larger. We discuss implications for the use of model-based policy analysis in low income countries
    Additional Edition: Erscheint auch als Druck-Ausgabe Andrle, Michal Forecasting and Monetary Policy Analysis in Low-Income Countries: Food and non-Food Inflation in Kenya Washington, D.C. : International Monetary Fund, 2013 ISBN 9781475537444
    Language: English
    DOI: 10.5089/9781475537444.001
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  • 3
    Online Resource
    Online Resource
    What is in your output gap? : unified framework & decomposition into observables (2013)
    Washington, D.C. :International Monetary Fund,
    Details
    UID:
    edocfu_9958090041502883
    Format: 1 online resource (46 p.)
    ISBN: 1-4843-0333-4 , 1-4843-6051-6 , 1-4843-6634-4
    Series Statement: IMF working paper ; WP/13/ ;
    Content: This paper discusses several popular methods to estimate the ‘output gap’. It provides a unified, natural concept for the analysis, and demonstrates how to decompose the output gap into contributions of observed data on output, inflation, unemployment, and other variables. A simple bar-chart of contributing factors, in the case of multi-variable methods, sharpens the intuition behind the estimates and ultimately shows ‘what is in your output gap.’ The paper demonstrates how to interpret effects of data revisions and new data releases for output gap estimates (news effects) and how to obtain more insight into real-time properties of estimators.
    Note: Description based upon print version of record. , Cover; Contents; I. Introduction; II. Methods; A. Formulating Potential Output Estimates as Linear Filters; 1. State-Space Forms - Semi-structural and DSGE Models; 2. Univariate filters - Band-Pass, Hodrick-Prescott, etc.; 3. Structural VARs; 4. Production Function Approach; B. Analyzing Revision Properties - Data Revisions and News Effects; 1. Decomposing Effects of Data Revision; 2. News Effects and End-Point Bias; III. Applications - Decomposition into Observables & News Effects; A. Variants of Hodrick-Prescott/Leser Filter and Local Linear Trend Models , B. Output Gap Estimation using a Multivariate Semi-Structural FilterFigures; 1. HP filter vs. Modified HP filter - estimate & weights; 1. Decomposition into Observables; 2. Output-Gap Observable Decomposition of Benes et al. (2010) model; 3. Transfer function gains, Beneš et al. (2010) model; 4. Univariate approximation using unemployment; 5. News Effects Decomposition; 6. Output-Gap Estimates from the Beneš et al. (2010) model; 2. News Effects Decomposition; C. Natural Output Gap in a DSGE Model; 7. Output Gap Observable Decomposition from a DSGE Model; IV. Conclusions; References , AppendicesA. Parameter Estiates from Beneš et al. (2010); Tables; 1. Estimated parameters for Beneš et al. (2010); B. Not for publication: Difference between two representations of the HP filter; C. Example: Simple Multivariate Filter - Three Representations; D. Not for publication: Variance Reduction via Common Component and Multiple Measures; 8. Weights of AR(1) model; E. Not for publication: Additional Graphs; 9. HP filter transfer function; 10. Output, unemployment (+1Q) and cap. utilization detrended , English
    Additional Edition: ISBN 1-4843-9955-2
    Additional Edition: ISBN 1-299-66163-7
    Language: English
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  • 4
    Online Resource
    Online Resource
    Adding Indonesia to the global projection model / (2009)
    [Washington, D.C.] :International Monetary Fund, Research Dept.,
    Details
    UID:
    edocfu_9958078122802883
    Format: 57 p. : , ill.
    Edition: 1st ed.
    ISBN: 1-4623-8703-9 , 1-4527-5207-9 , 1-282-84491-1 , 9786612844911 , 1-4519-4171-4
    Series Statement: IMF working paper ; WP/09/253
    Content: This is the fifth of a series of papers that are being written as part of a larger project to estimate a small quarterly Global Projection Model (GPM). The GPM project is designed to improve the toolkit to which economists have access for studying both own-country and cross-country linkages. In this paper, we add Indonesia to a previously estimated small quarterly projection model of the US, euro area, and Japanese economies. The model is estimated with Bayesian techniques, which provide a very efficient way of imposing restrictions to produce both plausible dynamics and sensible forecasting properties.
    Note: "November 2009." , Intro -- Contents -- I. Introduction -- A. Background -- B. A Brief Outline of Indonesian Economic Developments Over The Sample Period -- II. Benchmark Model -- A. Background -- B. The Specification of The Model -- B.1 Observable variables and data definitions -- B.2 Stochastic processes and model definitions -- B.3 Behavorial equations -- B.4 Cross correlations of disturbances -- III. Extending the Model to Include Financial-Real Linkages -- A. Background -- B. Model Specication Incorporating the US Bank Lending TighteningVariable -- V. Confronting the Model with the Data -- A. Bayesian Estimation -- B. Results -- B.1 Estimates of coeficients -- B.2 Estimates of standard deviation of structural shocks and cross correlations -- B.3 RMSEs -- B.4 Impulse response functions -- VI. Concluding Remarks -- IV. Modifications of the Model for the Indonesian Economy -- References -- Appendix: GPM Data Definitions -- Figures -- 1. Indonesia - Historical Data [1] -- 2. Indonesia - Historical Data [2] -- 3. Indonesia - Historical Data [3] -- 4. Comparison CDS Emerging Countries -- 5. Indonesia Historical Inflation Graph -- 6. Domestic Demand Shock -- 7. Domestic Price Shock -- 8. Domestic Interest Rate Shock -- 9. Domestic Real Exchange Rate Shock -- 10. Shock to the Domestic Target Rate of Inflation -- 11. Demand Shock in the US -- 12. BLT Shock in the US -- Tables -- 1. Results from Posterior Maximization -- 2. Results from Posterior Parameters (standard deviation of structural shocks) -- 3. Results from Posterior Parameters (correlation of structural shocks) -- 4. Root Mean Squared Errors. , English
    Additional Edition: ISBN 1-4519-1814-3
    Language: English
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  • 5
    Online Resource
    Online Resource
    Output and Inflation Co-movement : An Update on Business-Cycle Stylized Facts (2016)
    Washington, D.C. :International Monetary Fund,
    Details
    UID:
    almafu_9959402884902883
    Format: 1 online resource (38 pages) : , illustrations (some color), graphs, tables.
    ISBN: 1-4755-6916-5 , 1-4755-6919-X
    Series Statement: IMF Working Papers
    Content: What are the drivers of business cycle fluctuations? And how many are there? By documenting strong and predictable co-movement of real variables during the business cycle in a sample of advanced economies, we argue that most business cycle fluctuations are driven by one major factor. The positive co-movement of real output and inflation convincingly argues for a demand story. We propose a simple statistic that can compare data and models. Based on this statistic, we show that the recent vintage of structural economic models has difficulties replicating the stylized facts we document.
    Additional Edition: ISBN 1-4755-6032-X
    Language: English
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  • 6
    Online Resource
    Online Resource
    A Model-Based Analysis of Spillovers : The Case of Poland and the Euro Area (2014)
    Washington, D.C : International Monetary Fund
    Details
    UID:
    gbv_845954385
    Format: Online-Ressource (50 p)
    Edition: Online-Ausg.
    ISBN: 1498381812 , 9781498381819
    Series Statement: IMF Working Papers Working Paper No. 14/186
    Content: This paper studies economic and financial spillovers from the euro area to Poland in a two-country semi-structural model. The model incorporates various channels of macrofinancial linkages and cross-border spillovers. We parameterize the model through an extensive calibration process, and provide a wide range of model properties and evaluation exercises. Simulation results suggest a prominent role of foreign demand shocks (euro area and global) in driving Poland’s output, inflation and interest rate dynamics, particularly in recent years. Our model also has the capability for medium-term conditional forecasting and policy analysis
    Additional Edition: Erscheint auch als Druck-Ausgabe Andrle, Michal A Model-Based Analysis of Spillovers: The Case of Poland and the Euro Area Washington, D.C. : International Monetary Fund, 2014 ISBN 9781498381819
    Language: English
    DOI: 10.5089/9781498381819.001
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  • 7
    Online Resource
    Online Resource
    What Is in Your Output Gap? : Unified Framework & Decomposition into Observables (2013)
    Washington, D.C : International Monetary Fund
    Details
    UID:
    gbv_845818414
    Format: Online-Ressource (45 p)
    Edition: Online-Ausg.
    ISBN: 1484399552 , 9781484399552
    Series Statement: IMF Working Papers Working Paper No. 13/105
    Content: This paper discusses several popular methods to estimate the ‘output gap’. It provides a unified, natural concept for the analysis, and demonstrates how to decompose the output gap into contributions of observed data on output, inflation, unemployment, and other variables. A simple bar-chart of contributing factors, in the case of multi-variable methods, sharpens the intuition behind the estimates and ultimately shows ‘what is in your output gap.’ The paper demonstrates how to interpret effects of data revisions and new data releases for output gap estimates (news effects) and how to obtain more insight into real-time properties of estimators
    Additional Edition: Erscheint auch als Druck-Ausgabe Andrle, Michal What Is in Your Output Gap? Unified Framework & Decomposition into Observables Washington, D.C. : International Monetary Fund, 2013 ISBN 9781484399552
    Language: English
    DOI: 10.5089/9781484399552.001
    URL: kostenfrei
    URL: kostenfrei
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  • 8
    Online Resource
    Online Resource
    Reforming Fiscal Governance in the European Union (2015)
    Washington, D.C : International Monetary Fund
    Details
    UID:
    gbv_845960644
    Format: Online-Ressource (28 p)
    Edition: Online-Ausg.
    ISBN: 1498338283 , 9781498338288
    Series Statement: IMF Staff Discussion Notes: Staff Discussion Notes No. 15 / 9
    Content: Successive reforms have brought many positive elements to the European Union’s fiscal framework. But they have also increased its complexity. The current system involves an intricate set of fiscal constraints, which hampers effective monitoring and public communication. Compliance has also been weak. This note discusses medium-term reform options to simplify the framework and improve compliance. Based on model simulations and practical considerations, it argues for moving to a two-pillar approach, with a single fiscal anchor (public debt-to-GDP) and a single operational target (an expenditure growth rule, possibly with an explicit debt correction mechanism) linked to the anchor
    Language: English
    DOI: 10.5089/9781498338288.006
    URL: kostenfrei
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  • 9
    Online Resource
    Online Resource
    Adding Indonesia to the global projection model (2009)
    [Washington, D.C.] :International Monetary Fund, Research Dept.,
    Details
    UID:
    almahu_9948319763302882
    Format: 57 p. : , ill.
    Edition: Electronic reproduction. Ann Arbor, MI : ProQuest, 2015. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.
    Series Statement: IMF working paper ; WP/09/253
    Note: "November 2009."
    Language: English
    Keywords: Electronic books.
    URL: Click to View
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    HU Berlin
  • 10
    Online Resource
    Online Resource
    Flexible System of Global Models - FSGM / (2015)
    Washington, D.C. :International Monetary Fund,
    Details
    UID:
    edoccha_9958057032802883
    Format: 1 online resource (102 p.)
    Edition: First edition.
    ISBN: 1-4755-9333-3 , 1-4755-4749-8
    Series Statement: IMF Working Papers ; Volume Working Paper No. 15/64
    Content: The Flexible System of Global Models (FSGM) is a group of models developed by the Economic Modeling Division of the IMF for policy analysis. A typical module of FSGM is a multi-region, forward-looking semi-structural global model consisting of 24 regions. Using the three core modules focused on the G-20, the euro area, and emerging market economies, this paper outlines the theory under-pinning the model, and illustrates its macroeconomic properties by presenting its responses under a wide range of experiments, including monetary, financial, demand, supply, fiscal and international shocks.
    Note: Description based upon print version of record. , Cover Page; Title Page; Copyright Page; Contents; I. Introduction; II. The Structure of FSGM; A. Model Overview; B. Consumption; C. Capital and Investment; D. Potential Output and the Output Gap; E. Labor Sector; F. Domestic Prices and Inflation; G. Trade Volumes and Prices; H. Commodities Volumes and Prices; I. Government; J. Interest Rates; K. External Sector; L. Productivity Spillovers; III. Calibration; A. First Step: Choosing Initial Values; B. Second Step: Calibration Adjustments Based on the Properties of the Full Model; C. Calibration Techniques Going Forward; IV. Model Properties , A. Temporary Increase in the Monetary Policy Rate in the United KingdomB. Temporary Increase in Aggregate Demand in Canada; C. Trade Spillovers; D. Permanent Increase in Trend Total Factor Productivity in the United States; E. Productivity Spillovers; F. Permanently Lower Household Saving Rate in China; G. Permanent Fiscal Consolidation in Japan; H. Temporary Increase in Interest Rate Risk Premiums in the United States; I. Temporary Increase in the Term Premium in the United States; J. Permanent Increase in the Real Global Price of Oil; V. Conclusions , Appendix I. Composition of the Modules of FSGMAppendix II. Structural Interpretation; References; Footnotes
    Additional Edition: ISBN 1-4755-1866-8
    Language: English
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