04488oam 22010454 450 991095980920332120250426110619.0978661382237697814623750281462375022978145198330214519833019781282558243128255824297814519871021451987102(CKB)3360000000443272(EBL)3014393(SSID)ssj0000939933(PQKBManifestationID)11596387(PQKBTitleCode)TC0000939933(PQKBWorkID)10939185(PQKB)10949693(OCoLC)698585655(MiAaPQ)EBC3014393(IMF)WPIEE2006175(IMF)WPIEA2006175WPIEA2006175(EXLCZ)99336000000044327220020129d2006 uf 0engur|n|---|||||txtccrThe Role of Seasonality and Monetary Policy in Inflation Forecasting /Francis Kumah1st ed.Washington, D.C. :International Monetary Fund,2006.1 online resource (27 p.)IMF Working Papers"July 2006".9781451864359 1451864353 Includes bibliographical references.""Contents""; ""I. INTRODUCTION""; ""II. INFLATION AND MONETARY POLICY IN THE KYRGYZ REPUBLIC""; ""III. SEASONAL CHARACTERISTICS OF CONSUMER PRICES""; ""IV. MODELING AND FORECASTING INFLATION""; ""V. CONCLUDING REMARKS""; ""References""; ""Appendix. Further Empirical Results""Adequate modeling of the seasonal structure of consumer prices is essential for inflation forecasting. This paper suggests a new econometric approach for jointly determining inflation forecasts and monetary policy stances, particularly where seasonal fluctuations of economic activity and prices are pronounced. In an application of the framework, the paper characterizes and investigates the stability of the seasonal pattern of consumer prices in the Kyrgyz Republic and estimates optimal money growth and implied exchange rate paths along with a jointly determined inflation forecast. The approach uses two broad specifications of an augmented error-correction model-with and without seasonal components. Findings from the paper confirm empirical superiority (in terms of information content and contributions to policymaking) of augmented error-correction models of inflation over single-equation, Box-Jenkins-type general autoregressive seasonal models. Simulations of the estimated errorcorrection models yield optimal monetary policy paths for achieving inflation targets and demonstrate the empirical significance of seasonality and monetary policy in inflation forecasting.IMF Working Papers; Working Paper ;No. 2006/175Inflation (Finance)ForecastingMonetary policyConsumer price indexesimfConsumer pricesimfCurrencyimfDeflationimfEconomic ForecastingimfEconomic forecastingimfExchange ratesimfForecasting and Other Model ApplicationsimfForecastingimfForeign ExchangeimfForeign exchangeimfInflationimfMacroeconomicsimfPrice indexesimfPrice LevelimfPricesimfKyrgyz RepublicimfInflation (Finance)Forecasting.Monetary policy.Consumer price indexesConsumer pricesCurrencyDeflationEconomic ForecastingEconomic forecastingExchange ratesForecasting and Other Model ApplicationsForecastingForeign ExchangeForeign exchangeInflationMacroeconomicsPrice indexesPrice LevelPricesKumah Francis1815690DcWaIMFBOOK9910959809203321The Role of Seasonality and Monetary Policy in Inflation Forecasting4371160UNINA06964nam 22007095 450 991101586470332120250703130250.03-031-97567-710.1007/978-3-031-97567-7(MiAaPQ)EBC32195978(Au-PeEL)EBL32195978(CKB)39578211800041(DE-He213)978-3-031-97567-7(EXLCZ)993957821180004120250703d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputational Science – ICCS 2025 Workshops 25th International Conference, Singapore, Singapore, July 7–9, 2025, Proceedings, Part IV /edited by Maciej Paszynski, Amanda S. Barnard, Yongjie Jessica Zhang1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (636 pages)Lecture Notes in Computer Science,1611-3349 ;159103-031-97566-9 Machine Learning and Data Assimilation for Dynamical Systems -- Cluster-based Reduced-order Modelling and Control for Chaotic Systems with Extreme Events -- First Experiences on Exploiting Physics-Informed Neural Networks for Approximating Solutions of a Biological Model -- A Machine Learning System for Energy Forecasting with Feature Importance Analysis -- Latent Three-dimensional Variational Data Assimilation with Convolutional Autoencoder and LSTM for Flood Forecasting -- Online Model Learning with Data-assimilated Reservoir Computers -- Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows -- SHAP-prioritised Machine Learning for Diagnostic Grade Prediction of Lung Function -- Turn Detection in Alpine Skiing Using Smartphone Sensors -- Assimilation of Data for Dynamic Digital Twins by Learning Covariance Information -- Multi-Criteria Decision-Making: Methods, Applications, and Innovations -- Issues Importance Analysis for Reaching High-Quality Consensus in Preference-Based Conflict Scenarios -- Integrating Conflict Analysis and Rule-Based Systems for Dispersed Data Classification -- Multicriteria Framework for Digital Content Design and Evaluation in Cross-Generational Targeting -- Integrating Habituation Effects with UCB and Softmax Multi-Armed Bandit Algorithms for Optimized Digital Content Delivery -- Computational Risk Assessment in Water Distribution Network -- Preserving Informative Content of Condition Attributes in Data Transformations for CRSA -- Using SSP-VIKOR in Sustainable Share of Renewable Energy Sources Assessment -- Decision-Making of Homogeneous Multiple Classifiers Based on Attribute Characterisation by Discretisation -- New Multi-Criteria Approach to Sustainable Development Assessment -- Compromise Fuzzy Ranking: A Novel Method for Reaching Consensus in Complex Multi-criteria Decision Problems -- A New Approach to Large-scale Multi-criteria Group Decision-making Based on the RANCOM Method -- Towards Sustainable Decision Making: New Reference Point-Based MCDA Method -- An Adaptive RANCOM-ST Method for Bias Reduction using Statistical Thresholds -- Strong Sustainability Paradigm in TOPSIS Method: New Approach to Wind Farm Selection Problem -- Aspects of Implementing RPA in an IT Company -- Evaluating Sufficiency Practices for Sustainable Competitiveness using AHP-grey Analysis -- Actionable Fire Modeling in Firemap for Extended Attack Decision Support -- The Role of Preference Reidentification in MCDA: Comparing Weight-Based, Normalization, and Reference-Object Approaches -- Subjective Equal Criteria Influence Approach (SECIA): A Novel Extended Approach to Weights Determination -- Local Markovian Consensus for Ranking Aggregation: A Novel Approach to Consensus Ranking with Weak Ordinal Dominance.The 6-volume set constitutes the worshop proceedings of the 25th International Conference on Computational Science, ICCS 2025, which took place in Singapore, Singapore, during July 7–9, 2025. The 137 full papers and 32 short papers presented in these proceedings were carefully reviewed and selected from 322 submissions. The papers are organized in the following topical sections: Volume I: Advances in high-performance computational earth sciences: numerical methods, frameworks & applications; artificial intelligence approaches for network analysis; artificial intelligence and high-performance computing for advanced simulations; and biomedical and bioinformatics challenges for computer science. Volume II: Computational health; computational modeling and artificial intelligence for social systems; and computational optimization, modelling and simulation. Volume III: Computational science and AI for addressing complex and dynamic societal challenges equitably; computer graphics, image processing and artificial intelligence; computing and data science for materials discovery and design; and large language models and intelligent decision-making within the digital economy. Volume IV: Machine learning and data assimilation for dynamical systems; and multi-criteria decision-making: methods, applications, and innovations. Volume V: (Credible) Multiscale modelling and simulation; numerical algorithms and computer arithmetic for computational science; quantum computing; retrieval-augmented generation; and simulations of flow and transport: modeling, algorithms and computation. Volume VI: Smart systems: bringing together computer vision, sensor networks and artificial intelligence; solving problems with uncertainty; and teaching computational science.Lecture Notes in Computer Science,1611-3349 ;15910Computer scienceArtificial intelligenceComputer engineeringComputer networksSoftware engineeringComputer scienceMathematicsTheory of ComputationArtificial IntelligenceComputer Engineering and NetworksSoftware EngineeringMathematics of ComputingComputer science.Artificial intelligence.Computer engineering.Computer networks.Software engineering.Computer scienceMathematics.Theory of Computation.Artificial Intelligence.Computer Engineering and Networks.Software Engineering.Mathematics of Computing.004.0151Paszynski Maciej1369669Barnard A. S(Amanda S.)1832326Zhang Yongjie Jessica1832327MiAaPQMiAaPQMiAaPQBOOK9911015864703321Computational Science - ICCS 2025 Workshops4406365UNINA