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1. |
Record Nr. |
UNINA9910972471103321 |
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Autore |
Isard Peter |
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Titolo |
Uncovered Interest Parity / / Peter Isard |
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Pubbl/distr/stampa |
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Washington, D.C. : , : International Monetary Fund, , 2006 |
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ISBN |
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9786613823311 |
9781462356874 |
1462356877 |
9781452744094 |
1452744092 |
9781283101264 |
1283101262 |
9781451908909 |
1451908903 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (14 pages) |
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Collana |
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Soggetti |
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Banks and Banking |
Foreign Exchange |
Money and Monetary Policy |
Interest Rates: Determination, Term Structure, and Effects |
Monetary Systems |
Standards |
Regimes |
Government and the Monetary System |
Payment Systems |
Currency |
Foreign exchange |
Finance |
Monetary economics |
Interest rate parity |
Spot exchange rates |
Exchange rates |
Currencies |
Forward exchange rates |
Interest rates |
Money |
United Kingdom |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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Sommario/riassunto |
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This paper provides an overview of the uncovered interest parity assumption. It traces the history of the interest parity concept, summarizes evidence on the empirical validity of uncovered interest parity, and discusses different interpretations of the evidence and the implications for macroeconomic analysis. The uncovered interest parity assumption has been an important building block in multiperiod models of open economies, and although its validity is strongly challenged by the empirical evidence, at least at short time horizons, its retention in macroeconomic models is supported on pragmatic grounds by the lack of much empirical support for existing models of the exchange risk premium. |
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2. |
Record Nr. |
UNINA9911011776303321 |
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Autore |
Wu Xintao |
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Titolo |
Data Science: Foundations and Applications : 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VI / / edited by Xintao Wu, Myra Spiliopoulou, Can Wang, Vipin Kumar, Longbing Cao, Xiangmin Zhou, Guansong Pang, Joao Gama |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (813 pages) |
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Collana |
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Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 15875 |
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Altri autori (Persone) |
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SpiliopoulouMyra |
WangCan |
KumarVipin |
CaoLongbing |
ZhouXiangmin |
PangGuansong |
GamaJoão |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Algorithms |
Education - Data processing |
Computer science - Mathematics |
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Signal processing |
Computer networks |
Artificial Intelligence |
Design and Analysis of Algorithms |
Computers and Education |
Mathematics of Computing |
Signal, Speech and Image Processing |
Computer Communication Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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-- Survey Track. -- Large Language Models for Cybersecurity Education: A Survey of Current Practices and Future Directions. -- A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping. -- A Survey of Foundation Models for Environmental Science. -- A Survey on Efficient Graph Reachability Queries. -- Machine Learning. -- Disentangled Representation Learning for Geospatial-temporal Data Modeling. -- Treatment Effect Estimation for Graph-Structured Targets. -- Dynamic DropConnect: Enhancing Neural Network Robustness through Adaptive Edge Dropping Strategies. -- The Brownian Integral Kernel: A New Kernel for Modeling Integrated Brownian Motions. -- Fed-ARIMA-OPARBFN: An Ensemble Model for Cross-Domain Crop Yield Time Series Prediction Based on Federated Learning. -- S-CPD: Topological Smoothing-Based Change Point Detection. -- VDASI: VAE-Enhanced Degradation-Aware System Identification Using Constrained Latent Spaces. -- Disentangled Mode-Specific Representations for Tensor Time Series via Contrastive Learning. -- PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting. -- Advancing Long-Term High-Frequency Dissolved Oxygen Forecasting for Australian Rivers. -- CNO-former: Chaotic Neural Oscillatory Transformer for Social Media Text Generation. -- Multilingual Non-Factoid Question Answering with Answer Paragraph Selection -- Turning Uncertainty to Information by Intervals in Ensemble Classifiers. -- Determining the Need for Multi-Label Classifiers by Measuring Unexplained Covariance. -- Evaluating Generative Vehicle Trajectory Models for Traffic Intersection Dynamics. -- Trustworthiness. -- Inversion Triplet - A Contrastive Backdoor Mitigation Method for Self-Supervised Vision Encoders. -- Beyond Uniformity: Robust Backdoor Attacks on Deep Neural Networks with Trigger Selection. -- Defence Against Multi-target Multi-trigger Backdoor Attack. -- How to Backdoor Consistency Models?. -- Multi-granularity Policy Explanation of Deep Reinforcement Learning Based on Saliency Map Clustering. -- FACROC: A Fairness Measure for Fair Clustering Through ROC Curves. -- Learning on Complex Data. -- Action Sequence Analysis Using Temporal Commonsense Knowledge. -- Foundation Model for Lossy Compression of Spatiotemporal Scientific Data. -- CANTER: A Novel Causal Model for Tourism Demand Forecasting. -- Time-Aware Complex Attention Space for Temporal Knowledge Graph Completion. -- Adaptive Extraction of Variable-Length Subsequence Patterns in |
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Noisy Time Series. -- Hunting Inside N-Quantiles of Outliers (Hino). -- Fast Approximation Algorithm for Euclidean Minimum Spanning Tree Building in High Dimensions. -- ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton. -- Offline Map Matching Based on Localization Error Distribution Modeling. |
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Sommario/riassunto |
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The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025. The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models. |
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