Forecasting The Nominal Brent Oil Price With Vars One Model Fits All

We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons.

This book presents the latest developments in both qualitative and quantitative computational methods for reliability and statistics, as well as their applications. Consisting of contributions from active researchers and experienced practitioners in the field, it fills the gap between theory and practice and explores new research challenges in reliability and statistical computing. The book consists of 18 chapters. It covers (1) modeling in and methods for reliability computing, with chapters dedicated to predicted reliability modeling, optimal maintenance models, and mechanical reliability and safety analysis; (2) statistical computing methods, including machine learning techniques and deep learning approaches for sentiment analysis and recommendation systems; and (3) applications and case studies, such as modeling innovation paths of European firms, aircraft components, bus safety analysis, performance prediction in textile finishing processes, and movie recommendation systems. Given its scope, the book will appeal to postgraduates, researchers, professors, scientists, and practitioners in a range of fields, including reliability engineering and management, maintenance engineering, quality management, statistics, computer science and engineering, mechanical engineering, business analytics, and data science.

In today’s competitive market, a manager must be able to look at data, understand it, analyze it, and then interpret it to design a smart business strategy. Big data is also a valuable source of information on how customers interact with firms through various mediums such as social media platforms, online reviews, and many more. The applications and uses of business analytics are numerous and must be further studied to ensure they are utilized appropriately. Data-Driven Approaches for Effective Managerial Decision Making investigates management concepts and applications using data analytics and outlines future research directions. The book also addresses contemporary advancements and innovations in the field of management. Covering key topics such as big data, business intelligence, and artificial intelligence, this reference work is ideal for managers, business owners, industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

The IMF Research Bulletin includes listings of recent IMF Working Papers and Staff Discussion Notes. The research summaries in this issue are “Explaining the Recent Slump in Investment” (Mathieu Bussiere, Laurent Ferrara, and Juliana Milovich) and “The Quest for Stability in the Housing Markets” (Hites Ahir). The Q&A column reviews “Seven Questions on Estimating Monetary Transmission Mechanism in Low-Income Countries” (Bin Grace Li, Christopher Adam, and Andrew Berg). Also included in this issue are updates on the IMF’s official journal, the IMF Economic Review, and recommended readings from IMF Publications.

We present a newly developed Quarterly Projection Model (QPM) for Vietnam. This QPM represents an extended version of the canonical New Keynesian semi-structural model, accounting for Vietnam-specific factors, including a hybrid monetary policy framework. The model incorporates the array of policy instruments, specifically interest rates, indicative nominal credit growth guidance, and exchange rate interventions, that the authorities employ to meet the primary objective of price stability. The calibrated model embeds a theoretically consistent monetary transmission mechanism and demonstrates robust in-sample forecasting accuracy, both of which are important prerequisites for the richer analysis and forecast-based narratives that support a forward-looking monetary policy regime.

Fluctuations of commodity prices, most notably of oil, capture considerable attention and have been tied to important economic effects. This book advances our understanding of the consequences of these fluctuations, providing both general analysis and a particular focus on the countries of the Pacific Rim.

This is the third 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 for studying both own-country and cross-country linkages. In this paper, we estimate a small quarterly projection model of the US, Euro Area, and Japanese economies that incorporates oil prices and allows us to trace out the effects of shocks to oil prices. The model is estimated with Bayesian techniques. We show how the model can be used to construct efficient baseline forecasts that incorporate judgment imposed on the near-term outlook.