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Many central banks and government agencies use nowcasting techniques to obtain policy relevant information about the business cycle. Existing nowcasting methods, however, have two critical shortcomings for this purpose. First, in contrast to machine-learning models, they do not provide much if any guidance on selecting the best explantory variables (both high- and low-frequency indicators) from the (typically) larger set of variables available to the nowcaster. Second, in addition to the selection of explanatory variables, the order of the autoregression and moving average terms to use in the baseline nowcasting regression is often set arbitrarily. This paper proposes a simple procedure that simultaneously selects the optimal indicators and ARIMA(p,q) terms for the baseline nowcasting regression. The proposed AS-ARIMAX (Adjusted Stepwise Autoregressive Moving Average methods with exogenous variables) approach significantly reduces out-of-sample root mean square error for nowcasts of real GDP of six countries, including India, Argentina, Australia, South Africa, the United Kingdom, and the United States.
The paper introduces a semi-structural Quarterly Projection Model (QPM) tailored for the Democratic Republic of the Congo (DRC), highlighting its resource richness and high degree of dollarization. We provide an overview of the model's specifications to elucidate key features of the DRC economy and present its properties, evaluating its alignment with DRC data and assessing its goodness of fit. Additionally, the paper demonstrates the QPM's practical application through a counterfactual scenario, comparing policy recommendations with the actual policy responses of the Central Bank of the Republic of Congo to observed exchange rate and inflation pressures in 2023. Beyond the QPM, the paper showcases supplementary tools that enhance its utility for generating medium-term forecasts and developiong narratives in support of monetary policymaking. Specifically, we introduce the Nowcasting and Near-Term Forecast models, designed to assess the economy in real-time and predict short-term inflationary trends.
Economic forecasting is a key ingredient of decision making in the public and private sectors. This book provides the necessary tools to solve real-world forecasting problems using time-series methods. It targets undergraduate and graduate students as well as researchers in public and private institutions interested in applied economic forecasting.
This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book. The book also discusses applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. In day-by-day data, we often encounter outliers that do not reflect the long-term economic trends, e.g., unexpected and abrupt fluctuations. As such, it is important to develop robust data processing techniques that can accommodate these fluctuations.
This book presents the latest developments in the theory and applications of time series analysis and forecasting. Comprising a selection of refereed papers, it is divided into several parts that address modern theoretical aspects of time series analysis, forecasting and prediction, with applications to various disciplines, including econometrics and energy research. The broad range of topics discussed, including matters of particular relevance for sustainable development, will give readers a modern perspective on the subject. The included contributions were originally presented at the 8th International Conference on Time Series and Forecasting, ITISE 2022, held in Gran Canaria, Spain, June 27-30, 2022. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.
This book constitutes the refereed post-conference proceedings of the 7th International Conference on Nature of Computation and Communication, ICTCC 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 revised full papers presented were carefully selected from 43 submissions. The papers of ICTCC 2021 cover formal methods for self-adaptive systems and discuss natural approaches and techniques for natural computing systems and their applications.
Mrs Midas, Queen Kong, Mrs Lazarus, the Kray sisters, and a huge cast of others startle with their wit, imagination, lyrical intuition and incisiveness.
This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions