Time Series Methods in Macroeconomic Forecasting

Dear Colleagues,

One of the research areas that plays an important role in monetary policy, fiscal planning, financial stability, and development strategy formulation is macroeconomic forecasting. Recent global events such as pandemics, geopolitical tensions, climate risks and financial volatility demonstrated that traditional forecasting frameworks have limitations. This accelerated the adoption of advanced time series methods, real-time data, and hybrid econometric and machine learning approaches.

The aim of this Special Issue is to bring together cutting-edge theoretical, methodological, and applied research on time series methods in macroeconomic forecasting. It has  a strong emphasis on policy relevance. We encourage contributions from researchers working at the interface of academia and policy institutions, such as central banks, treasuries, and international financial institutions.

Topics of interest

Methodological advances in econometric modelling and forecasting (core time series methodologies)

• VAR, SVAR, FAVAR and time-varying models
• Advances in Multivariate Time Series Models for Macroeconomic Forecasting
• Nonlinear Time Series Models and Regime-Switching Macroeconomic Dynamics
• High-Dimensional Time Series and Factor Models
• Structural Breaks, Time-Varying Parameters, and Forecast Instability
• Machine learning approaches
• Application of machine learning and traditional time series models in macroeconomic forecasting
• Machine learning models application in macroeconomic time series
• Deep learning in macroeconomic forecasting
• Artificial intelligence in macroeconomic forecasting
• Model averaging and forecast combination in big data
• Data innovation and real time issues
• Mixed frequency vector autoregression and mixed data sampling in macroeconomic forecasting
• Nowcasting using high frequency data
• Big data and text-based time series in macroeconomics
• Monetary policy, inflation and financial stability
• Supply-side shocks: inflation forecasting
• Yield curve and inflation forecasting
• Systemic risk and macroeconomic linkages forecasting
• Use of survey and market-based data to forecast inflation
• Public finance forecasting
• Forecasting and expenditure and revenue
• Models to forecast sustainability of debt (using stochastic time series methods)
• Modelling and forecasting of fiscal risks in economies that are dependent on commodities
• Global forecasting and open economy
• External imbalances and exchange rate forecasting
• Balance of payments, a capital flows and spillover effects
• Global business transmission (using panel data models)
• Risk, uncertainty and policy communications
• Density forecasting and probabilistic forecasting
• Analysis of scenarios and stress testing
• Forecast uncertainty and communicate to policymakers
• Forecasting in emerging economies and development context
• Macroeconomic modelling and forecasting in data constrained environments
• Structural change and time series econometric modelling
• The challenges of forecasting in economies that are resource-rich and those that are vulnerable to climate change
• Climate change economics modelling

Prof. Dr. Joel Hinaunye Eita
Dr. John Weirstrass Muteba Mwamba
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• time series econometrics
• macroeconomic forecasting
• inflation forecasting
• monetary policy analysis
• fiscal and revenue forecasting
• VAR
• Bayesian VAR
• regime-switching models
• structural breaks
• mixed-frequency data
• nowcasting
• real-time data and data revisions
• density forecasting
• forecast uncertainty
• forecast evaluation
• exchange rate forecasting
• macro-financial linkages
• panel data models
• big data and alternative data
• machine learning
• AI for policy forecasting
• central bank forecasting models
• international financial institutions