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Econometrics
Special Issues
Advancements in Macroeconometric Modeling and Time Series Analysis
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Advancements in Macroeconometric Modeling and Time Series Analysis

A special issue of Econometrics (ISSN 2225-1146).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3867

Special Issue Editor

Prof. Dr. Julien Chevallier

E-Mail Website
Guest Editor
Laboratoire d'Économie Dionysien (LED), University Paris 8, 93526 Saint-Denis, France
Interests: empirical finance; computational econometrics; commodities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues:

Econometrics is looking for state-of-the-art theoretical and empirical contributions in the fields of macroeconomic modeling and time series analysis.

Throughout the review process, special attention will be devoted to replicating the empirical estimates (e.g., an external audit of code and data).

Research areas may include (but are not limited to) the following:

  • econometric testing
  • asymptotic theory, simulation, and computation
  • causal inference
  • sampling methods
  • probabilistic prediction
  • robust statistics and estimation
  • high/infinite-dimensional inference
  • semi- and non-parametric models
  • mixture models
  • statistical modeling for stochastic processes
  • Markov regime-switching processes
  • Lévy processes and jumps
  • artificial intelligence applications in econometrics
  • machine learning versus time series: estimation, tests, and regularization
  • quantile regression
  • copulas and dependence
  • extreme value theory and statistics
  • financial econometrics
  • option pricing
  • dynamic conditional score models
  • asset pricing
  • risk and volatility in financial markets
  • high-frequency data, realized volatility, and microstructure noise
  • multivariate correlation models
  • macroeconometrics
  • identification of structural VARs: new developments
  • latent macroeconomic variable modeling and Kalman filtering
  • nowcasting
  • credit risk modeling
  • Value-at-Risk and tail risk
  • inflation dynamics and targeting
  • fixed-income markets: new horizons
  • factor models
  • forecasting macroeconomic and financial risk
  • yield curve modeling
  • Central Bank’s announcement modeling
  • analysts’ forecast accuracy
  • MIDAS modeling in macroeconomics
  • effects of macroeconomic policies

In this Special Issue, both original research articles and reviews are welcome.

We look forward to receiving your original contributions.

Prof. Dr. Julien Chevallier
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Econometrics is an international peer-reviewed open access quarterly 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 1400 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.

Keywords

  • time series analysis
  • multivariate data analysis
  • numerical methods
  • data visualization
  • macroeconomy
  • structural VARs
  • testing
  • inference

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

36 pages, 670 KiB  
Article
Forecasting Asset Returns Using Nelson–Siegel Factors Estimated from the US Yield Curve
by Massimo Guidolin and Serena Ionta
Econometrics 2025, 13(2), 17; https://doi.org/10.3390/econometrics13020017 - 11 Apr 2025
Viewed by 211
Abstract
This paper explores the hypothesis that the returns of asset classes can be predicted using common, systematic risk factors represented by the level, slope, and curvature of the US interest rate term structure. These are extracted using the Nelson–Siegel model, which effectively captures [...] Read more.
This paper explores the hypothesis that the returns of asset classes can be predicted using common, systematic risk factors represented by the level, slope, and curvature of the US interest rate term structure. These are extracted using the Nelson–Siegel model, which effectively captures the three dimensions of the yield curve. To forecast the factors, we applied autoregressive (AR) and vector autoregressive (VAR) models. Using their forecasts, we predict the returns of government and corporate bonds, equities, REITs, and commodity futures. Our predictions were compared against two benchmarks: the historical mean, and an AR(1) model based on past returns. We employed the Diebold–Mariano test and the Model Confidence Set procedure to assess the comparative forecast accuracy. We found that Nelson–Siegel factors had significant predictive power for one-month-ahead returns of bonds, equities, and REITs, but not for commodity futures. However, for 6-month and 12-month-ahead forecasts, neither the AR(1) nor VAR(1) models based on Nelson–Siegel factors outperformed the benchmarks. These results suggest that the Nelson–Siegel factors affect the aggregate stochastic discount factor for pricing all assets traded in the US economy. Full article
(This article belongs to the Special Issue Advancements in Macroeconometric Modeling and Time Series Analysis)
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13 pages, 314 KiB  
Article
Investigating Some Issues Relating to Regime Matching
by Anthony D. Hall and Adrian R. Pagan
Econometrics 2025, 13(1), 9; https://doi.org/10.3390/econometrics13010009 - 21 Feb 2025
Viewed by 434
Abstract
Markov switching models are a common tool used in many disciplines as well as in Economics, and estimation methods are available in many software packages. Estimated models are commonly used for allocating observations to regimes. This allocation is usually done using a rule [...] Read more.
Markov switching models are a common tool used in many disciplines as well as in Economics, and estimation methods are available in many software packages. Estimated models are commonly used for allocating observations to regimes. This allocation is usually done using a rule based on the estimated smoothed probabilities, such as, in the two regime case, when it exceeds the threshold of 0.5. The accuracy of the regime matching is often measured by the concordance index. Can regime matching be improved by using other rules? By replicating a number of published two-and three- regime studies and the use of simulation methods, it demonstrates that other rules can improve on the performance of the rule based on the threshold of 0.5. Using simulated models we extend the analysis of a single series to investigate, and demonstrate the efficacy of Markov switching models identifying a common factor in multiple time series. Full article
(This article belongs to the Special Issue Advancements in Macroeconometric Modeling and Time Series Analysis)
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20 pages, 478 KiB  
Article
Long-Term Care in Germany in the Context of the Demographic Transition—An Outlook for the Expenses of Long-Term Care Insurance through 2050
by Patrizio Vanella, Christina Benita Wilke and Moritz Heß
Econometrics 2024, 12(4), 28; https://doi.org/10.3390/econometrics12040028 - 9 Oct 2024
Viewed by 2064
Abstract
Demographic aging results in a growing number of older people in need of care in many regions all over the world. Germany has witnessed steady population aging for decades, prompting policymakers and other stakeholders to discuss how to fulfill the rapidly growing demand [...] Read more.
Demographic aging results in a growing number of older people in need of care in many regions all over the world. Germany has witnessed steady population aging for decades, prompting policymakers and other stakeholders to discuss how to fulfill the rapidly growing demand for care workers and finance the rising costs of long-term care. Informed decisions on this matter to ensure the sustainability of the statutory long-term care insurance system require reliable knowledge of the associated future costs. These need to be simulated based on well-designed forecast models that holistically include the complexity of the forecast problem, namely the demographic transition, epidemiological trends, concrete demand for and supply of specific care services, and the respective costs. Care risks heavily depend on demographics, both in absolute terms and according to severity. The number of persons in need of care, disaggregated by severity of disability, in turn, is the main driver of the remuneration that is paid by long-term care insurance. Therefore, detailed forecasts of the population and care rates are important ingredients for forecasts of long-term care insurance expenditures. We present a novel approach based on a stochastic demographic cohort-component approach that includes trends in age- and sex-specific care rates and the demand for specific care services, given changing preferences over the life course. The model is executed for Germany until the year 2050 as a case study. Full article
(This article belongs to the Special Issue Advancements in Macroeconometric Modeling and Time Series Analysis)
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