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 1757

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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
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  • mixture models
  • statistical modeling for stochastic processes
  • Markov regime-switching processes
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  • quantile regression
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  • extreme value theory and statistics
  • financial econometrics
  • option pricing
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  • risk and volatility in financial markets
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  • multivariate correlation models
  • macroeconometrics
  • identification of structural VARs: new developments
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  • nowcasting
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  • 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

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Keywords

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

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Published Papers (1 paper)

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Research

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 1027
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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