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Econometrics
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Special Issue on Time Series Econometrics
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Special Issue on Time Series Econometrics

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

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 18119

Special Issue Editors

Prof. Dr. Alessandra Luati

E-Mail Website
Guest Editor
Dipartimento di Scienze Statistiche “Paolo Fortunati”, University of Bologna, 40126 Bologna BO, Italy
Interests: time series analysis; frequency domain methods; score-driven models; locally stationary processes; statistical inference for quantum mechanics: information theory
Prof. Dr. Claudio Morana

E-Mail Website
Guest Editor
Dipartimento di Economia, Metodi Quantitativi e Strategie di Impresa, University of Milano- Bicocca, 20126 Milano, MI, Italy
Interests: linear and nonlinear large-scale time series models; macro, financial, and climate change econometrics; the microfinance interface and boom–bust macro-financial cycles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to discuss advances in time series econometrics from both a theoretical and applied perspective. We solicit the submission of papers whose novelty stems from the development and introduction of new time series econometric models. Concerning applied works, the issue is welcoming applications to macroeconomic and financial analysis, their interface, and contributions relevant for policy evaluation. We also welcome papers dealing with the COVID-19 pandemic. We particularly welcome submissions highlighting interesting statistical challenges to which time series econometric methods can contribute.

Prof. Dr. Alessandra Luati
Prof. Dr. Claudio Morana
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 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
  • frequency and time domain methods
  • univariate and multivariate analysis
  • macroeconomic and financial applications of time series analysis
  • COVID-19 applications of time series analysis

Published Papers (5 papers)

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Research

29 pages, 6426 KiB  
Article
Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers
by Graziano Moramarco
Econometrics 2023, 11(1), 2; https://doi.org/10.3390/econometrics11010002 - 28 Dec 2022
Cited by 2 | Viewed by 4753
Abstract
We propose an approach for jointly measuring global macroeconomic uncertainty and bilateral spillovers of uncertainty between countries using a global vector autoregressive (GVAR) model. Over the period 2000Q1–2020Q4, our global index is able to summarize a variety of uncertainty measures, such as financial-market [...] Read more.
We propose an approach for jointly measuring global macroeconomic uncertainty and bilateral spillovers of uncertainty between countries using a global vector autoregressive (GVAR) model. Over the period 2000Q1–2020Q4, our global index is able to summarize a variety of uncertainty measures, such as financial-market volatility, economic-policy uncertainty, survey-forecast-based measures and econometric measures of macroeconomic uncertainty, showing major peaks during both the global financial crisis and the COVID-19 pandemic. Global spillover effects are quantified through a novel GVAR-based decomposition of country-level uncertainty into the contributions from all countries in the global model. We show that this approach produces estimates of uncertainty spillovers which are strongly related to the structure of the global economy. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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41 pages, 2020 KiB  
Article
A Parsimonious Test of Constancy of a Positive Definite Correlation Matrix in a Multivariate Time-Varying GARCH Model
by Jian Kang, Johan Stax Jakobsen, Annastiina Silvennoinen, Timo Teräsvirta and Glen Wade
Econometrics 2022, 10(3), 30; https://doi.org/10.3390/econometrics10030030 - 24 Aug 2022
Cited by 1 | Viewed by 2283
Abstract
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a [...] Read more.
We construct a parsimonious test of constancy of the correlation matrix in the multivariate conditional correlation GARCH model, where the GARCH equations are time-varying. The alternative to constancy is that the correlations change deterministically as a function of time. The alternative is a covariance matrix, not a correlation matrix, so the test may be viewed as a general test of stability of a constant correlation matrix. The size of the test in finite samples is studied by simulation. An empirical example involving daily returns of 26 stocks included in the Dow Jones stock index is given. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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27 pages, 701 KiB  
Article
Common Correlated Effects Estimation for Dynamic Heterogeneous Panels with Non-Stationary Multi-Factor Error Structures
by Shiyun Cao and Qiankun Zhou
Econometrics 2022, 10(3), 29; https://doi.org/10.3390/econometrics10030029 - 11 Aug 2022
Cited by 4 | Viewed by 3450
Abstract
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as [...] Read more.
In this paper, we consider the estimation of a dynamic panel data model with non-stationary multi-factor error structures. We adopted the common correlated effect (CCE) estimation and established the asymptotic properties of the CCE and common correlated effects mean group (CCEMG) estimators, as N and T tend to infinity. The results show that both the CCE and CCEMG estimators are consistent and the CCEMG estimator is asymptotically normally distributed. The theoretical findings were supported for small samples by an extensive simulation study, showing that the CCE estimators are robust to a wide variety of data generation processes. Empirical findings suggest that the CCE estimation is widely applicable to models with non-stationary factors. The proposed procedure is also illustrated by an empirical application to analyze the U.S. cigar dataset. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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24 pages, 1175 KiB  
Article
Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure
by Antonio Pacifico
Econometrics 2022, 10(3), 28; https://doi.org/10.3390/econometrics10030028 - 12 Jul 2022
Cited by 1 | Viewed by 2414
Abstract
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry [...] Read more.
This paper improves the existing literature on the shrinkage of high dimensional model and parameter spaces through Bayesian priors and Markov Chains algorithms. A hierarchical semiparametric Bayes approach is developed to overtake limits and misspecificity involved in compressed regression models. Methodologically, a multicountry large structural Panel Vector Autoregression is compressed through a robust model averaging to select the best subset across all possible combinations of predictors, where robust stands for the use of mixtures of proper conjugate priors. Concerning dynamic analysis, volatility changes and conditional density forecasts are addressed ensuring accurate predictive performance and capability. An empirical and simulated experiment are developed to highlight and discuss the functioning of the estimating procedure and forecasting accuracy. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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14 pages, 1199 KiB  
Article
Impact of COVID-19 Pandemic News on the Cryptocurrency Market and Gold Returns: A Quantile-on-Quantile Regression Analysis
by Esam Mahdi and Ameena Al-Abdulla
Econometrics 2022, 10(2), 26; https://doi.org/10.3390/econometrics10020026 - 02 Jun 2022
Cited by 5 | Viewed by 3409
Abstract
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index [...] Read more.
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index associated with coronavirus outbreak and Bitcoin and gold returns. Our results reveal that the daily levels of positive and negative shocks in indices induced by pandemic news asymmetrically affect the Bearish and Bullish on Bitcoin and gold, and fear sentiment induced by coronavirus-related news plays a major role in driving the values of Bitcoin and gold more than other indices. We find that both commodities, Bitcoin and gold, can serve as a hedge against pandemic-related news. In general, the COVID-19 pandemic-related news encourages people to invest in gold and Bitcoin. Full article
(This article belongs to the Special Issue Special Issue on Time Series Econometrics)
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