journal: Studies in Nonlinear Dynamics & Econometrics

Impact Factor: 0.8

Veröffentlicht seit 1. April 1996

Studies in Nonlinear Dynamics & Econometrics

ISSN: 1558-3708

Herausgegeben von: Bruce Mizrach

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Über diese Zeitschrift

Objective
A peer-reviewed journal since 1996, Studies in Nonlinear Dynamics & Econometrics (SNDE) is at the forefront of statistical and theoretical approaches to economics. The journal studies ways in which econometrics and dynamical systems theory increase our understanding of economic and financial markets. The journal disseminates authors' algorithms, programs, and data sets, allowing other scholars to replicate empirical results. Authors include econometricians such as Clive Granger, James Hamilton, and Halbert White, and theorists Jess Benhabib, Alan Kirman, and Kazuo Nishimura.

Best Paper Award
Since 2015, the Society has awarded $500 each year to the best paper published in the Society’s journal: Studies in Nonlinear Dynamics and Econometrics.

The Best Paper in 2022 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Szabolcs Blazsek, Alvaro Escribano and Adrian Licht. for their paper: "Multivariate Markov-switching score-driven models: an application to the global crude oil market" Studies in Nonlinear Dynamics & Econometrics, vol. 26, no. 3, 2022, pp. 313-335.

The Best Paper in 2021 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Eric Mark Aldrich and Howard Kung for their paper: “Computational Methods for Production-Based Asset Pricing Models with Recursive Utility”, published in Volume 25, Issue 1.

The Best Paper in 2020 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Gregor Bäurle, Daniel Kaufmann, Sylvia Kaufmann and Rodney Strachan for their paper: “Constrained interest rates and changing dynamics at the zero lower bound”, published in Volume 24, Issue 2.

The Best Paper in 2019 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Chang-Jin Kim and Yunmi Kim for their paper: “A unified framework jointly explaining business conditions, stock returns, volatility and “volatility feedback news” effects”, published in Volume 23, Issue 2.

The Best Paper in 2018 for Studies in Nonlinear Dynamics and Econometrics, has been awarded to Luiggi Donayre, Yunjong Eo and James Morley for their paper: “Improving likelihood-ratio-based confidence intervals for threshold parameters in finite samples”,published in Volume 22, Issue 1 (Feb 2018)

The Best Paper in 2017 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Julien Chevallier and Stephane Goutte for their paper: “On the estimation of regime-switching Levy models”, published in Volume 21, Issue 1 (Feb 2017).

The Best Paper in 2016 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Mark J. Jensen for his paper: “Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility?”, published in Volume 20, Issue 4 (Sept. 2016).

The Best Paper in 2015 for Studies in Nonlinear Dynamics and Econometrics has been awarded to Markus Jochmann and Gary Koop for their paper: “Regime-switching cointegration?”, published in Volume 19, Issue 1 (Feb 2015).

Topics

Probability
Statistics
Macroeconomics
Finance
Forecasting
Econometrics

Article formats
Research articles

Information on submission process

Ihre Vorteile

Your benefits:

Theoretical and applied studies that characterize and motivate nonlinear phenomena
State-of the-art research results that increase understanding of economic and financial markets
Novel tools that allow replication of empirical results
High quality peer-review
International and renowned editorial board

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Content available since 1996 (Volume 1, Issue 1)

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Online Submission of Manuscripts

Band 27 Heft 3

Juni 2023

Öffentlich zugänglich 28. Juni 2023

Frontmatter

Seiten: i-ii

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Research Articles

3. Oktober 2022

On determination of the number of factors in an approximate factor model

Jinshan Liu, Jiazhu Pan, Qiang Xia, Li Xiao

Seiten: 285-298

Mehr

Abstract

This paper proposes a ridge-type method for determining the number of factors in an approximate factor model. The new estimator of factor number is obtained by maximizing both the ratio of two adjacent eigenvalues and the cumulative contribution rate of the factors which represents the explanatory power of the common factors for response variables. Our estimator is proved to be as asymptotically consistent as those in (Ahn, S., and A. Horenstein. 2013. “Eigenvalue Ratio Test for the Number of Factors.” Econometrica 81: 1203–27). But Monte Carlo simulation experiments show our method has better correct selection rates in finite sample cases. A real data example is given for illustration.

15. Juli 2022

Clean energy consumption and economic growth in China: a time-varying analysis

Pejman Bahramian, Andisheh Saliminezhad, Sami Fethi

Seiten: 299-313

Mehr

Abstract

Assessing the causal relationships between clean energy consumption and economic growth in China, a central actor in the world’s climate future, have received considerable attention among scholars. However, due to the lack of methodological rigour in the causality analysis, available literature failed to provide solid inferences on the links between the variables. Therefore, this study aims to re-examine the variables’ dynamic linkages with a more well-established approach from 1965 to 2020. We use a time-varying framework that relaxes the assumption of parameter stability, a remarkable feature that distinguishes our paper from the previous studies. Utilizing the conventional Granger causality test, we fail to detect causation between the variables. However, the evidence of substantial time variation in the causal relationships implies that the standard framework’s inference is unreliable. The findings of our time-varying analysis indicate different forms of causality flows in various subperiods. This can be a dependable reason for China to follow its enhanced carbon neutrality target safely. The results of our study also emphasize the significance of considering time-varying causality tests to avoid the risk of misleading inferences.

8. Juli 2022

Panel data models with two threshold variables

Arturo Lamadrid-Contreras, Nelson R. Ramírez-Rondán

Seiten: 315-333

Mehr

Abstract

We develop threshold estimation methods for panel data models with two threshold variables and individual fixed specific effects covering short time periods. In the static panel data model, we propose least squares estimation of the threshold and regression slopes using fixed effects transformations; while in the dynamic panel data model, we propose maximum likelihood estimation of the threshold and slope parameters using first difference transformations. In both models, we propose to estimate the threshold parameters sequentially. We apply the methods to a 15-year sample of 565 U.S. firms to test whether financial constraints affect investment decisions.

24. Juni 2022

What will drive global economic growth in the digital age?

Jakub Growiec

Seiten: 335-354

Mehr

Abstract

This paper provides a theoretical investigation of possible sources of long-run economic growth in the future. Historically, in the industrial era and during the ongoing digital revolution (which began approximately in the 1980s) the main engine of global economic growth has been research and development (R&D), translating into systematic labor-augmenting technological progress and trend growth in labor productivity. If in the future all essential production or R&D tasks will eventually be subject to automation, though, the engine of growth will be shifted to the accumulation of programmable hardware (capital), and R&D will lose its prominence. Economic growth will then accelerate, no longer constrained by the scarce human input. By contrast, if some essential production and R&D tasks will never be fully automatable, then R&D may forever remain the main growth engine, and the human input may forever remain the scarce, limiting factor of global growth. Additional studied mechanisms include the accumulation of R&D capital and hardware-augmenting technical change.

14. Juli 2022

On the nonlinear relationships between shadow economy and the three pillars of sustainable development: new evidence from panel threshold analysis

Sami Saafi, Ridha Nouira, Nadia Assidi

Seiten: 355-375

Mehr

Abstract

Most previous studies that examined the relationship between the size of shadow economy and the pillars of sustainable development maintained that this relationship is linear. This paper provides an empirical contribution to the literature by arguing that this relationship is likely to be nonlinear, and it might be subject to threshold effects. For this purpose, in addition to the static threshold panel model of Hansen (1999. “Threshold Effects in Non-dynamic Panels: Estimation, Testing, and Inference.” Journal of Econometrics 93 (2): 345–68), the dynamic panel threshold model suggested by Seo and Shin (2016. “Dynamic Panels with Threshold Effect and Endogeneity.” Journal of Econometrics 195 (2): 169–86) has been applied to a larger panel-data set covering 83 developed and developing countries over the 1996–2017 period. Empirical results from both models yield evidence advocating the existence of threshold effects of the shadow economy on the economic, social, and environmental dimensions of sustainable development for the global sample as well as the sub-samples of developed and developing countries. Moreover, for the global sample and developing countries, our findings show that shadow economy would spoil the three sustainable development pillars only when its size exceeds a certain threshold critical size. While, the impact for developed countries was found negative even for low levels of underground activities. These finding are shown to be robust to alternative proxies for the size of the shadow economy and have important policy implications, especially for developing countries. In these countries, a moderate size of the shadow economy might have positive spillovers on long-term growth and sustainable development. Our research also suggests that, for developing and developed countries to achieve sustainable goal 8.3, the extent of the shadow activities should be taken into account.

27. Juni 2022

Tail behaviours of multiple-regime threshold AR models with heavy-tailed innovations

Jiazhu Pan, Yali He

Seiten: 377-395

Mehr

Abstract

This paper studies the tail behaviours of the stationary distribution of multiple-regime threshold AR models with multiple heavy-tailed innovations. It is shown that the marginal tail probability has the same order as that of the innovation with the heaviest tail. Other new results in this paper include the geometric ergodicity and the tail dependence of TAR models with multiple heavy-tailed innovations.

30. Mai 2022

Stock price prediction using multi-scale nonlinear ensemble of deep learning and evolutionary weighted support vector regression

Jujie Wang, Zhenzhen Zhuang, Dongming Gao, Yang Li, Liu Feng

Seiten: 397-421

Mehr

Abstract

Stock price prediction has become a focal topic for relevant investors and scholars in these years. However, owning to the non-stationarity and complexity of stock price data, it is challenging to predict stock price accurately. This research develops a novel multi-scale nonlinear ensemble learning framework for stock price prediction, which consists of variational mode decomposition (VMD), evolutionary weighted support vector regression (EWSVR) and long short-term memory network (LSTM). The VMD is utilized to extract the basic features from an original stock price signal and eliminate the disturbance of illusive components. The EWSVR is utilized to predict each sub-signal with corresponding features, whose penalty weights are determined according to the time order and whose parameters are optimized by tree-structured Parzen estimator (TPE). The LSTM-based nonlinear ensemble learning paradigm is employed to integrate the predicted value of each sub-signal into the final prediction result of stock price. Four real prediction cases are utilized to test the proposed model. The proposed model’s prediction results of multiple evaluation metrics are significantly improved compared to other benchmark models both in stock market closing price forecasting.

Ahead-of-Print / Just-Accepted

Ahead-of-Print / Just-Accepted

Band 27 (2023)

Band 26 (2022)

Band 25 (2021)

Band 24 (2020)

Band 23 (2019)

Band 22 (2018)

Band 21 (2017)

Band 20 (2016)

Band 19 (2015)

Band 18 (2014)

Band 17 (2013)

Band 16 (2012)

Band 15 (2011)

Band 14 (2010)

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Band 3 (1999)

Band 2 (1998)

Band 1 (1997)

Journal Impact Factor	0.8	2022, Journal Citation Reports (Clarivate, 2023)
5-year Journal Impact Factor	0.9	2022, Journal Citation Reports (Clarivate, 2023)
Journal Citation Indicator	0.29	2022, Journal Citation Reports (Clarivate, 2023)
CiteScore	1.4	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.467	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	0.721	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)
Mathematical Citation Quotient	0.06

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Editorial

Editor-in-Chief

Bruce Mizrach, Rutgers University

Associate Editors
Heather Anderson, Monash University
Robert A. Becker, Indiana University
Hilde C. Bjørnland, BI Norwegian Business School
Francesco Ravazzolo, University of Bolzano
Yoosoon Chang, Indiana University
Robert de Jong, Ohio State University
Cees Diks, CeNDEF, U. of Amsterdam
Alvaro Escribano, Universidad Carlos III de Madrid
Giancarlo Gandolfo, CIDEI, Universitá di Roma "La Sapienza"
René Garcia, EDHEC Business School
Massimo Guidolin, Bocconi University
Christian Hafner, Université catholique de Louvain
Gary Koop, University of Strathclyde
Dimitris Korobilis, University of Glasgow
Tae-Hwy Lee, University of California-Riverside
Vance Martin, University of Melbourne
Stefan Mittnik, University of Munich
James Morley, University of New South Wales
Kazuo Nishimura, Kyoto University
Michael T. Owyang, Assistant Vice President, Federal Reserve Bank of St. Louis
Jeremy Piger, University of Oregon
Philip Rothman, East Carolina University
Tatevik Sekhposyan, Texas A&M University
Martin Sola, Birkbeck College
Gerhard Sorger, University of Vienna
Ruey Tsay, University of Chicago
Dick van Dijk, Erasmus University
Anastasios Xepapadeas, Athens University of Economics and Business

Advisory Panel

Jess Benhabib, New York University
William A. Brock, University of Wisconsin-Madison
Jean-Michel Grandmont, CREST-CNRS
James D. Hamilton, University of California-San Diego
Jose Scheinkman, Princeton University

(English)

Online ISSN: 1558-3708
Typ: Zeitschrift
Sprache: Englisch
Verlag: De Gruyter
Erstveröffentlichung: 1. April 1996
Erscheinungsweise: 5 Hefte pro Jahr
Zielgruppe: Researchers and professionals in the field of econometrics, economic and financial markets