Journal of Time Series Analysis

1.7k papers and 45.7k indexed citations i.

About

The 1.7k papers published in Journal of Time Series Analysis in the last decades have received a total of 45.7k indexed citations. Papers published in Journal of Time Series Analysis usually cover Finance (824 papers), Statistics and Probability (759 papers) and Economics and Econometrics (543 papers) specifically the topics of Financial Risk and Volatility Modeling (786 papers), Statistical Methods and Inference (476 papers) and Monetary Policy and Economic Impact (364 papers). The most active scholars publishing in Journal of Time Series Analysis are Clive W. J. Granger, T. Subba Rao, G. J. Janacek, Roselyne Joyeux, John Geweke, Susan Porter‐Hudak, Anindya Banerjee, Sylvia Frühwirth‐Schnatter, Juan J. Dolado and Ricardo Mestre.

In The Last Decade

Fields of papers published in Journal of Time Series Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Journal of Time Series Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Journal of Time Series Analysis.

Countries where authors publish in Journal of Time Series Analysis

Since Specialization
Citations

This map shows the geographic impact of research published in Journal of Time Series Analysis. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Journal of Time Series Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Journal of Time Series Analysis more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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