Example of Journal of Time Series Econometrics format
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Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format
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Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format Example of Journal of Time Series Econometrics format
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Journal of Time Series Econometrics — Template for authors

Publisher: De Gruyter
Categories Rank Trend in last 3 yrs
Economics and Econometrics #575 of 661 down down by 11 ranks
journal-quality-icon Journal quality:
Low
calendar-icon Last 4 years overview: 32 Published Papers | 15 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/07/2020
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Journal Performance & Insights

CiteRatio

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

A measure of average citations received per peer-reviewed paper published in the journal.

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.5

25% from 2019

CiteRatio for Journal of Time Series Econometrics from 2016 - 2020
Year Value
2020 0.5
2019 0.4
2018 0.3
2017 0.1
graph view Graph view
table view Table view

0.169

19% from 2019

SJR for Journal of Time Series Econometrics from 2017 - 2020
Year Value
2020 0.169
2019 0.142
2018 0.323
2017 0.236
graph view Graph view
table view Table view

0.391

28% from 2019

SNIP for Journal of Time Series Econometrics from 2017 - 2020
Year Value
2020 0.391
2019 0.543
2018 0.291
2017 0.68
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 25% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

insights Insights

  • SJR of this journal has increased by 19% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 28% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Time Series Econometrics

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De Gruyter

Journal of Time Series Econometrics

Approved by publishing and review experts on SciSpace, this template is built as per for Journal of Time Series Econometrics formatting guidelines as mentioned in De Gruyter author instructions. The current version was created on and has been used by 272 authors to write and format their manuscripts to this journal.

Economics

Mathematics

i
Last updated on
17 Jul 2020
i
ISSN
1941-1928
i
Sherpa RoMEO Archiving Policy
Yellow faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
C. W. J. Beenakker. Specular andreev reflection in graphene. Phys. Rev. Lett., 97(6):067007, 2006.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.2202/1941-1928.1097
Evaluating Automatic Model Selection

Abstract:

We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orth... We outline a range of criteria for evaluating model selection approaches that have been used in the literature. Focusing on three key criteria, we evaluate automatically selecting the relevant variables in an econometric model from a large candidate set. General-to-specific selection is outlined for a regression model in orthogonal variables, where only one decision is required to select, irrespective of the number of regressors. Comparisons with an automated model selection algorithm, Autometrics (Doornik, 2009), show similar properties, but not restricted to orthogonal cases. Monte Carlo experiments examine the roles of post-selection bias corrections and diagnostic testing as well as evaluate selection in dynamic models by costs of search versus costs of inference. read more read less

Topics:

Model selection (64%)64% related to the paper, Selection (genetic algorithm) (59%)59% related to the paper, Regression analysis (50%)50% related to the paper
View PDF
127 Citations
open accessOpen access Journal Article DOI: 10.2202/1941-1928.1032
The PCSE Estimator is Good - Just Not as Good As You Think

Abstract:

This paper investigates the properties of the Panel-Corrected Standard Error (PCSE) estimator. The PCSE estimator is commonly used when working with time-series, cross-sectional (TSCS) data. In an influential paper, Beck and Katz (1995) (henceforth BK) demonstrated that FGLS produces coefficient standard errors that are sever... This paper investigates the properties of the Panel-Corrected Standard Error (PCSE) estimator. The PCSE estimator is commonly used when working with time-series, cross-sectional (TSCS) data. In an influential paper, Beck and Katz (1995) (henceforth BK) demonstrated that FGLS produces coefficient standard errors that are severely underestimated. They report Monte Carlo experiments in which the PCSE estimator produces accurate standard error estimates at no or little loss in efficiency compared to FGLS. Our study further investigates the properties of the PCSE estimator. We first reproduce the main experimental results of BK using their Monte Carlo framework. We then show that the PCSE estimator does not perform as well when tested in data environments that better resemble practical research situations. When (i) the explanatory variable(s) are characterized by substantial persistence, (ii) there is serial correlation in the errors, and (iii) the time span of the data series is relatively short, coverage rates for the PCSE estimator frequently fall between 80 and 90 percent. Further, we find many practical research situations where the PCSE estimator compares poorly with FGLS on efficiency grounds. read more read less

Topics:

Estimator (54%)54% related to the paper
View PDF
81 Citations
Journal Article DOI: 10.2202/1941-1928.1014
Selecting instrumental variables in a data rich environment
Serena Ng1, Jushan Bai2

Abstract:

Practitioners often have at their disposal a large number of instruments that are weakly exogenous for the parameter of interest. However, not every instrument has the same predictive power for the endogenous variable, and using too many instruments can induce bias. We consider two ways of handling these problems. The rst is ... Practitioners often have at their disposal a large number of instruments that are weakly exogenous for the parameter of interest. However, not every instrument has the same predictive power for the endogenous variable, and using too many instruments can induce bias. We consider two ways of handling these problems. The rst is to form principal components from the observed instruments, and the second is to reduce the number of instruments by subset variable selection. For the latter, we consider boosting, a method that does not require an a priori ordering of the instruments. We also suggest a way to pre-order the instruments and then screen the instruments using the goodness of t of the rst stage regression and information criteria. Using these methods to form smaller sets of instruments from the observed data or their principal components, we nd that the principal components are often better instruments than the observed data. The exception is when the number of relevant instruments is small. Of the three methods for selecting instruments, no single method dominates. But a hard-thresholding method based on thet test generally yields estimates with small biases and small root-mean-squared errors. read more read less

Topics:

Instrumental variable (51%)51% related to the paper
View PDF
69 Citations
open accessOpen access Journal Article DOI: 10.2202/1941-1928.1080
Noncausal Autoregressions for Economic Time Series

Abstract:

This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical liter... This paper is concerned with univariate noncausal autoregressive models and their potential usefulness in economic applications. In these models, future errors are predictable, indicating that they can be used to empirically approach rational expectations models with nonfundamental solutions. In the previous theoretical literature, nonfundamental solutions have typically been represented by noninvertible moving average models. However, noncausal autoregressive and noninvertible moving average models closely approximate each other, and therefore,the former provide a viable and practically convenient alternative. We show how the parameters of a noncausal autoregressive model can be estimated by the method of maximum likelihood and derive related test procedures. Because noncausal autoregressive models cannot be distinguished from conventional causal autoregressive models by second order properties or Gaussian likelihood, a model selection procedure is proposed. As an empirical application, we consider modeling the U.S. inflation which, according to our results, exhibits purely forward-looking dynamics. read more read less

Topics:

SETAR (64%)64% related to the paper, Autoregressive integrated moving average (64%)64% related to the paper, STAR model (64%)64% related to the paper, Autoregressive model (57%)57% related to the paper, Model selection (51%)51% related to the paper
View PDF
52 Citations
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Frequently asked questions

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3. Can I cite my article in multiple styles in Journal of Time Series Econometrics?

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Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Time Series Econometrics that you can download at the end.

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13. What is Sherpa RoMEO Archiving Policy for Journal of Time Series Econometrics?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Time Series Econometrics. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Time Series Econometrics?

The 5 most common citation types in order of usage for Journal of Time Series Econometrics are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download Journal of Time Series Econometrics in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Time Series Econometrics Endnote style according to Elsevier guidelines.

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