Guides manuscripts through the complete submission pipeline for 《数量经济技术经济研究》 (JQTE), from fit assessment, measurement design, and reproducibility checks to policy writing, submission preflight, and R&R rebuttals, enforcing the journal's standards for quantitative economics and forecasting.
Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Use when the empirical core of a 《数量经济技术经济研究》 (JQTE) manuscript is an econometric model — time series, cointegration, mixed-frequency, VAR/SVAR, state-space, or panel / macro-econometrics. Enforces correct model setup, stationarity / unit-root and cointegration diagnostics, and lag/specification justification. Use when the method itself is the contribution rather than a causal identification claim.
Use to judge whether a manuscript fits 《数量经济技术经济研究》 (JQTE) and to decide how to frame its contribution — measurement, forecasting, or method-application versus clean causal inference — before investing in revision. The most common save: stopping a strong measurement paper from being dressed up as a weak causal study, and routing genuinely clean-causal work elsewhere.
Use when a 《数量经济技术经济研究》 (JQTE) manuscript makes a forecasting or prediction claim — macro forecasting, business-cycle / sentiment indices, mixed-frequency nowcasting, or model-based prediction. Enforces genuine out-of-sample evaluation (RMSE / MAE / directional accuracy / Diebold-Mariano), a proper benchmark, and a recursive / rolling design. The fastest desk-reject here is reporting in-sample fit only.
Use when writing the policy / practice implications of a 《数量经济技术经济研究》 (JQTE) manuscript — translating measurement, forecasting, or decomposition results into concrete guidance for planning, forecasting, industrial, or technology decisions. JQTE expects a substantive policy section (≥ 1000 characters, except pure-method papers) grounded in the paper's own quantitative findings.
Use when a 《数量经济技术经济研究》 (JQTE) manuscript is built on an input-output table, a CGE model, or a structural decomposition (SDA). Enforces explicit data sources, parameter calibration, closure rules, scenario design, and end-to-end reproducibility. The standard reject here is a black-box model run whose setup cannot be reconstructed.
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横跨经管社科 · 人文社科 · 自然科学 · 临床医学 · AI 计算机等多个主流学科,为每一本期刊 / 每一个会议单独编码它的投稿工作流。
🧭 布局指南 · 📚 Skill Pack 一览 · ⚡ 如何使用 · 🗺 路线图 · 🌐 English
先看期刊,再进 Pack。点击任意封面即可进入对应的期刊 Skill 包。
🆕 四个最新学科广度合集 —— 工程技术 40 · 农业环境 30 · 临床医学 30 · 英文人文 36;点击封面进入合集页。
其他合集 · 中文体育学 · 12 本 CSSCI 体育学来源刊
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