Browse reusable Agent Skills, each with a clear purpose and practical guidance.
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 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.
Use when the contribution of a 《数量经济技术经济研究》 (JQTE) manuscript is productivity / efficiency / index measurement — TFP, Malmquist, DEA, SFA, technical-progress decomposition, or index construction. Enforces transparent, reproducible construction plus sensitivity to method and parameter choices. This is the centerpiece skill for the journal's measurement track.
Use to sharpen a topic for 《数量经济技术经济研究》 (JQTE) — turning a vague empirical idea into a measurement / forecasting / method-application question with a clear quantitative object, a method-side novelty, and value for China's planning or technology decisions. Use before drafting, when the contribution shape is still fuzzy.
Use when identifying a risk or uncertainty parameter is the bottleneck for a Journal of Risk and Uncertainty (JRU) manuscript — incentive-compatible elicitation in an experiment, or structural/empirical estimation of risk preferences, VSL, or insurance demand. Stress-tests how the data pin the primitive; it does not invent evidence or citations.
Use when the spatial identification argument is the bottleneck for a Journal of Urban Economics (JUE) manuscript — boundary discontinuity, shift-share/Bartik, historical or geographic IV, place-based DiD, or mobility experiment. Stress-tests the spatial design against sorting, spillovers, and spatial-autocorrelation confounds before exhibits are finalized.
Use when a Journal of Urban Economics (JUE) manuscript's headline spatial estimate must be shown to survive specification, spatial-scale, sorting, spillover, and inference choices before submission or in an R&R. Builds the spatially-aware robustness suite a JUE referee expects; it does not establish the identification (jue-identification) or format the maps (jue-tables-figures).
Use when a Journal of Urban Economics (JUE) manuscript needs a spatial model to interpret its mechanism — a spatial-equilibrium frame, a sorting model, or a quantitative spatial model (QSM) for counterfactuals. Builds the theory that disciplines the empirics; it does not establish the identification (jue-identification) or run robustness (jue-robustness).
Use when deciding whether a question is a genuinely spatial, urban-economics contribution that fits the Journal of Urban Economics (JUE) rather than a sibling venue. Locks the spatial question and outlet fit before positioning; it does not write the literature review or design the identification.
Use when choosing or defending the data behind a 《世界经济》 (The Journal of World Economy) manuscript — customs micro data (firm-product-destination), cross-country panels, and GVC / input-output decomposition (KWW value-added, ADB / WIOD MRIO). Steers away from generic provincial panels toward internationally-credible sources.