{
  "schema": "econometrics-research-register-v1",
  "reviewed": "2026-10-09",
  "scope": "Primary research anchors and proposed reproducible study paths. The listed paper replications have not been executed in this project. The current course has its own separately verified calculations and Texas case.",
  "records": [
    {"id":"forecast","citation":"Hyndman and Khandakar (2008)","title":"Automatic Time Series Forecasting: The forecast Package for R","doi":"10.18637/jss.v027.i03","paper":"https://www.jstatsoft.org/article/view/v027i03","software":"https://www.jstatsoft.org/article/view/v027i03","laboratory":"weather","question":"Can a proposed forecasting model beat automatic ARIMA, ETS, and seasonal naive forecasts?","condition":"Intervals and forecasts depend on the selected model and innovation specification.","failure":"Change seasonality or introduce a break; evaluate only at chronological origins.","replication":"Start with the journal's archived R script and package. Compare current-package output separately.","extension":"A break-aware combination evaluated on frozen future origins.","paper_rights":"JSS open-access policy; inspect the selected artifact before reuse.","code_rights":"Selected archive license not yet inspected.","data_rights":"Selected input-series terms not yet inspected.","status":"Replication planned"},
    {"id":"kfas","citation":"Helske (2017)","title":"KFAS: Exponential Family State Space Models in R","doi":"10.18637/jss.v078.i10","paper":"https://www.jstatsoft.org/article/view/v078i10","software":"https://github.com/helske/KFAS","laboratory":"signals","question":"How should a noisy measurement change an estimate of an evolving state?","condition":"The observation and transition models determine which latent quantity is estimable.","failure":"Confound state variation with measurement noise; add missing readings and outliers.","replication":"Run the archived example, then compare a small Gaussian filter's likelihood and moments in three languages.","extension":"An online innovation-scale adaptation with a measured false-alarm cost.","paper_rights":"JSS open-access policy; inspect the selected artifact before reuse.","code_rights":"Selected package-version license not yet inspected.","data_rights":"Example-data terms not yet inspected.","status":"Replication planned"},
    {"id":"strucchange","citation":"Zeileis, Leisch, Hornik, and Kleiber (2002)","title":"strucchange: An R Package for Testing for Structural Change in Linear Regression Models","doi":"10.18637/jss.v007.i02","paper":"https://www.jstatsoft.org/article/view/v007i02","software":"https://stat.ethz.ch/CRAN/web/packages/strucchange/refman/strucchange.html","laboratory":"signals","question":"Is a changed relationship distinguishable from sampling fluctuation?","condition":"Test calibration depends on the regression and disturbance assumptions; dating and monitoring answer different questions.","failure":"Use stable-model critical values after introducing dependent errors or nonstationary regressors.","replication":"Separate the archived 2002 examples from functionality added in later package versions.","extension":"A forecast adaptation that uses only online break evidence.","paper_rights":"JSS open-access policy; inspect the selected artifact before reuse.","code_rights":"Selected package-version license not yet inspected.","data_rights":"Example-data terms not yet inspected.","status":"Replication planned"},
    {"id":"ccr","citation":"Park (1992)","title":"Canonical Cointegrating Regressions","doi":null,"paper":"https://bashtage.github.io/arch/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html","software":"https://github.com/bashtage/arch","laboratory":"signals","question":"When can trending variables have a stable long-run relation?","condition":"Integration orders, stable cointegration, and long-run covariance regularity matter.","failure":"Compare unrelated random walks, then change a genuine cointegrating coefficient.","replication":"Validate the documented CCR transform on a known simulated system; this is method verification, not a historical empirical-table replication.","extension":"Changing long-run energy-demand relations with measurement error.","paper_rights":"Original article access and reuse not established; the linked source is primary software documentation citing the article.","code_rights":"Selected arch-version license not yet inspected.","data_rights":"Proposed fixture would be original synthetic data.","status":"Method verification planned"},
    {"id":"hamilton-filter","citation":"Hamilton (2018; working paper 2017)","title":"Why You Should Never Use the Hodrick-Prescott Filter","doi":null,"paper":"https://www.nber.org/papers/w23429","software":"https://www.nber.org/papers/w23429","laboratory":"signals","question":"Which apparent dynamics come from a filter?","condition":"A detrending rule defines a measured object; it does not reveal a uniquely true latent cycle.","failure":"Filter a random walk and compare endpoint revisions and induced autocorrelation.","replication":"Inspect the author appendix before attempting reproduction. Automated access to the NBER record returned HTTP 403 in this pass.","extension":"A forecast-origin revision score reported alongside predictive accuracy.","paper_rights":"Article republication rights not established.","code_rights":"Appendix license not yet inspected.","data_rights":"Appendix input terms not yet inspected.","status":"Acquisition pending"},
    {"id":"spatial-software","citation":"Bivand and Piras (2015)","title":"Comparing Implementations of Estimation Methods for Spatial Econometrics","doi":"10.18637/jss.v063.i18","paper":"https://www.jstatsoft.org/article/view/v063i18","software":"https://www.jstatsoft.org/article/view/v063i18","laboratory":"geography","question":"Why do nominally identical spatial models differ across programs?","condition":"Weights, normalization, ordering, and impact definitions are part of the specification.","failure":"Reorder observations without reordering weights or interpret a lag coefficient as the full marginal effect.","replication":"Reproduce one archived county specification before comparing current R and PySAL versions and an independent Julia calculation.","extension":"Propagate uncertainty about spatial exposure into estimated impacts.","paper_rights":"JSS open-access policy; inspect the selected artifact before reuse.","code_rights":"Each selected software component needs its own license record.","data_rights":"County-source and archive terms not yet inspected.","status":"Replication planned"},
    {"id":"wavelets","citation":"Torrence and Compo (1998)","title":"A Practical Guide to Wavelet Analysis","doi":null,"paper":"https://atoc.colorado.edu/research/wavelets/bams_79_01_0061.pdf","software":"https://github.com/ct6502/wavelets","laboratory":"weather","question":"Does an oscillation persist or appear during particular periods?","condition":"Significance uses a declared background-noise model; boundary effects and multiple comparisons remain relevant.","failure":"Compare red-noise apparent cycles with the cone of influence and a prespecified search.","replication":"Start with the supplied ENSO example and original routines before porting.","extension":"Forecast features selected only within training histories.","paper_rights":"Author-hosted manuscript; general republication rights not established.","code_rights":"MIT verified in https://raw.githubusercontent.com/ct6502/wavelets/main/LICENSE on 2026-10-09; retain notices and pin an execution commit.","data_rights":"ENSO input terms not yet inspected.","status":"Replication planned"},
    {"id":"peak-demand","citation":"Hyndman and Fan (2010)","title":"Density Forecasting for Long-Term Peak Electricity Demand","doi":"10.1109/TPWRS.2009.2036017","paper":"https://robjhyndman.com/papers/peak_electricity_demand.pdf","software":"https://tsibbledata.tidyverts.org/reference/vic_elec.html","laboratory":"weather","question":"What uncertainty disappears when a forecast uses one weather trajectory?","condition":"Weather, economic scenarios, and residual resampling must match the target horizon.","failure":"Shuffle weather hours or admit realized future temperature as though it were known.","replication":"Original input access is unresolved. A vic_elec exercise would be a labelled new-data reconstruction, not reproduction of the original study.","extension":"Regime-sensitive demand uncertainty with coherent weather blocks.","paper_rights":"Author-hosted manuscript; general republication rights not established.","code_rights":"No original replication bundle inspected.","data_rights":"vic_elec provenance is documented; redistribution terms and demand-unit convention must be checked before acquisition.","status":"New-data reconstruction planned"},
    {"id":"causalimpact","citation":"Brodersen, Gallusser, Koehler, Remy, and Scott (2015)","title":"Inferring causal impact using Bayesian structural time-series models","doi":null,"paper":"https://kaybrodersen.github.io/publications/Brodersen_2015_AOAS.pdf","software":"https://github.com/google/CausalImpact","laboratory":"geography","question":"When does a post-intervention forecast error identify an effect?","condition":"Controls must be unaffected by treatment and their relation with the treated outcome must remain stable.","failure":"Contaminate controls with spillovers or add a coincident unrelated break.","replication":"Run the official R examples and a known-effect simulation; port only a declared common model.","extension":"Sensitivity to control contamination and loading drift.","paper_rights":"Author-hosted manuscript; general republication rights not established.","code_rights":"Apache-2.0 verified in https://raw.githubusercontent.com/google/CausalImpact/master/LICENSE on 2026-10-09.","data_rights":"Selected example-data terms not yet inspected.","status":"Replication planned"},
    {"id":"did","citation":"Callaway and Sant'Anna (2021)","title":"Difference-in-Differences with Multiple Time Periods","doi":"10.1016/j.jeconom.2020.12.001","paper":"https://arxiv.org/abs/1803.09015","software":"https://bcallaway11.github.io/did/","laboratory":"geography","question":"Which effects are averaged under staggered adoption?","condition":"Specify group-time targets, comparison groups, parallel trends, overlap, and anticipation restrictions.","failure":"Introduce spillovers or heterogeneous dynamics and compare with a conventional two-way fixed-effects coefficient.","replication":"Run the documented mpdta example. Do not invent adjacency where locations are unavailable.","extension":"Exposure-sensitive comparisons with the changed target population reported.","paper_rights":"Public preprint; selected manuscript reuse terms not yet inspected.","code_rights":"GPL-3 verified in https://raw.githubusercontent.com/bcallaway11/did/master/DESCRIPTION on 2026-10-09.","data_rights":"mpdta redistribution terms not yet inspected.","status":"Replication planned"},
    {"id":"doubleml","citation":"Bach, Chernozhukov, Kurz, and Spindler (2022)","title":"DoubleML—An Object-Oriented Implementation of Double Machine Learning in Python","doi":null,"paper":"https://www.jmlr.org/papers/v23/21-0862.html","software":"https://github.com/DoubleML/doubleml-for-py","laboratory":"geography","question":"How can flexible prediction support parameter estimation?","condition":"Orthogonality and cross-fitting do not supply identification, overlap, or an appropriate dependence framework.","failure":"Randomly split dependent observations or predict accurately in a confounded design.","replication":"Start with known-truth examples and identical folds before a simple independent Julia port.","extension":"Cross-fitting for a precisely specified dependent sampling design, after a focused literature review.","paper_rights":"Open JMLR article; inspect the selected artifact before reuse.","code_rights":"MIT reported in the preliminary source review; selected execution-commit and license readback remain pending.","data_rights":"Selected fixture terms not yet inspected.","status":"Method verification planned"},
    {"id":"enbpi","citation":"Xu and Xie (2021)","title":"Conformal prediction interval for dynamic time-series","doi":null,"paper":"https://proceedings.mlr.press/v139/xu21h.html","software":"https://github.com/hamrel-cxu/EnbPI","laboratory":"weather","question":"How useful are prediction intervals under dependent errors?","condition":"Approximate marginal coverage depends on assumptions, including the error dependence conditions; it is not exact conditional coverage for arbitrary series.","failure":"Increase dependence, change variance, and measure local coverage.","replication":"Pin the maintained author code rather than assuming the supplementary archive is identical.","extension":"Weather-regime calibration with a declared effective sample requirement.","paper_rights":"Open proceedings article; inspect the selected artifact before reuse.","code_rights":"MIT verified in https://raw.githubusercontent.com/hamrel-cxu/EnbPI/main/LICENSE on 2026-10-09.","data_rights":"Bundled-data terms not yet inspected.","status":"Replication planned"},
    {"id":"spci","citation":"Xu and Xie (2023)","title":"Sequential Predictive Conformal Inference for Time Series","doi":null,"paper":"https://proceedings.mlr.press/v202/xu23r.html","software":"https://github.com/hamrel-cxu/SPCI-code","laboratory":"weather","question":"Can residual predictability improve interval calibration?","condition":"Asymptotic conditional coverage is tied to consistency conditions for residual-quantile prediction.","failure":"Misspecify that predictor or shorten calibration history.","replication":"Inspect and run tutorial_electric_EnbPI_SPCI.ipynb with a pinned source snapshot.","extension":"Adapt residual quantiles using only break information available at the forecast origin.","paper_rights":"Open proceedings article; inspect the selected artifact before reuse.","code_rights":"Explicit reuse license not established.","data_rights":"Bundled electricity-data terms not yet inspected.","status":"Acquisition and replication planned"},
    {"id":"dtaci","citation":"Gibbs and Candès (2024)","title":"Conformal Inference for Online Prediction with Arbitrary Distribution Shifts","doi":null,"paper":"https://www.jmlr.org/papers/v25/22-1218.html","software":"https://github.com/isgibbs/DtACI","laboratory":"weather","question":"How quickly should calibration forget earlier errors?","condition":"Regret and empirical coverage-frequency claims differ from pointwise conditional coverage.","failure":"Compare abrupt and gradual variance changes, interval widths, and adaptation delays.","replication":"Reproduce a simulation before the financial application and retain the original bootstrap conventions.","extension":"A forecast-origin-available adaptation signal compared with unchanged DtACI and SPCI.","paper_rights":"Open JMLR article; inspect the selected artifact before reuse.","code_rights":"Explicit reuse license not established.","data_rights":"Financial-data redistribution terms not yet inspected.","status":"Acquisition and replication planned"},
    {"id":"proper-scores","citation":"Gneiting and Raftery (2007)","title":"Strictly Proper Scoring Rules, Prediction, and Estimation","doi":"10.1198/016214506000001437","paper":"https://sites.stat.washington.edu/people/raftery/Research/PDF/Gneiting2007jasa.pdf","software":null,"laboratory":"weather","question":"How can evaluation reward honest uncertainty?","condition":"Scoring a full distribution and scoring an interval are distinct tasks; coverage alone does not reward sharpness.","failure":"Compare narrow under-covering intervals with very wide intervals.","replication":"Implement interval score and sample CRPS independently and check analytic toy distributions; this is an original exercise, not a paper replication.","extension":"Decision-relevant demand evaluation that retains proper scoring incentives.","paper_rights":"Author-hosted manuscript; general republication rights not established.","code_rights":"Independent original teaching code planned.","data_rights":"Original synthetic fixtures planned.","status":"Computational verification planned"},
    {"id":"msgarch","citation":"Ardia, Bluteau, Boudt, Catania, and Trottier (2019)","title":"Markov-Switching GARCH Models in R: The MSGARCH Package","doi":"10.18637/jss.v091.i04","paper":"https://www.jstatsoft.org/article/view/v091i04","software":"https://www.jstatsoft.org/article/view/v091i04","laboratory":"signals","question":"Does a high-volatility episode require another regime?","condition":"Regime identification, innovation tails, and numerical convergence require separate checks.","failure":"Fit switching models to heavy-tailed single-regime data and retain failed fits.","replication":"Run archived R materials before comparing independently checked single-regime baselines.","extension":"Parametric risk versus adaptive calibration during declared variance breaks.","paper_rights":"JSS open-access policy; inspect the selected artifact before reuse.","code_rights":"Selected package/archive license not yet inspected.","data_rights":"Financial-data redistribution terms not yet inspected.","status":"Replication planned"}
  ]
}