Pith Number

pith:FE6QBH6P

pith:2026:FE6QBH6PMHYHCFAEKUOQMRG6F2

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Double Descent and Benign Overfitting in Macroeconomic Forecasting

Andrea Carriero, Davide Pettenuzzo, Florian Huber

Augmenting macroeconomic datasets with synthetic copies from an estimated factor model produces an estimator that outperforms the Stock-Watson factor model for point forecasting across all series and horizons.

arxiv:2605.15358 v1 · 2026-05-14 · econ.EM

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Claims

C1strongest claim

Using monthly (FRED-MD) and quarterly (FRED-QD) US data, the resulting estimator consistently outperforms the Stock-Watson factor model for point forecasting across all series and horizons, with gains that are pervasive, statistically significant, and increasing with the forecast horizon.

C2weakest assumption

The conditions of Bartlett et al. (2020) can hold under the approximate factor model provided idiosyncratic variances are not too dispersed across series; this dispersion assumption is required for the benign-overfitting mechanism to operate after data augmentation.

C3one line summary

Augmenting macro panels with synthetic factor-model copies creates a factor-structured kernel ridge regressor that outperforms the Stock-Watson benchmark in point forecasts, with gains rising at longer horizons.

References

24 extracted · 24 resolved · 0 Pith anchors

[1] Bai, J. and Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1):191--221 2002

[2] Bai, J. and Ng, S. (2008). Forecasting economic time series using targeted predictors. Journal of Econometrics, 146(2):304--317 2008

[3] Bartlett, P. L., Long, P. M., Lugosi, G., and Tsigler, A. (2020). Benign overfitting in linear regression. Proceedings of the National Academy of Sciences, 117(48):30063--30070 2020

[4] Belkin, M., Hsu, D., Ma, S., and Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proceedings of the National Academy of Sciences, 116(32):158 2019

[5] Boot, T. and Nibbering, D. (2019). Forecasting using random subspace methods. Journal of Econometrics, 209(2):391--406 2019

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:54.240012Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

293d009fcf61f0711404551d0644de2eb6ca26aa10a7a49267b1c3f9f26acf2e

Aliases

arxiv: 2605.15358 · arxiv_version: 2605.15358v1 · doi: 10.48550/arxiv.2605.15358 · pith_short_12: FE6QBH6PMHYH · pith_short_16: FE6QBH6PMHYHCFAE · pith_short_8: FE6QBH6P

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FE6QBH6PMHYHCFAEKUOQMRG6F2 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 293d009fcf61f0711404551d0644de2eb6ca26aa10a7a49267b1c3f9f26acf2e

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fefd44ce5551b751cad0f8f09b74c84c8f08e5b9adc529407c5929572ec33a4c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "econ.EM",
    "submitted_at": "2026-05-14T19:38:40Z",
    "title_canon_sha256": "9b194988d269043b594d077661d560f595277555bf6928d220c21e012f833f7c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.15358",
    "kind": "arxiv",
    "version": 1
  }
}