thanatchon36/lockdowns-as-partial-holidays

Lockdowns as Partial Holidays: data and Monte Carlo results

The export values are simulated. Every series under simulated/ and every forecast and score under results/ come from a Monte Carlo study whose export values are generated. The drivers in data/ are real: the observed Thai holiday calendar, the OxCGRT stringency indices and the Google Community Mobility data. The one exception in data/ is e_cn, a stylised path of Chinese industrial stoppage (see below). Nothing here is a result on official Thai export statistics.

Data for the preprint Lockdowns as Partial Holidays: Identifying Work-Stoppage Elasticities from Recurring Holidays and Transferring Them to Forecast Thailand's Monthly Exports through the COVID-19 Shock by Thanatchon Chongmankhong (read online · PDF · ฉบับภาษาไทย). The paper treats a lockdown as a partial holiday. Recurring holidays (Thai public holidays including Songkran, and the Chinese New Year window) identify a work-stoppage elasticity through one shared regressor per channel in Prophet. During the pandemic the same regressor carries a dose, the Holiday-Equivalent Stoppage Index (HESI): the drop in workplace mobility on a day divided by the median drop on a weekday public holiday. Code: https://github.com/thanatchon36/lockdowns-as-partial-holidays; fitted models: thanatchon36/lockdowns-as-partial-holidays; archived on Zenodo: 10.5281/zenodo.24036954.

Independent personal research. Does not represent the views of any employer or institution.

Files

path	content
data/drivers_daily.csv	real, except e_cn. One row per day, 2010-01-01 to 2024-12-31 (5,479 rows): the calendar and every COVID driver the models use. Columns below
data/regressors_daily.csv	the shared stoppage regressors xD (domestic) and xP (partner) for each dose variant, long format (38,353 rows); variant sep adds the separate COVID regressors sD and sP
data/raw/oxcgrt_si.csv	real. OxCGRT national Stringency Index, Thailand and China, January 2020 to February 2023, in the source's wide layout (one column per day)
data/raw/gmob_thailand.csv	real. Google COVID-19 Community Mobility Reports, Thailand, national level, 15 February 2020 to 19 August 2022, % change against the baseline for six place categories
simulated/exports_daily_S1_seed0.csv	simulated. Daily export values of replication 0, scenario S1
simulated/exports_monthly_seed0.csv	simulated. Monthly export values of replication 0, scenarios S1 and S2, 2010-01 to 2024-12
results/forecast_paths_seed0.csv	simulated. Replication 0: monthly forecast and actual by experiment, scenario, model and dose variant (3,000 rows)
results/metrics.csv	simulated. All 30 replications: MAPE, RMSE and MASE by seed, scenario, experiment, model, variant and window, plus the dose sweep (27,690 rows)
results/betas.csv	simulated. Estimated domestic and partner elasticities beta_D, beta_P by seed, experiment, scenario, model and variant
results/lamhat.csv	simulated. Dose multiplier calibrated on March-April 2020, by seed, scenario and model
results/robust_metrics.csv, results/robust_betas.csv	simulated. Robustness experiments R1 (heterogeneous holidays: one-tier against two-tier festival specification) and R2 (centred against trailing, real-time HESI)
SHA256SUMS	checksums of every file above

data/drivers_daily.csv

column	meaning
H	1 on an observed Thai public holiday (day off), from the holidays package. Observed dates: Songkran 2020 was postponed and replaced by 27 July, 4 and 7 September 2020
C	1 in the Chinese New Year window, one day before to six days after (eight days)
weekend	1 on Saturday and Sunday
workplaces_pct	Google Mobility, workplaces, % change against the baseline (empty outside 15 Feb 2020 - 19 Aug 2022)
s_th, s_cn	OxCGRT Stringency Index / 100, Thailand and China
hesi_th	the Thai HESI in 0..1: the workplace-mobility drop divided by its median drop on weekday public holidays (0.47), smoothed over 7 days with holiday days left out
e_cn	stylised, not measured. Chinese industrial stoppage in Chinese-New-Year units: the 2020 national lockdown and the 2022 Shanghai lockdown. OxCGRT's China index stays high for three years under zero-COVID while factories mostly ran, so it is not used as a stoppage measure
bin_th, bin_cn	whole-month binary switches: 1 for every day of a month whose mean driver exceeds 0.70 (Thailand) or 0.50 (China)

Codes

• Scenarios: S1 ideal transfer, the HESI is the true export stoppage; S2 realistic, mobility overstates the true stoppage two-fold and a demand shock is present.
• Experiments: E0 normal times (forecasts of 2018-2019), E1 the shock (2020-2021, made in December 2019), E2 after the shock (2023-2024). R1, R2 robustness.
• Windows: W24 January 2020 - December 2021 (all 24 months), W20 May 2020 - December 2021 (after the March-April 2020 calibration months).
• Models: SNaive seasonal naive; Prophet-D, Prophet-M daily and monthly Prophet; HE with the shared stoppage regressors; +AC with the closed-form aggregation correction; SARIMAX; LightGBM-D, LightGBM-M.
• Variants: off no COVID dose; binary whole-month switch; stringency raw OxCGRT; hesi HESI dose with lambda = 1; hesi-cal dose calibrated on March-April 2020 (lambda in lam); oracle the true pass-through; lam the dose sweep, lambda in lam from 0 to 3; sep separate COVID regressors; mask the COVID window masked; cal holiday regressors only, COVID ignored; vanilla no calendar regressors; - not applicable.

Headline numbers

Daily Prophet, MAPE over 2020-2021 (exp == "E1", window == "W24", mean over 30 seeds): no dose 26.0% (S1) and 14.7% (S2); HESI dose 7.8% and 10.9%; oracle 7.8% and 8.3%. To reproduce:

from datasets import load_dataset

m = load_dataset("thanatchon36/lockdowns-as-partial-holidays", "metrics", split="train").to_pandas()
sel = m[(m.exp == "E1") & (m.window == "W24") & (m.model == "Prophet-D HE")]
print(sel.groupby(["scen", "variant"]).mape.mean().round(1).unstack(0))

make simulate robust in the code repository regenerates results/, and make dataset rebuilds these files.

Licence

LICENSE gives the terms. The simulated series, the derived regressors and all results are CC BY 4.0 (© 2026 Thanatchon Chongmankhong). data/raw/oxcgrt_si.csv and the s_th, s_cn columns come from the Oxford COVID-19 Government Response Tracker, CC BY 4.0; cite Hale et al. (2021). data/raw/gmob_thailand.csv and the workplaces_pct and hesi_th columns come from the Google COVID-19 Community Mobility Reports and remain under Google's terms; attribute "Google LLC, Google COVID-19 Community Mobility Reports".

Citation

@misc{chongmankhong2026lockdowns,
  author = {Chongmankhong, Thanatchon},
  title  = {Lockdowns as Partial Holidays: Identifying Work-Stoppage Elasticities from Recurring Holidays
            and Transferring Them to Forecast {Thailand}'s Monthly Exports through the {COVID-19} Shock},
  year   = {2026},
  doi    = {10.5281/zenodo.24036954},
  note   = {Preprint; export values are simulated},
  url    = {https://thanatchon36.github.io/lockdowns-as-partial-holidays/paper/}
}

Hale, T., Angrist, N., Goldszmidt, R., et al. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5, 529-538.