Daniel Dzikowski, Carsten Jentsch
arXiv 25 Jan 2024 · Econometrics · publishedJournal of Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2401.14545 · PDF · DOI · OpenAlex · Extracted main text
While seasonality inherent to raw macroeconomic data is commonly removed by seasonal adjustment techniques before it is used for structural inference, this may distort valuable information in the data. As an alternative method to commonly used structural vector autoregressions (SVARs) for seasonally adjusted data, we propose to model potential periodicity in seasonally unadjusted (raw) data directly by structural periodic vector autoregressions (SPVARs). This approach does not only allow for periodically time-varying intercepts, but also for periodic autoregressive parameters and innovations variances. As this larger flexibility leads to an increased number of parameters, we propose linearly constrained estimation techniques. Moreover, based on SPVARs, we provide two novel identification schemes and propose a general framework for impulse response analyses that allows for direct consideration of seasonal patterns. We provide asymptotic theory for SPVAR estimators and impulse responses under flexible linear restrictions and introduce a test for seasonality in impulse responses. For the construction of confidence intervals, we discuss several residual-based (seasonal) bootstrap methods and prove their bootstrap consistency under different assumptions. A real data application shows that useful information about the periodic structure in the data may be lost when relying on common seasonal adjustment methods.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ursu \ Duchesne (2009) `On modelling and diagnostic checking of vector periodic autoregressive time series models', Journal of Time Series Analysis 30(… | 1.000 | 10 | 4 | 100% |
| 2 | Brüggemann, Jentsch \ Trenkler (2016) `Inference in VARs with conditional heteroskedasticity of unknown form', Journal of Econometrics 191(1), 69–85 | 1.000 | 5 | 3 | 100% |
| 3 | Jentsch \ Lunsford (2022) `Asymptotically valid bootstrap inference for proxy SVARs', Journal of Business & Economic Statistics 40(4), 1876–1891 | 0.941 | 6 | 4 | 83% |
| 4 | Boubacar Manassara \ Ursu (2023) `Estimating weak periodic vector autoregressive time series', TEST 32, 958–997 | 0.874 | 7 | 2 | 100% |
| 5 | Bertail \ Dudek (2024) `Optimal choice of bootstrap block length for periodically correlated time series', Bernoulli 30(3), 2521–2545 | 0.737 | 3 | 3 | 67% |
| 6 | Franses \ Paap (2004) Periodic time series models, Oxford University Press | 0.737 | 3 | 2 | 100% |
| 7 | Kilian \ Lütkepohl (2017) Structural vector autoregressive analysis, Cambridge University Press | 0.737 | 3 | 2 | 100% |
| 8 | Jentsch \ Lunsford (2019) `The dynamic effects of personal and corporate income tax changes in the United States: Comment', American Economic Review 109(7… | 0.644 | 2 | 2 | 100% |
| 9 | Lütkepohl (2005) New introduction to multiple time series analysis, Springer Science & Business Media | 0.644 | 2 | 2 | 100% |
| 10 | Franses (1996) `Recent advances in modelling seasonality', Journal of Economic Surveys 10(3), 299–345 | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 60 scored citations.