Francesco Angelini, Massimiliano Castellani, Simone Giannerini, Greta Goracci
arXiv 1 Aug 2023 · Econometrics · publishedOxford Bulletin of Economics and Statistics (2024) · 2 citations (OpenAlex)
arXiv:2308.00444 · PDF · DOI · OpenAlex · Extracted main text
Many macroeconomic time series are characterised by nonlinearity both in the conditional mean and in the conditional variance and, in practice, it is important to investigate separately these two aspects. Here we address the issue of testing for threshold nonlinearity in the conditional mean, in the presence of conditional heteroskedasticity. We propose a supremum Lagrange Multiplier approach to test a linear ARMA-GARCH model against the alternative of a TARMA-GARCH model. We derive the asymptotic null distribution of the test statistic and this requires novel results since the difficulties of working with nuisance parameters, absent under the null hypothesis, are amplified by the non-linear moving average, combined with GARCH-type innovations. We show that tests that do not account for heteroskedasticity fail to achieve the correct size even for large sample sizes. Moreover, we show that the TARMA specification naturally accounts for the ubiquitous presence of measurement error that affects macroeconomic data. We apply the results to analyse the time series of Italian strikes and we show that the TARMA-GARCH specification is consistent with the relevant macroeconomic theory while capturing the main features of the Italian strikes dynamics, such as asymmetric cycles and regime-switching.
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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 | K.-S. Chan, S. Giannerini, G. Goracci, and H. Tong (2002) Testing for threshold regulation in presence of measurement error with an application to the PPP hypothesis, 2020 | 1.000 | 5 | 3 | 100% |
| 2 | G. Goracci, S. Giannerini, K.-S. Chan, and H. Tong (2023) Testing for threshold effects in the TARMA framework | 0.843 | 4 | 4 | 75% |
| 3 | J. Godard (2011) What has happened to strikes? | 0.843 | 3 | 3 | 100% |
| 4 | G. Li and W.K. Li (2008) Testing for threshold moving average with conditional heteroscedasticity | 0.811 | 4 | 2 | 100% |
| 5 | C.S. Wong and W.K. Li (1997) Testing for threshold autoregression with conditional heteroscedasticity | 0.737 | 4 | 3 | 50% |
| 6 | S. Ng and P. Perron Lag length selection and the construction of unit root tests with good size and power | 0.644 | 4 | 1 | 100% |
| 7 | G. Li and W.K. Li (2011) Testing a linear time series model against its threshold extension | 0.644 | 3 | 2 | 67% |
| 8 | D.W.K. Andrews (2003) Tests for parameter instability and structural change with unknown change point: A corrigendum | 0.644 | 2 | 2 | 100% |
| 9 | K.-S. Chan and G. Goracci (2019) On the ergodicity of first-order threshold autoregressive moving-average processes | 0.644 | 2 | 2 | 100% |
| 10 | G. Goracci (2021) An empirical study on the parsimony and descriptive power of TARMA models | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 54 scored citations.