Testing for a Moderately Explosive Process with Structural Change in Drift
Bibliographic Data
| ID | 21512935 |
|---|---|
| Authors | Jingjie Xiang (0000-0003-1877-1383, School of Economics and Business Administration Central China Normal University Wuhan China), Gangzheng Guo (International Institute China Construction Bank Beijing China), Qing Zhao (0000-0003-2836-6090, School of Finance Dongbei University of Finance & Economics Dalian China, corresponding author) |
| Year | 2022 |
| Volume | 84 |
| Issue | 2 |
| Pages | 300-333 |
| Publication date | 2022-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Oxford Bulletin of Economics and Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0305-9049 • E-ISSN: 1468-0084 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/obes.12469 |
| OpenAlex | W3215781001 |
| Language | EN |
| References cited | 18 |
This paper studies large sample properties of a moderately explosive autoregression with a structural change in the unobservable drift term, and develops asymptotic tests for the null of moderate explosiveness under different dependence structures. When the innovation sequence is independently and identically distributed (i.i.d.), we show that the t statistic is asymptotically standard normal. When the innovations are weakly dependent in the form of homoskedasticity or conditional heteroskedasticity, we invoke the fixed‐smoothing asymptotics to construct the heteroskedasticity and autocorrelation robust standard error, under which the t statistic follows Student’s t distribution in large samples. Monte Carlo simulations show that our tests have small size distortion and high power in finite samples. As we impose no restrictions on the occurrence time and magnitude of the drift, our proposed asymptotic tests enjoy strong robustness and applicability
Asymptotic distribution · Autocorrelation · Econometrics · Estimator · Explosive material · Heteroscedasticity · Homoscedasticity · Monte Carlo method · Physics · Statistic · Statistical hypothesis testing · Statistical physics · Statistics · Test statistic · Unobservable · Advanced Statistical Process Monitoring · Market Dynamics and Volatility · Mathematics · Statistical Methods and Inference · Applied Mathematics
| Highly cited | No |
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