Marina Khismatullina, Michael Vogt
arXiv 22 Sep 2022 · Econometrics
arXiv:2209.10841 · PDF · Extracted main text
We develop new econometric methods for the comparison of nonparametric time trends. In many applications, practitioners are interested in whether the observed time series all have the same time trend. Moreover, they would often like to know which trends are different and in which time intervals they differ. We design a multiscale test to formally approach these questions. Specifically, we develop a test which allows to make rigorous confidence statements about which time trends are different and where (that is, in which time intervals) they differ. Based on our multiscale test, we further develop a clustering algorithm which allows to cluster the observed time series into groups with the same trend. We derive asymptotic theory for our test and clustering methods. The theory is complemented by a simulation study and two applications to GDP growth data and house pricing data.
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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 | Khismatullina, M. and Vogt, M (2020) Multiscale inference and long-run variance estimation in non-parametric regression with time series errors self | 1.000 | 8 | 5 | 100% |
| 2 | Zhang, Y., Su, L. and Phillips, P. C (2012) Testing for common trends in semi-parametric panel data models with fixed effects | 0.874 | 11 | 2 | 100% |
| 3 | Knoll, K., Schularick, M. and Steger, T (2017) No price like home: global house prices, 1870-2012 | 0.644 | 4 | 1 | 100% |
| 4 | Carlstein, E (1986) The use of subseries values for estimating the variance of a general statistic from a stationary sequence | 0.644 | 3 | 2 | 67% |
| 5 | Khismatullina, M. and Vogt, M (2021) Nonparametric comparison of epidemic time trends: the case of COVID-19 self | 0.585 | 3 | 1 | 100% |
| 6 | Wu, W. B. and Wu, Y. N (2016) Performance bounds for parameter estimates of high-dimensional linear models with correlated errors | 0.511 | 3 | 2 | 33% |
| 7 | Berkes, I., Liu, W. and Wu, W. B (2014) Komlós-Major-Tusnády approximation under dependence | 0.511 | 2 | 2 | 50% |
| 8 | Nazarov, F (2003) On the maximal perimeter of a convex set in $^n$ with respect to a Gaussian measure | 0.511 | 2 | 2 | 50% |
| 9 | Wu, W. B. and Zhao, Z (2007) Inference of trends in time series | 0.511 | 2 | 2 | 50% |
| 10 | Churchill, S. A., Baako, K. T., Mintah, K. and Zhang, Q (2021) Transport infrastructure and house prices in the long run | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 53 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Multiscale Comparison of Nonparametric Trending Coefficients | 0.851 | 13 | 8 |