arXiv 23 Oct 2020 · Econometrics · publishedENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (2020)
arXiv:2010.12263 · PDF · DOI · OpenAlex · Extracted main text
This paper considers forecasts of the growth and inflation distributions of the United Kingdom with factor-augmented quantile autoregressions under a model averaging framework. We investigate model combinations across models using weights that minimise the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Quantile Regression Information Criterion (QRIC) as well as the leave-one-out cross validation criterion. The unobserved factors are estimated by principal components of a large panel with N predictors over T periods under a recursive estimation scheme. We apply the aforementioned methods to the UK GDP growth and CPI inflation rate. We find that, on average, for GDP growth, in terms of coverage and final prediction error, the equal weights or the weights obtained by the AIC and BIC perform equally well but are outperformed by the QRIC and the Jackknife approach on the majority of the quantiles of interest. In contrast, the naive QAR(1) model of inflation outperforms all model averaging methodologies.
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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 | Lu, Xun, & Su, Liangjun (2015) Jackknife model averaging for quantile regressions | 0.811 | 4 | 2 | 100% |
| 2 | Adrian, Tobias, Boyarchenko, Nina, & Giannone, Domenico (2019) Vulnerable Growth | 0.737 | 3 | 2 | 100% |
| 3 | Buckland, S. T., Burnham, K. P., & Augustin, N. H (1997) Model Selection: An Integral Part of Inference | 0.737 | 3 | 2 | 100% |
| 4 | Kapetanios, George, & Labhard, Vincent (2008) Forecast combination and the Bank of England's suite of statistical forecasting models | 0.737 | 3 | 2 | 100% |
| 5 | Phella, Anthoulla (2020) Consistent Specification Test of the Quantile Autoregression self | 0.737 | 3 | 2 | 100% |
| 6 | Granger, Clive W. J (1996) Can we improve the perceived quality of economic forecasts? | 0.644 | 2 | 2 | 100% |
| 7 | Stock, James H., & Watson, Mark W (2002) Forecasting Using Principal Components from a Large Number of Predictors | 0.644 | 2 | 2 | 100% |
| 8 | Hansen, Bruce E (2007) Least Squares Model Averaging | 0.511 | 2 | 1 | 100% |
| 9 | Pasaogullari, Mehmet, & Meyer, Brent H (2010) Simple Ways to Forecast Inflation: What Works Best? | 0.511 | 2 | 1 | 100% |
| 10 | Akaike, Hirotugu (1970) Statistical predictor identification | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 63 scored citations.