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on Econometric Time Series |
| By: | Lucchetti, Riccardo; Valentini, Francesco |
| Abstract: | Giraitis, Kapetanios, and Marcellino (Journal of Econometrics, 2020) proposed a kernel-based time-varying coefficients IV estimator. By using entirely different code, We broadly replicate the simulation results and the empirical application on the Phillips Curve but we note that a small coding mistake might have affected some of the reported results. Further, we extend the results by using a different sample and many kernel functions; we find that the estimator is remarkably robust across a wide range of smoothing choices, but the effect of outliers may be less obvious than expected. |
| Keywords: | Instrumental variables, Time-varying parameters, Hausman test, Phillips curve |
| JEL: | C14 C26 C51 |
| Date: | 2021–10–06 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:110033&r= |
| By: | Dissanayake, Pushpa; Flock, Teresa; Meier, Johanna; Sibbertsen, Philipp |
| Abstract: | The peaks-over-threshold (POT) method has a long tradition in modelling extremes in environmental variables. However, the assumption of independently and identically distributed (iid) data is likely to be violated in practical settings, leading to clustering of high-threshold exceedances. These violations can be the result of short- and long-term dependencies in the underlying time series. We review popular approaches that either focus on modelling short- or long-range dynamics explicitly. In particular, we consider conditional POT variants and the Mittag-Leffler distribution modelling waiting times between exceedances. Further, we propose a two-step approach capturing both short- and long-range correlations simultaneously. We suggest the autoregressive fractionally integrated moving average (ARFIMA)-POT model, which first fits an ARFIMA model to the original series and then utilises a classical POT model for the residuals. Applying these models to an oceanographic time series of significant wave heights measured on the Sefton coast (UK), we find that neither solely modelling short- nor long-range dependencies satisfactorily explains the clustering of extremes. The ARFIMA-POT approach however provides a significant improvement in terms of model fit. We therefore conclude that there is a need for developing new models that jointly incorporate short- and long-range dependence to address extremal clustering. |
| Keywords: | peaks-over-threshold; extremal clustering; long-range dependence; ARFIMA models; extreme value theory; significant wave heights; Sefton coast |
| JEL: | C22 C52 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-690&r= |
| By: | Dante Amengual (CEMFI, Centro de Estudios Monetarios y Financieros); Gabriele Fiorentini (Università di Firenze and RCEA); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros) |
| Abstract: | We propose the information matrix test to assess the constancy of mean and variance parameters in vector autoregressions. We additively decompose it into several orthogonal components: conditional heteroskedasticity and asymmetry of the innovations, and their unconditional skewness and kurtosis. Our Monte Carlo simulations explore both its finite size properties and its power against i.i.d. coefficients, persistent but stationary ones, and regime switching. Our procedures detect variation in the autoregressive coefficients and residual covariance matrix of a VAR for the US GDP growth rate and the statistical discrepancy, but they fail to detect any covariation between those two sets of coefficients. |
| Keywords: | GDP, GDI, Hessian matrix, information matrix test, outer product of the score. |
| JEL: | C32 C52 E01 |
| Date: | 2021–09 |
| URL: | http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2021_2108&r= |
| By: | Shijia Song; Handong Li |
| Abstract: | Under the framework of dynamic conditional score, we propose a parametric forecasting model for Value-at-Risk based on the normal inverse Gaussian distribution (Hereinafter NIG-DCS-VaR), which creatively incorporates intraday information into daily VaR forecast. NIG specifies an appropriate distribution to return and the semi-additivity of the NIG parameters makes it feasible to improve the estimation of daily return in light of intraday return, and thus the VaR can be explicitly obtained by calculating the quantile of the re-estimated distribution of daily return. We conducted an empirical analysis using two main indexes of the Chinese stock market, and a variety of backtesting approaches as well as the model confidence set approach prove that the VaR forecasts of NIG-DCS model generally gain an advantage over those of realized GARCH (RGARCH) models. Especially when the risk level is relatively high, NIG-DCS-VaR beats RGARCH-VaR in terms of coverage ability and independence. |
| Date: | 2021–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2110.02492&r= |