nep-ets New Economics Papers
on Econometric Time Series
Issue of 2021‒01‒18
five papers chosen by
Jaqueson K. Galimberti
Auckland University of Technology

  1. Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions By Knorre, Fabian; Wagner, Martin; Grupe, Maximilian
  2. Smooth Robust Multi-Horizon Forecasts By Andrew B. Martinez; Jennifer L. Castle; David F. Hendry
  3. Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility By Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
  4. Nowcasting in a pandemic using non-parametric mixed frequency VARs By Huber, Florian; Koop, Gary; Onorante, Luca; Pfarrhofer, Michael; Schreiner, Josef
  5. How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters By Paul Ho; Thomas A. Lubik; Christian Matthes

  1. By: Knorre, Fabian (TU Dortmund University, Germany and Ruhr Graduate School in Economics Essen, Germany); Wagner, Martin (University of Klagenfurt, Austria, and Bank of Slovenia, Ljubljana, Slovenia, and Institute for Advanced Studies, Vienna, Austria); Grupe, Maximilian (TU Dortmund University, Germany)
    Abstract: This paper develops residual-based monitoring procedures for cointegrating polynomial regressions, i. e., regression models including deterministic variables, integrated processes as well as integer powers of integrated processes as regressors. The regressors are allowed to be endogenous and the stationary errors are allowed to be serially correlated. We consider five variants of monitoring statistics and develop the results for three modified least squares estimators for the parameters of the CPRs. The simulations show that using the combination of self-normalization and a moving window leads to the best performance. We use the developed monitoring statistics to assess the structural stability of environmental Kuznets curves (EKCs) for both CO2and SO2 emissions for twelve industrialized country since the first oil price shock.
    Keywords: Cointegrating Polynomial Regression, Environmental Kuznets Curve, Monitoring, Structural Change
    JEL: C22 C52 Q56
    Date: 2020–12
  2. By: Andrew B. Martinez (Office of Macroeconomic Analysis, US Department of the Treasury); Jennifer L. Castle (Magdalen College, Climate Econometrics, and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford); David F. Hendry (Nuffield College, Climate Econometrics, and Institute for New Economic Thinking at the Oxford Martin School, University of Oxford)
    Abstract: We investigate whether smooth robust methods for forecasting can help mitigate pronounced and persistent failure across multiple forecast horizons. We demonstrate that naive predictors are interpretable as local estimators of the long-run relationship with the advantage of adapting quickly after a break, but at a cost of additional forecast error variance. Smoothing over naive estimates helps retain these advantages while reducing the costs, especially for longer forecast horizons. We derive the performance of these predictors after a location shift, and confirm the results using simulations. We apply smooth methods to forecasts of U.K. productivity and U.S. 10-year Treasury yields and show that they can dramatically reduce persistent forecast failure exhibited by forecasts from macroeconomic models and professional forecasters.
    Keywords: Location Shifts; Long differencing; Productivity forecasts; Robust forecasts
    JEL: C51 C53
    Date: 2020–12
  3. By: Takahashi, Makoto; Watanabe, Toshiaki; Omori, Yasuhiro
    Abstract: This paper compares the volatility predictive abilities of some time-varying volatility models such as thestochastic volatility (SV) and exponential GARCH (EGARCH) models using daily returns, the heterogeneous au-toregressive (HAR) model using daily realized volatility (RV) and the realized SV (RSV) and realized EGARCH(REGARCH) models using the both. The data are the daily return and RV of Dow Jones Industrial Aver-age (DJIA) in US and Nikkei 225 (N225) in Japan. All models are extended to accommodate the well-knownphenomenon in stock markets of a negative correlation between today's return and tomorrow's volatility. Weestimate the HAR model by the ordinary least squares (OLS) and the EGARCH and REGARCH models bythe quasi-maximum likelihood (QML) method. Since it is not straightforward to evaluate the likelihood of theSV and RSV models, we apply a Bayesian estimation via Markov chain Monte Carlo (MCMC) to them. Byconducting predictive ability tests and analyses based on model confidence sets, we confirm that the models us-ing RV outperform the models without RV, that is, the RV provides useful information on forecasting volatility.Moreover, we find that the realized SV model performs best and the HAR model can compete with it. Thecumulative loss analysis suggests that the differences of the predictive abilities among the models are partlycaused by the rise of volatility.
    Keywords: Exponential GARCH (EGARCH) model, Heterogeneous autoregressive (HAR) model, Markov chain Monte Carlo (MCMC), Realized volatility, Stochastic volatility, Volatility forecasting
    JEL: C11 C22 C53 C58 G17
    Date: 2021–01
  4. By: Huber, Florian; Koop, Gary; Onorante, Luca; Pfarrhofer, Michael; Schreiner, Josef
    Abstract: This paper develops Bayesian econometric methods for posterior inference in non-parametric mixed frequency VARs using additive regression trees. We argue that regression tree models are ideally suited for macroeconomic nowcasting in the face of extreme observations, for instance those produced by the COVID-19 pandemic of 2020. This is due to their flexibility and ability to model outliers. In an application involving four major euro area countries, we find substantial improvements in nowcasting performance relative to a linear mixed frequency VAR. JEL Classification: C11, C32, C53, E37
    Keywords: Bayesian, macroeconomic forecasting, regression tree models, vector autoregressions
    Date: 2021–01
  5. By: Paul Ho; Thomas A. Lubik; Christian Matthes
    Abstract: This paper estimates a panel model with endogenously time-varying parameters for COVID-19 cases and deaths in U.S. states. The functional form for infections incorporates important features of epidemiological models but is flexibly parameterized to capture different trajectories of the pandemic. Daily deaths are modeled as a spike-and-slab regression on lagged cases. The paper's Bayesian estimation reveals that social distancing and testing have significant effects on the parameters. For example, a 10 percentage point increase in the positive test rate is associated with a 2 percentage point increase in the death rate among reported cases. The model forecasts perform well, even relative to models from epidemiology and statistics.
    Keywords: Bayesian Estimation; Panel; Time-Varying Parameters
    JEL: C32 C51
    Date: 2020–08–21

This nep-ets issue is ©2021 by Jaqueson K. Galimberti. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.