Econometric Time Series
http://lists.repec.org/mailman/listinfo/nep-ets
Econometric Time Series
2021-01-18
Monitoring Cointegrating Polynomial Regressions: Theory and Application to the Environmental Kuznets Curves for Carbon and Sulfur Dioxide Emissions
http://d.repec.org/n?u=RePEc:ihs:ihswps:27&r=ets
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.
Knorre, Fabian
Wagner, Martin
Grupe, Maximilian
Cointegrating Polynomial Regression, Environmental Kuznets Curve, Monitoring, Structural Change
2020-12
Smooth Robust Multi-Horizon Forecasts
http://d.repec.org/n?u=RePEc:gwc:wpaper:2020-009&r=ets
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.
Andrew B. Martinez
Jennifer L. Castle
David F. Hendry
Location Shifts; Long differencing; Productivity forecasts; Robust forecasts
2020-12
Forecasting Daily Volatility of Stock Price Index Using Daily Returns and Realized Volatility
http://d.repec.org/n?u=RePEc:hit:hiasdp:hias-e-104&r=ets
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.
Takahashi, Makoto
Watanabe, Toshiaki
Omori, Yasuhiro
Exponential GARCH (EGARCH) model, Heterogeneous autoregressive (HAR) model, Markov chain Monte Carlo (MCMC), Realized volatility, Stochastic volatility, Volatility forecasting
2021-01
Nowcasting in a pandemic using non-parametric mixed frequency VARs
http://d.repec.org/n?u=RePEc:ecb:ecbwps:20212510&r=ets
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
Huber, Florian
Koop, Gary
Onorante, Luca
Pfarrhofer, Michael
Schreiner, Josef
Bayesian, macroeconomic forecasting, regression tree models, vector autoregressions
2021-01
How To Go Viral: A COVID-19 Model with Endogenously Time-Varying Parameters
http://d.repec.org/n?u=RePEc:fip:fedrwp:88807&r=ets
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.
Paul Ho
Thomas A. Lubik
Christian Matthes
Bayesian Estimation; Panel; Time-Varying Parameters
2020-08-21