nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒01‒14
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts By Krueger, Fabian; Clark, Todd E.; Ravazzolo, Francesco
  2. Improving the Finite Sample Performance of Tests for a Shift in Mean By YAMAZAKI, Daisuke; KUROZUMI, Eiji
  3. Nonlinear GARCH model and 1/f noise By Aleksejus Kononovicius; Julius Ruseckas
  4. The Fluke Of Stochastic Volatility Versus Garch Inevitability : Which Model Creates Better Forecasts? By Valeria V. Lakshina

  1. By: Krueger, Fabian (Heidelburg Institute for Theoretical Studies); Clark, Todd E. (Federal Reserve Bank of Cleveland); Ravazzolo, Francesco (Norges Bank and the BI Norwegian Business School)
    Abstract: This paper shows entropic tilting to be a flexible and powerful tool for combining medium-term forecasts from BVARs with short-term forecasts from other sources (nowcasts from either surveys or other models). Tilting systematically improves the accuracy of both point and density forecasts, and tilting the BVAR forecasts based on nowcast means and variances yields slightly greater gains in density accuracy than does just tilting based on the nowcast means. Hence entropic tilting can offer—more so for persistent variables than not-persistent variables—some benefits for accurately estimating the uncertainty of multi-step forecasts that incorporate nowcast information.
    Keywords: Forecasting; Prediction; Bayesian Analysis
    JEL: C11 C53 E17
    Date: 2015–01–07
  2. By: YAMAZAKI, Daisuke; KUROZUMI, Eiji
    Abstract: It is widely known that structural break tests based on the long-run variance estimator, which is estimated under the alternative, suffer from serious size distortion when the errors are serially correlated. In this paper, we propose bias-corrected tests for a shift in mean by correcting the bias of the long-run variance estimator up to O(1/T). Simulation results show that the proposed tests have good size and high power.
    Keywords: structural change, long-run variance, bias correction
    JEL: C12 C22
    Date: 2014–11–10
  3. By: Aleksejus Kononovicius; Julius Ruseckas
    Abstract: Auto-regressive conditionally heteroskedastic (ARCH) family models are still used, by practitioners in business and economic policy making, as a conditional volatility forecasting models. Furthermore ARCH models still are attracting an interest of the researchers. In this contribution we consider the well known GARCH(1,1) process and its nonlinear modifications, reminiscent of NGARCH model. We investigate the possibility to reproduce power law statistics, probability density function and power spectral density, using ARCH family models. For this purpose we derive stochastic differential equations from the GARCH processes in consideration. We find the obtained equations to be similar to a general class of stochastic differential equations known to reproduce power law statistics. We show that linear GARCH(1,1) process has power law distribution, but its power spectral density is Brownian noise-like. However, the nonlinear modifications exhibit both power law distribution and power spectral density of the power law form, including 1/f noise.
    Date: 2014–12
  4. By: Valeria V. Lakshina (National Research University Higher School)
    Abstract: The paper proposes the thorough investigation of the in-sample and out-of-sample performance of four GARCH and two stochastic volatility models, which were estimated based on Russian financial data. The data includes Aeroflot and Gazprom’s stock prices, and the rouble against the US dollar exchange rates. In our analysis, we use the probability integral transform for the in-sample comparison, and a Mincer-Zarnowitz regression, along with classical forecast performance measures, for the out-of-sample comparison. Studying both the explanatory and the forecasting power of the models analyzed, we came to the conclusion that stochastic volatility models perform equally or in some cases better than GARCH models.
    Keywords: GARCH, stochastic volatility, markov switching multifractal, forecast performance.
    JEL: C01 C58 C51 G17
    Date: 2014

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