nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒12‒19
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. Modeling and Forecasting Volatility – How Reliable are modern day approaches? By Mehta, Anirudh; Kanishka, Kunal
  2. Variable Selection in Predictive MIDAS Models By C. Marsilli
  3. Generalized Dynamic Factor Models and Volatilities. Recovering the Market Volatility Shocks By Matteo Barigozzi; Marc Hallin
  5. Outlier detection in structural time series models: The indicator saturation approach By Marczak, Martyna; Proietti, Tommaso

  1. By: Mehta, Anirudh; Kanishka, Kunal
    Abstract: This study explores the volatility models and evaluates the quality of one-step ahead forecasts of volatility constructed by (1) GARCH, (2) TGARCH, (3) Risk metrics and (4) Historical volatility. Volatility forecasts suggest that TGARCH performs relatively best in term of MSPE, followed by GARCH, Risk metrics and historical volatility. In terms of VaR, we test for correct unconditional coverage and index- Dependence of violations using Likelihood Ratio tests. The tests suggest that VaR forecasts at 90 % and 95% have desirable properties. Regarding 99% VaR forecasts, We find significant evidence that suggests none of the models can reliably predict at this confidence level.
    Keywords: Asset pricing, Volatility Forecasting, GARCH, T-GARCH, Risk metrics, LR ratio, VaR
    JEL: C10 C12 C15 C19 C51 C53 C58
    Date: 2014–11–08
  2. By: C. Marsilli
    Abstract: In short-term forecasting, it is essential to take into account all available information on the current state of the economic activity. Yet, the fact that various time series are sampled at different frequencies prevents an efficient use of available data. In this respect, the Mixed-Data Sampling (MIDAS) model has proved to outperform existing tools by combining data series of different frequencies. However, major issues remain regarding the choice of explanatory variables. The paper first addresses this point by developing MIDAS based dimension reduction techniques and by introducing two novel approaches based on either a method of penalized variable selection or Bayesian stochastic search variable selection. These features integrate a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Then the developed techniques are assessed with regards to their forecasting power of US economic growth during the period 2000-2013 using jointly daily and monthly data. Our model succeeds in identifying leading indicators and constructing an objective variable selection with broad applicability.
    Keywords: Forecasting, Mixed frequency data, MIDAS, Variable selection, GDP.
    JEL: C53 E37
    Date: 2014
  3. By: Matteo Barigozzi; Marc Hallin
    Keywords: volatility; dynamic factor models; block structure
    JEL: C32
    Date: 2014–11
  4. By: Gabriele Fiorentini (Università di Firenze); Enrique Sentana (CEMFI, Centro de Estudios Monetarios y Financieros)
    Abstract: We derive computationally simple and intuitive score tests of neglected serial correlation in unobserved component univariate models using frequency domain techniques. In some common situations in which the information matrix is singular under the null we derive extremum tests that are asymptotically equivalent to likelihood ratio tests, which become one-sided, and explain how to compute reliable Wald tests. We also explicitly relate the incidence of those problems to the model identification conditions and compare our tests with tests based on the reduced form prediction errors. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests.
    Keywords: Extremum tests, Kalman filter, LM tests, singular information matrix, spectral maximum likelihood, Wiener-Kolmogorov filter.
    JEL: C22 C52 C12
    Date: 2014–10
  5. By: Marczak, Martyna; Proietti, Tommaso
    Abstract: Structural change affects the estimation of economic signals, like the underlying growth rate or the seasonally adjusted series. An important issue, which has at- tracted a great deal of attention also in the seasonal adjustment literature, is its detection by an expert procedure. The general-to-specific approach to the detection of structural change, currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse-and step-indicator saturation, we investigate via Monte Carlo simulations how this approach performs for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to detect additive outliers and level shifts in the industrial production series in five European countries.
    Keywords: indicator saturation,seasonal adjustment,structural time series model,outliers,structural change,general-to-specific approach,state space model
    JEL: C22 C51 C53
    Date: 2014

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