nep-ets New Economics Papers
on Econometric Time Series
Issue of 2017‒11‒12
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. A state space approach to evaluate multi-horizon forecasts By Thomas Goodwin; Jing Tian
  2. Optimal Dimension Reduction for High-dimensional and Functional Time Series By Marc Hallin; Siegfried Hörmann; Marco Lippi
  3. Estimating dynamic panel models: backing out the Nickell Bias By Hausman, Jerry A.; Pinkovskiy, Maxim L.
  4. Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory By Asai, M.; McAleer, M.J.; Peiris, S.
  5. Joint tests of contagion with applications to financial crises By Renée Fry-McKibbin; Cody Yu-Ling Hsiao; Vance L. Martin
  6. Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors By Todd E Clark; Michael W McCracken; Elmar Mertens
  7. Functional central limit theorems for rough volatility By Blanka Horvath; Antoine Jacquier; Aitor Muguruza

  1. By: Thomas Goodwin; Jing Tian
    Abstract: We propose a state space modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. We derive the conditions under which forecasts that contain error that is irrelevant to the target can still present the second moment bounds of rational forecasts. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decision.
    Keywords: Rational forecasts, implicit forecasts, forecast revision structure, weather forecasts.
    JEL: C32 C53
    Date: 2017–11
  2. By: Marc Hallin; Siegfried Hörmann; Marco Lippi
    Abstract: Dimension reduction techniques are at the core of the statistical analysis of high-dimensional and functional observations. Whether the data are vector- or function-valued, principal component techniques, in this context, play a central role. The success of principal components in the dimension reduction problem is explained by the fact that, for any K
    Keywords: dimension reduction; time series; principal components; functional principal components; dynamic principal components; Karhunen-Loève expansion
    Date: 2017–11
  3. By: Hausman, Jerry A. (MIT); Pinkovskiy, Maxim L. (Federal Reserve Bank of New York)
    Abstract: We propose a novel estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the first-order conditions as a function of the autoregressive parameter and solving the resulting equation. We show that this estimator performs well as compared with approaches in current use. We also propose a general method for including predetermined variables in fixed-effects panel regressions that appears to perform well.
    Keywords: dynamic panel data; bias correction; econometrics
    JEL: C2 C23 C26
    Date: 2017–10–01
  4. By: Asai, M.; McAleer, M.J.; Peiris, S.
    Abstract: In recent years fractionally differenced processes have received a great deal of attention due to their exibility in nancial applications with long memory. In this paper, we develop a new re- alized stochastic volatility (RSV) model with general Gegenbauer long memory (GGLM), which encompasses a new RSV model with seasonal long memory (SLM). The RSV model uses the infor- mation from returns and realized volatility measures simultaneously. The long memory structure of both models can describe unbounded peaks apart from the origin in the power spectrum. For estimating the RSV-GGLM model, we suggest estimating the location parameters for the peaks of the power spectrum in the rst step, and the remaining parameters based on the Whittle likelihood in the second step. We conduct Monte Carlo experiments for investigating the nite sample properties of the estimators, with a quasi-likelihood ratio test of RSV-SLM model against theRSV-GGLM model. We apply the RSV-GGLM and RSV-SLM model to three stock market indices. The estimation and forecasting results indicate the adequacy of considering general long memory.
    Keywords: Stochastic Volatility, Realized Volatility Measure, Long Memory, Gegenbauer Poly-nomial, Seasonality, Whittle Likelihood
    JEL: C18 C21 C58
    Date: 2017–11–01
  5. By: Renée Fry-McKibbin; Cody Yu-Ling Hsiao; Vance L. Martin
    Abstract: Joint tests of contagion are derived which are designed to have power where contagion operates simultaneously through coskewness, cokurtosis and covolatility. Finite sample properties of the new tests are evaluated and compared with existing tests of contagion that focus on a single channel. Applying the tests to daily Eurozone equity returns from 2005 to 2014 shows that contagion operates through higher order moment channels during the GFC and the European debt crisis, which are not necessarily detected by traditional tests based on correlations.
    Keywords: Coskewness, Cokurtosis, Covolatility, Lagrange multiplier tests, European financial crisis, equity markets.
    JEL: C1 F3
    Date: 2017–10
  6. By: Todd E Clark; Michael W McCracken; Elmar Mertens
    Abstract: We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon speci cation of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
    Keywords: stochastic volatility, survey forecasts, fan charts
    JEL: E37 C53
    Date: 2017–10
  7. By: Blanka Horvath; Antoine Jacquier; Aitor Muguruza
    Abstract: We extend Donsker's approximation of Brownian motion to fractional Brownian motion with Hurst exponent $H \in (0,1)$ and to Volterra-like processes. Some of the most relevant consequences of our `rough Donsker (rDonsker) Theorem' are convergence results for discrete approximations of a large class of rough models. This justifies the validity of simple and easy-to-implement Monte-Carlo methods, for which we provide detailed numerical recipes. We test these against the current benchmark Hybrid scheme [BLP15] and find remarkable agreement (for a large range of values of~$H$). This rDonsker Theorem further provides a weak convergence proof for the Hybrid scheme itself, and allows to construct binomial trees for rough volatility models, the first available scheme (in the rough volatility context) for early exercise options such as American or Bermudan.
    Date: 2017–11

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