nep-ets New Economics Papers
on Econometric Time Series
Issue of 2018‒05‒07
four papers chosen by
Yong Yin
SUNY at Buffalo

  1. Models with Multiplicative Decomposition of Conditional Variances and Correlations By Cristina Amado; Annastiina Silvennoinen; Timo Ter¨asvirta
  2. Nonstationary cointegration in the fractionally cointegrated VAR model By Søren Johansen; Morten Ørregaard Nielsen
  3. Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts By Jonas Dovern; Hans Manner
  4. Model-based forecast adjustment; with an illustration to inflation By Franses, Ph.H.B.F.

  1. By: Cristina Amado (University of Minho and NIPE, CREATES and Aarhus University); Annastiina Silvennoinen (School of Economics and Finance, Queensland University of Technology); Timo Ter¨asvirta (CREATES and Aarhus University, C.A.S.E., Humboldt-Universit¨at zu Berlin)
    Abstract: Univariate and multivariate GARCH type models with multiplicative decomposition of the variance to short and long run components are surveyed. The latter component can be either deterministic or stochastic. Examples of both types are studied.
    Keywords: Conditional heteroskedasticity; Deterministically varying correlations; Multiplicative decomposition; Nonstationary volatility
    JEL: C12 C32 C51 C52
    Date: 2018
  2. By: Søren Johansen (University of Copenhagen and CREATES); Morten Ørregaard Nielsen (Queen's University and CREATES)
    Abstract: We consider the fractional cointegrated vector autoregressive (CVAR) model of Johansen and Nielsen (2012a) and make two distinct contributions. First, in their consistency proof, Johansen and Nielsen (2012a) imposed moment conditions on the errors that depend on the parameter space, such that when the parameter space is larger, stronger moment conditions are required. We show that these moment conditions can be relaxed, and for consistency we require just eight moments regardless of the parameter space. Second, Johansen and Nielsen (2012a) assumed that the cointegrating vectors are stationary, and we extend the analysis to include the possibility that the cointegrating vectors are nonstationary. Both contributions require new analysis and results for the asymptotic properties of the likelihood function of the fractional CVAR model, which we provide. Finally, our analysis follows recent research and applies a parameter space large enough that the usual (non-fractional) CVAR model constitutes an interior point and hence can be tested against the fractional model using a χ²-test.
    Keywords: cointegration, fractional integration, likelihood inference, vector autoregressive model
    JEL: C32
    Date: 2018–05
  3. By: Jonas Dovern (Alfred-Weber-Institute for Economics, Heidelberg University); Hans Manner (University of Graz, Austria)
    Abstract: Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to established approaches. We use the tests to evaluate GARCH-based multivariate density forecasts for a vector of stock market returns.
    Keywords: Density calibration; Goodness-of-fit test; Predictive density; Rosenblatt transformation
    JEL: C12 C32 C52 C53
    Date: 2018–04
  4. By: Franses, Ph.H.B.F.
    Abstract: This paper introduces the idea to adjust forecasts from a linear time series model where the adjustment relies on the assumption that this linear model is an approximation of for example a nonlinear time series model. This way to create forecasts can be convenient when inference for the nonlinear model is impossible, complicated or unreliable in small samples. The size of the forecast adjustment can be based on the estimation results for the linear model and on other data properties like the first few moments or autocorrelations. An illustration is given for an ARMA(1,1) model which is known to approximate a first order diagonal bilinear time series model. For this case, the forecast adjustment is easy to derive, which is convenient as the particular bilinear model is indeed cumbersome to analyze. An application to a range of inflation series for low income countries shows that such adjustment can lead to improved forecasts, although the gain is not large nor frequent
    Keywords: ARMA(1, 1), Inflation, First-order diagonal bilinear time series model, Methods, of Moments, Adjustment of forecasts
    JEL: C22 C53
    Date: 2018–03–01

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