nep-ets New Economics Papers
on Econometric Time Series
Issue of 2015‒08‒13
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. A GARCH Model for Testing Market Efficiency By Paresh K Narayan; Ruipeng Liu
  2. A Unit Root Model for Trending Time-series Energy Variables By Paresh K Narayan; Ruipeng Liu
  3. Prior selection for panel vector autoregressions By Dimitris Korobilis.
  4. Modeling Dependence Structure and Forecasting Market Risk with Dynamic Asymmetric Copula By Mario Cerrato; John Crosby; Minjoo Kim; Yang Zhao
  5. Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates By William Larson
  6. Individual and time effects in nonlinear panel models with large N, T By Ivan Fernandez-Val; Martin Weidner
  7. Structural VARs, deterministic and stochastic trends: Does detrending matter? By Benjamin Wong; Varang Wiriyawit

  1. By: Paresh K Narayan (Deakin University); Ruipeng Liu (Deakin University)
    Abstract: In this paper we propose a generalised autoregressive conditional heteroskedasticity (GARCH) model-based test for a unit root. The model allows for two endogenous structural breaks. We test for unit roots in 156 US stocks listed on the NYSE over the period 1980 to 2007. We find that the unit root null hypothesis is rejected in 40% of the stocks, and only in four out of the nine sectors the null is rejected for over 50% of stocks. We conclude with an economic significance analysis, showing that mostly stocks with mean reverting prices tend to outperform stocks with non-stationary prices.
    Keywords: Efficient Market Hypothesis; GARCH; Unit Root; Structural Break; Stock Price.
  2. By: Paresh K Narayan (Deakin University); Ruipeng Liu (Deakin University)
    Abstract: In this paper, we propose a GARCH-based unit root test that is flexible enough to account for; (a) trending variables, (b) two endogenous structural breaks, and (c) heteroskedastic data series. Our proposed model is applied to a range of time-series, trending, and heteroskedastic energy variables. Our two main findings are: first, the proposed trend-based GARCH unit root model outperforms a GARCH model without trend; and, second, allowing for a time trend and two endogenous structural breaks are important in practice, for doing so allows us to reject the unit root null hypothesis.
    Keywords: Time-series; Energy; Unit Root; Trending Variables.
  3. By: Dimitris Korobilis.
    Abstract: There is a vast literature that speciÖes Bayesian shrinkage priors for vector autoregressions (VARs) of possibly large dimensions. In this paper I argue that many of these priors are not appropriate for multi-country settings, which motivates me to develop priors for panel VARs (PVARs). The parametric and semi-parametric priors I suggest not only perform valuable shrinkage in large dimensions, but also allow for soft clustering of variables or countries which are homogeneous. I discuss the implications of these new priors for modelling interdependencies and heterogeneities among di§erent countries in a panel VAR setting. Monte Carlo evidence and an empirical forecasting exercise show clear and important gains of the new priors compared to existing popular priors for VARs and PVARs.
    Keywords: Bayesian model selection; shrinkage; spike and slab priors; forecasting; large vector autoregression
    JEL: C11 C33 C52
    Date: 2015–04
  4. By: Mario Cerrato; John Crosby; Minjoo Kim; Yang Zhao
    Abstract: We investigate the dynamic and asymmetric dependence structure between equity portfolios from the US and UK. We demonstrate the statistical significance of dynamic asymmetric copula models in modelling and forecasting market risk. First, we construct “high-minus-low" equity portfolios sorted on beta, coskewness, and cokurtosis. We find substantial evidence of dynamic and asymmetric de- pendence between characteristic-sorted portfolios. Second, we consider a dynamic asymmetric copula model by combining the generalized hyperbolic skewed t copula with the generalized autoregressive score (GAS) model to capture both the multivariate non-normality and the dynamic and asymmetric dependence between equity portfolios. We demonstrate its usefulness by evaluating the forecasting performance of Value-at-Risk and Expected Shortfall for the high-minus-low portfolios. From back- testing, we find consistent and robust evidence that our dynamic asymmetric copula model provides the most accurate forecasts, indicating the importance of incorporating the dynamic and asymmetric dependence structure in risk management.
    Keywords: asymmetry, tail dependence, dependence dynamics, dynamic skewed t copulas, VaR and ES forecasting
    JEL: C32 C53 G17 G32
    Date: 2015–02
  5. By: William Larson (Federal Housing Finance Agency)
    Abstract: There is a debate in the literature on the best method to forecast an aggregate: (1) forecast the aggregate directly, (2) forecast the disaggregates and then aggregate, or (3) forecast the aggregate using disaggregate information. This paper contributes to this debate by suggesting that in the presence of moderate-sized structural breaks in the disaggregates, approach (2) is preferred because of the low power to detect mean shifts in the disaggregates using models of aggregates. In support of this approach are two exercises. First, a simple Monte Carlo study demonstrates theoretical forecasting improvements. Second, empirical evidence is given using pseudo-ex ante forecasts of aggregate proven oil reserves in the United States.
    Keywords: Model selection; Intercept correction; Forecast robustification
    JEL: C52 C53 Q3
    Date: 2015–07
  6. By: Ivan Fernandez-Val (Institute for Fiscal Studies and University of Boston); Martin Weidner (Institute for Fiscal Studies and cemmap and UCL)
    Abstract: Fixed effects estimators of nonlinear panel data models can be severely biased because of the incidental parameter problem. We develop analytical and jackknife bias corrections for nonlinear models with both individual and time effects. Under asymptotic sequences where the time-dimension (T) grows with the cross-sectional dimension (N), the time effects introduce additional incidental parameter bias. As the existing bias corrections apply to models with only individual effects, we derive the appropriate corrections for the case when both effects are present. The basis for the corrections are general asymptotic expansions of fixed effects estimators with incidental parameters in multiple dimensions. We apply the expansions to conditional maximum likelihood estimators with concave objective functions in parameters for panel models with additively separable individual and time effects. These estimators cover fixed effects estimators of the most popular limited dependent variable models such as logit, probit, ordered probit, Tobit and Poisson models. Our analysis therefore extends the use of large-T bias adjustments to an important class of models. We also analyze the properties of fixed effects estimators of functions of the data, parameters and individual and time effects including average partial effects. Here, we uncover that the incidental parameter bias is asymptotically of second order, because the rate of the convergence of the fixed effects estimators is slower for average partial effects than for model parameters. The bias corrections are still effective to improve finite-sample properties. View the supplementary document for this paper here.
    Keywords: Panel data; nonlinear model; dynamic model; asymptotic bias correction; ï¬xed effects; time effects
    JEL: C13 C23
    Date: 2015–04
  7. By: Benjamin Wong; Varang Wiriyawit (Reserve Bank of New Zealand)
    Abstract: We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of biases in the estimated impulse response functions, we find the biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Misspecifying the trend can also distort impulse response functions of even the correctly detrended variable within the SVAR system. A possible solution hinted by our analysis is that increasing the lag order when estimating the SVAR may mitigate some of the biases associated with trend misspecification.
    Date: 2015–04

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