nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒12‒29
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Lag Order and Critical Values of the Augmented Dickey-Fuller Test: A Replication By Kulaksizoglu, Tamer
  2. Modeling Covariance Breakdowns in Multivariate GARCH By Xin Jin; John M. Maheu
  3. True Limit Distributions of the Anderson-Hsiao IV Estimators in Panel Autoregression By Peter C.B. Phillips; Chirok Han
  4. Inference about Non-Identified SVARs By Giacomini, Raffaella; Kitagawa, Toru
  5. Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions By Stefan Bruder
  6. Testing the maximal rank of the volatility process for continuous diffusions observed with noise By Tobias Fissler; Mark Podolskij

  1. By: Kulaksizoglu, Tamer
    Abstract: This paper replicates Cheung and Lai (1995), who use response surface analysis to obtain approximate finite-sample critical values adjusted for lag order and sample size for the augmented Dickey-Fuller test. We obtain results that are quite close to their results. We provide the Ox source code. We also provide a Windows application with a graphical user interface, which makes obtaining custom critical values quite simple.
    Keywords: Finite-sample critical value; Monte Carlo; Response surface
    JEL: C12 C15
    Date: 2014–08–31
  2. By: Xin Jin (Shanghai University of Finance and Economics); John M. Maheu (DeGroote School of Business, McMaster University, Canada; L8S4M4 and University of Toronto, Canada; The Rimini Centre for Economic Analysis, Italy)
    Abstract: This paper proposes a flexible way of modeling dynamic heterogeneous covariance breakdowns in multivariate GARCH (MGARCH) models. During periods of normal market activity, volatility dynamics are governed by an MGARCH specification. A covariance breakdown is any significant temporary deviation of the conditional covariance matrix from its implied MGARCH dynamics. This is captured through a flexible stochastic component that allows for changes in the conditional variances, covariances and implied correlation coefficients. Different breakdown periods will have different impacts on the conditional covariance matrix and are estimated from the data. We propose an efficient Bayesian posterior sampling procedure for the estimation and show how to compute the marginal likelihood of the model. When applying the model to daily stock market and bond market data, we identify a number of different covariance breakdowns. Modeling covariance breakdowns leads to a significant improvement in the marginal likelihood and gains in portfolio choice.
    Date: 2014–11
  3. By: Peter C.B. Phillips (Cowles Foundation, Yale University); Chirok Han (Korea University)
    Abstract: This note derives the correct limit distributions of the Anderson Hsiao (1981) levels and differences instrumental variable estimators, provides comparisons showing that the levels IV estimator has uniformly smaller variance asymptotically as the cross section (n) and time series (T) sample sizes tend to infinity, and compares these results with those of the first difference least squares (FDLS) estimator.
    Keywords: Dynamic panel, IV estimation, Levels and difference instruments
    JEL: C23 C36
    Date: 2014–12
  4. By: Giacomini, Raffaella; Kitagawa, Toru
    Abstract: We propose a method for conducting inference on impulse responses in structural vector autoregressions (SVARs) when the impulse response is not point identified because the number of equality restrictions one can credibly impose is not sufficient for point identification and/or one imposes sign restrictions. We proceed in three steps. We first define the object of interest as the identified set for a given impulse response at a given horizon and discuss how inference is simple when the identified set is convex, as one can limit attention to the set's upper and lower bounds. We then provide easily verifiable conditions on the type of equality and sign restrictions that guarantee convexity. These cover most cases of practical interest, with exceptions including sign restrictions on multiple shocks and equality restrictions that make the impulse response locally, but not globally, identified. Second, we show how to conduct inference on the identified set. We adopt a robust Bayes approach that considers the class of all possible priors for the non-identified aspects of the model and delivers a class of associated posteriors. We summarize the posterior class by reporting the "posterior mean bounds", which can be interpreted as an estimator of the identified set. We also consider a "robustified credible region" which is a measure of the posterior uncertainty about the identified set. The two intervals can be obtained using a computationally convenient numerical procedure. Third, we show that the posterior bounds converge asymptotically to the identified set if the set is convex. If the identified set is not convex, our posterior bounds can be interpreted as an estimator of the convex hull of the identified set. Finally, a useful diagnostic tool delivered by our procedure is the posterior belief about the plausibility of the imposed identifying restrictions.
    Keywords: ambiguous beliefs; credible region; partial causal ordering; posterior bounds
    JEL: C11 C54
    Date: 2014–12
  5. By: Stefan Bruder
    Abstract: Path forecasts, defined as sequences of individual forecasts, generated by vector autoregressions are widely used in applied work. It has been recognized that a profound econometric analysis requires, besides the path forecast, a joint prediction region that contains the whole future path with a prespecified coverage probability. The forecasting literature offers several different methods of computing joint prediction regions, where the existing methods are either bootstrap based or rely on asymptotic results. The aim of this paper is to investigate the finite-sample performance of three methods for constructing joint prediction regions in various scenarios via Monte Carlo simulations.
    Keywords: Path forecast, joint prediction region, Monte Carlo simulation
    JEL: C15 C32 C53
    Date: 2014–11
  6. By: Tobias Fissler (University of Bern); Mark Podolskij (Aarhus University and CREATES)
    Abstract: In this paper, we present a test for the maximal rank of the volatility process in continuous diffusion models observed with noise. Such models are typically applied in mathematical finance, where latent price processes are corrupted by microstructure noise at ultra high frequencies. Using high frequency observations we construct a test statistic for the maximal rank of the time varying stochastic volatility process. Our methodology is based upon a combination of a matrix perturbation approach and pre-averaging. We will show the asymptotic mixed normality of the test statistic and obtain a consistent testing procedure.
    Keywords: continuous Itô semimartingales, high frequency data, microstructure noise, rank testing, stable convergence
    JEL: C10 C13 C14
    Date: 2014–12–10

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