nep-ets New Economics Papers
on Econometric Time Series
Issue of 2014‒02‒15
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. A Lagrange Multiplier Test for Testing the Adequacy of the Constant Conditional Correlation GARCH Model By Paul Catani; Timo Teräsvirta; Meiqun Yin
  2. The Typical Spectral Shape of An Economic Variable: A Visual Guide with 100 Examples By Carlos Medel
  3. On the Asymptotic Distribution of the DF–GLS Test Statistic By Joakim Westerlund
  4. The Local Power of the CADF and CIPS Panel Unit Root Tests By Joakim Westerlund; Mehdi Hosseinkouchack; Martin Solberger
  5. On the Importance of the First Observation in GLS Detrending in Unit Root Testing By Joakim Westerlund
  6. A Method for Experimental Events that Break Cointegration: Counterfactual Simulation By Bell, Peter N
  7. Rank and order conditions for identification in simultaneous system of cointegrating equations with integrated variables of order two By Mosconi, Rocco; Paruolo, Paolo
  8. Residual-based Rank Specification Tests for AR-GARCH type models By Elena Andreou; Bas J.M. Werker
  9. A general theory of rank testing By Majid M. Al-Sadoon

  1. By: Paul Catani (Hanken School of Economics); Timo Teräsvirta (Aarhus University and CREATES); Meiqun Yin (Beijing International Studies University)
    Abstract: A Lagrange multiplier test for testing the parametric structure of a constant conditional correlation generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which represents the null model, whereas the other one describes the misspeci?cation. A simulation study shows that the test has good ?nite sample properties. We compare the test with other tests for misspeci?cation of multivariate GARCH models. The test has high power against alternatives where the misspeci?cation is in the GARCH parameters and is superior to other tests. The test is not greatly affected by misspeci?cation in the conditional correlations and is therefore well suited for considering misspeci?cation of GARCH equations. JEL Codes: C32, C52, C58
    Keywords: constant conditional correlation, LM test, misspeci?cation testing, modelling volatility, multivariate GARCH
    Date: 2014–01–28
  2. By: Carlos Medel
    Abstract: Granger (1966) describes how the spectral shape of an economic variable concentrates spectral mass at low frequencies, declining smoothly as frequency increases. Despite a discussion about how to assess robustness of his results, the empirical exercise focused on the evidence obtained from a handful of series. In this paper, I focus on a broad range of economic variables to investigate their spectral shape. Hence, through different examples taken from both actual and simulated series, I provide an intuition of the typical spectral shape of a wide range of economic variables and the impact of their typical treatments. After performing 100 different exercises, the results show that Granger's assertion holds more often than not. I also confirm that the basic shape holds for a number of transformations, time aggregations, series' anomalies, variables of the real economy, and also, but to a lesser extent, financial variables. Especially fuzzy cases are those that exhibit some degree of transition to a different regime, as are those estimated with a very short bandwidth.
    Date: 2014–01
  3. By: Joakim Westerlund (Deakin University)
    Abstract: In a very influential paper Elliott et al. (Efficient Tests for an Autoregressive Unit Root, Econometrica 64, 813–836, 1996) show that no uniformly most powerful test for the unit root testing problem exits, derive the relevant power envelope and characterize a family of point-optimal tests. As a by-product, they also propose a “GLS detrended” version of the conventional Dickey–Fuller test, denoted DF–GLS, that has since then become very popular among practitioners, much more so than the point-optimal tests. In view of this, it is quite strange to find that, while conjectured in Elliott et al. (1996), so far there seems to be no formal proof of the asymptotic distribution of the DF–GLS test statistic. By providing three separate proofs the current paper not only substantiates the required result, but also provides insight regarding the pros and cons of different methods of proof.
    Keywords: Unit root test; GLS detrending; Asymptotic distribution; Asymptotic local power; Method of proof.
    JEL: C12 C22
  4. By: Joakim Westerlund (Deakin University); Mehdi Hosseinkouchack; Martin Solberger
    Abstract: Very little is known about the local power of second generation panel unit root tests that are robust to cross-section dependence. This paper derives the local asymptotic power functions of the CADF and CIPS tests of Pesaran (A Simple Panel Unit Root Test in Presence of Cross-Section Dependence, Journal of Applied Econometrics 22, 265–312, 2007), which are among the most popular tests around.
    Keywords: Panel unit root test; common factor model; cross-sectional averages; crosssectional dependence; local asymptotic power.
    JEL: C12 C13 C33
  5. By: Joakim Westerlund (Deakin University)
    Abstract: First-differencing is generally taken to imply the loss of one observation, the first, or at least that the effect of ignoring this observation is asymptotically negligible. However, this is not always true, as in the case of GLS detrending. In order to illustrate this, the current paper considers as an example the use of GLS detrended data when testing for a unit root. The results show that the treatment of the first observation is absolutely crucial for test performance, and that ignorance causes test break-down.
    Keywords: Unit root test; GLS detrending; Local asymptotic power.
    JEL: C12 C13 C33
  6. By: Bell, Peter N
    Abstract: In this paper I develop a method to estimate the effect of an event on a time series variable. The event is framed in a quasi-experimental setting with time series observations on a treatment variable, which is affected by the event, and a control variable, which is not. Prior to the event, the two variables are cointegrated. After the event, they are not. Since the event only affects the treatment variable, the method uses observations on the control variable after the event and the distribution of difference in differences before the event to simulate values for the treatment variable as-if the event did not occur; hence the name counterfactual simulation. I describe theoretical properties of the method and show the method in action with purpose-built data.
    Keywords: Quasi-experiment, cointegration, time series, counterfactual, simulation
    JEL: C15 C32 C63 C90
    Date: 2014–02–07
  7. By: Mosconi, Rocco; Paruolo, Paolo
    Abstract: This paper discusses identification of systems of simultaneous cointegrating equations with integrated variables of order two. Rank and order conditions for identification are provided for general linear restrictions, as well as for equation-by-equation constraints. As expected, the application of the rank conditions to triangular forms and other previous formulations for these systems shows that they are just-identifying. The conditions are illustrated on models of aggregate consumption with liquid assets and on system of equations for inventories.
    Keywords: Identification, (Multi-)Cointegration, I(2), Stocks and flows, Inventory models.
    JEL: C10 C32
    Date: 2014–01–15
  8. By: Elena Andreou; Bas J.M. Werker
    Abstract: This paper derives the asymptotic distribution for a number of rank-based and classical residual specification tests in AR-GARCH type models. We consider tests for the null hypotheses of no linear and quadratic serial residual autocorrelation, residual symmetry, and no structural breaks. For these tests we show that, generally, no size correction is needed in the asymptotic test distribution when applied to AR-GARCH type residuals obtained through QMLE estimation. To be precise, we give exact expressions for the limiting null distribution of the test statistics applied to residuals, and find that standard critical values often lead to conservative tests. For this result, we give simple sufficient conditions. Simulations show that our asymptotic approximations work well for a large number of AR-GARCH models and parameter values. We also show that the rank-based tests often, though not always, have superior power properties over the classical tests, even if they are conservative. We thereby provide a useful extension to the econometrician's toolkit. An empirical application illustrates the relevance of these tests to the AR-GARCH models for the weekly stock market return indices of some major and emerging countries.
    Keywords: Conditional heteroskedasticity, Linear and quadratic residual autocorrelation tests, Model misspecification test, Nonlinear time series, Parameter constancy, Residual symmetry tests
    Date: 2014–01
  9. By: Majid M. Al-Sadoon
    Abstract: This paper develops an approach to rank testing that nests all existing rank tests and simplifies their asymptotics. The approach is based on the fact that implicit in every rank test there are estimators of the null spaces of the matrix in question. The approach yields many new insights about the behavior of rank testing statistics under the null as well as local and global alternatives in both the standard and the cointegration setting. The approach also suggests many new rank tests based on alternative estimates of the null spaces as well as the new fixed-b theory. A brief Monte Carlo study illustrates the results.
    Keywords: Rank testing, stochastic tests, classical tests, subspace estimation, cointegration.
    JEL: C12 C13 C30
    Date: 2014–02

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