nep-ets New Economics Papers
on Econometric Time Series
Issue of 2007‒01‒23
thirteen papers chosen by
Yong Yin
SUNY at Buffalo

  1. Spurious Regression in Nonstationary Panels with Cross-Unit Cointegration By Urbain Jean-Pierre; Westerlund Joakim
  2. Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment By Siem Jan Koopman; Marius Ooms; Irma Hindrayanto
  3. On the stability of nonlinear ARMA models By Fonseca Giovanni
  4. Design of vector autoregressive processes for invariant statistics By Paruolo Paolo
  5. Multivariate ARCH with spatial effects for stock sector and size By Caporin Massimiliano; Paruolo Paolo
  6. Random effects and Spatial Autocorrelations with Equal Weights By Badi H. Baltagi
  7. Panel Cointegration with Global Stochastic Trends By Jushan Bai; Chihwa Kao; Serena Ng
  8. Lag-Augmented Two- and Three-Stage Least Squares Estimators for Integrated Structural Dynamic Models By Cheng Hsiao; Siyan Wang
  9. Opening the black box - structural factor models with large gross-sections By Mario Forni; Domenico Giannone; Marco Lippi; Lucrezia Reichlin
  10. Backtesting VaR Models: An Expected Shortfall Approach By Timotheos Angelidis; Stavros Degiannakis
  11. Panel Data Models with Multiple Time-Varying Individual Effects By Seung C. Ahn; Young H. Lee; Peter Schmidt
  12. Structural change and estimated persistence in the GARCH(1,1)-model By Prof. Dr. Walter Krämer; Baudouin Tameze Azamo
  13. Long memory with Markov-Switching GARCH By Prof. Dr. Walter Krämer

  1. By: Urbain Jean-Pierre; Westerlund Joakim (METEOR)
    Abstract: This paper illustrates analytically the effects of cross-unit cointegration using as an example the conventional pooled least squares estimate in the spurious panel regression case. The results suggest that the usual result of asymptotic normality depends critically on the absence of cross-unit cointegration.
    Keywords: econometrics;
    Date: 2006
  2. By: Siem Jan Koopman (Vrije Universiteit Amsterdam); Marius Ooms (Vrije Universiteit Amsterdam); Irma Hindrayanto (Vrije Universiteit Amsterdam)
    Abstract: This paper discusses identification, specification, estimation and forecasting for a general class of periodic unobserved components time series models with stochastic trend, seasonal and cycle components. Convenient state space formulations are introduced for exact maximum likelihood estimation, component estimation and forecasting. Identification issues are considered and a novel periodic version of the stochastic cycle component is presented. In the empirical illustration, the model is applied to postwar monthly US unemployment series and we discover a significantly periodic cycle. Furthermore, a comparison is made between the performance of the periodic unobserved components time series model and a periodic seasonal autoregressive integrated moving average model. Moreover, we introduce a new method to estimate the latter model.
    Keywords: Unobserved component models; state space methods; seasonal adjustment; time–varying parameters; forecasting
    JEL: C22 C51 E32 E37
    Date: 2006–11–20
  3. By: Fonseca Giovanni (Department of Economics, University of Insubria, Italy)
    Abstract: In the present paper we study the stability of a class of nonlinear ARMA models. We derive a sufficient condition to ensure the geometric ergodicity and we apply it to a very general threshold ARMA model imposing a mild assumption on the thresholds
    Keywords: Nonlinear ARMA models, threshold ARMA processes, stationary processes, geometric ergodicity
    Date: 2005–06
  4. By: Paruolo Paolo (Department of Economics, University of Insubria, Italy)
    Abstract: This paper discusses the Monte Carlo (MC) design of Gaussian Vector Au- toregressive processes (VAR) for the evalutation of invariant statistics. We focus on the case of cointegrated (CI) I(1) processes, linear and invertible trans- formations and CI rank likelihood ratio (LR) tests. It is found that all VAR of order 1 can be reduced to a system of independent or recursive subsystems, of computational dimension at most equal to 2. The results are applied to the indexing of the distribution of LR test statistics for CI rank under local alternatives. They are also extended to the case of VAR processes of higher order.
    Keywords: Invariance, Vector autoregressive process, Monte Carlo, Likeli-hood ratio test, Cointegration.
    JEL: C32
    Date: 2005–09
  5. By: Caporin Massimiliano (Department of Economics, University of Padova, Italy); Paruolo Paolo (Department of Economics, University of Insubria, Italy)
    Abstract: This paper applies a new spatial approach for the specfication of multivariate GARCH models, called Spatial Effects in ARCH, SEARCH. We consider spatial dependence associated with industrial sectors and capitalization size. This parametrization extends current feasible specifications for large scale GARCH models, keeping the numbers of parameters linear as a function of the number of assets. An application to daily returns on 150 stocks from the NYSE for the period January 1994 to June 2001 shows the benefits of the present specification when compared to alternative specifications.
    Keywords: Spatial models, GARCH, Volatility, Large scale models, Portfolio allocation.
    JEL: C32 C51 C52
    Date: 2005–12
  6. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020)
    Abstract: This note considers a panel data regression model with spatial autoregressive disturbances and random effects where the weight matrix is normalized and has equal elements. This is motivated by Kelejian et al. (2005), who argue that such a weighting matrix, having blocks of equal elements, might be considered when units are equally distant within certain neighborhoods but unrelated between neighborhoods. We derive a simple weighted least squares transformation that obtains GLS on this model as a simple OLS. For the special case of a spatial panel model with no random effects, we obtain two sufficient conditions where GLS on this model is equivalent to OLS. Finally, we show that these results, for the equal weight matrix, hold whether we use the spatial autoregressive specification, the spatial moving average specification, the spatial error components specification or the Kapoor et al. (2005) alternative to modeling panel data with spatially correlated error components.
    Keywords: Panel data, spatial error correlation, equal weights, error components
    JEL: C23 C12
    Date: 2006–12
  7. By: Jushan Bai (Department of Economics, New York University, Newe York, New York 10003 USA, and School of Economics and Management, Tsinghua University); Chihwa Kao (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Serena Ng (Department of Economics, University of Michigan, Ann Arbor, MI 48109, USA)
    Abstract: This paper studies estimation of panel cointegration models with cross-sectional dependence generated by unobserved global stochastic trends. The standard least squares estimator is, in general, inconsistent owing to the spuriousness induced by the unobservabla I(1) trends. We propose two iterative procedures that jointly estimate the slope parameters and the stochastic trends. The resulting estimators are referred to respectively as CupBC (continuously updated and bias-corrected) and the CupFM (continuously updated and fully modified) estimators. We establish their consistency and derive their limiting distributions. Both are asymptotically unbiased and asymptotically normal and permit inference to be conducted using standard test statistics. The estimates are also valid when there are mixed stationary and non-stationary factors, as well as when the factors are all stationary.
    JEL: C13 C33
  8. By: Cheng Hsiao (Department of Economics, University of Southern California); Siyan Wang (Department of Economics, University of Delaware)
    Abstract: We consider a lag-augmented two- or three-stage least squares estimator for a structural dynamic model of nonstationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two- and three-stage least squares estimators are consistent but contain nonstandard distributions without the strict exogeneity assumption, hence the conventional Wald type test statistics may not be chi-square distributed. We propose a lag order augmented two- or three-stage least squares estimator that is consistent and asymptotically normally distributed. Limited Monte Carlo studies are conducted to shed light on the finite sample properties of various estimators.
    Keywords: Structural vector autoregressions, Nonstationary time series, Cointegration, Hypothesis testing, Two and Three Stage Least Squares
    JEL: C1 C3
    Date: 2006–09
  9. By: Mario Forni (Università di Modena e Reggio Emilia and CEPR Address: Università degli studi di Modena e Reggio Emilia - Dipartimento di Economia Politica, Viale Berengario 51, 41100 Modena, Italy.); Domenico Giannone (ECARES, Université Libre de Bruxelles, Campus du Solbosch, CP114, avenue F.D. Roosevelt 50, 1050 Bruxelles, Belgium.); Marco Lippi (Dipartimento di Scienze Economiche, Università di Roma “La Sapienza”, Via Cesalpino 12, 00161 Roma, Italy.); Lucrezia Reichlin (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: This paper shows how large-dimensional dynamic factor models are suitable for structural analysis. We establish sufficient conditions for identification of the structural shocks and the associated impulse-response functions. In particular, we argue that, if the data follow an approximate factor structure, the “problem of fundamentalness”, which is intractable in structural VARs, can be solved provided that the impulse responses are sufficiently heterogeneous. Finally, we propose a consistent method (and n,T rates of convergence) to estimate the impulse-response functions, as well as a bootstrapping procedure for statistical inference. JEL Classification: E0, C1.
    Keywords: Dynamic factor models, structural VARs, identification, fundamentalness.
    Date: 2007–01
  10. By: Timotheos Angelidis; Stavros Degiannakis
    Abstract: Academics and practitioners have extensively studied Value-at-Risk (VaR) to propose a unique risk management technique that generates accurate VaR estimations for long and short trading positions and for all types of financial assets. However, they have not succeeded yet as the testing frameworks of the proposals developed, have not been widely accepted. A two-stage backtesting procedure is proposed to select a model that not only forecasts VaR but also predicts the losses beyond VaR. Numerous conditional volatility models that capture the main characteristics of asset returns (asymmetric and leptokurtic unconditional distribution of returns, power transformation and fractional integration of the conditional variance) under four distributional assumptions (normal, GED, Student-t, and skewed Student-t) have been estimated to find the best model for three financial markets, long and short trading positions, and two confidence levels. By following this procedure, the risk manager can significantly reduce the number of competing models that accurately predict both the VaR and the Expected Shortfall (ES) measures.
    Keywords: Value-at-Risk, Expected Shortfall, Volatility Forecasting, Arch Models
    JEL: C22 C52 G15
    Date: 2007–01–12
  11. By: Seung C. Ahn (Dept. of Economics, W.P. Carey School of Business, Arizona State University, Tempe, AZ 85287); Young H. Lee (Hansung University); Peter Schmidt (Michigan State University)
    Abstract: This paper considers a panel data model with time-varying individual effects. The data are assumed to contain a large number of cross-sectional units repeatedly observed over a fixed number of time periods. The model has a feature of the fixed-effects model in that the effects are assumed to be correlated with the regressors. The unobservable individual effects are assumed to have a factor structure. For consistent estimation of the model, it is important to estimate the true number of factors. We propose a generalized methods of moments procedure by which both the number of factors and the regression coefficients can be consistently estimated. Some important identification issues are also discussed. Our simulation results indicate that the proposed methods produce reliable estimates.
    Keywords: panel data, time-varying individual effects, factor models
    JEL: C51 D24
    Date: 2006–10
  12. By: Prof. Dr. Walter Krämer (Fachbereich Statistik, Universität Dortmund); Baudouin Tameze Azamo (Fachbereich Statistik, Universität Dortmund)
    Abstract: It has long been known that the estimated persistence parameter in the GARCH(1,1) - model is biased upwards when the parameters of the model are not constant throughout the sample. The present paper explains the mechanics of this behavior for a particular class of estimates of the model parameters and for a particular type of structural change. It shows for any given sample size that the estimated persistence must tend to one in probability if the structural change is ignored and large enough.
    Keywords: long memory, GARCH, structural change
    Date: 2006–05
  13. By: Prof. Dr. Walter Krämer (Fachbereich Statistik, Universität Dortmund)
    Abstract: The paper considers the Markov-Switching GARCH(1,1)-model with time-varying transition probabilities. It derives su±cient conditions for the square of the process to display long memory and provides some additional intuition for the empirical observation that estimated GARCH-parameters often sum to almost one.
    Keywords: Markov switching, GARCH, long memory
    Date: 2006–10

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