nep-ets New Economics Papers
on Econometric Time Series
Issue of 2010‒04‒11
six papers chosen by
Yong Yin
SUNY at Buffalo

  1. Threshold Autoregressions Under Near Integratedness By Pitarakis, Jean-Yves
  2. Identification and Estimation of Nonlinear Dynamic Panel Data Models with Unobserved Covariates By Ji-Liang Shiu and Yingyao Hu
  3. Bootstrap co-integration rank testing: the role of deterministic variables and initial values in the bootstrap recursion By Giuseppe Cavaliere; A. M. Robert Taylor; Carsten Trenkler
  4. Multivariate stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes: A quasi-likelihood approach By Arvid Raknerud and Øivind Skare
  5. Dynamic specification tests for static factor models By Gabriele Fiorentini; Enrique Sentana
  6. Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate By David F. Hendry; Kirstin Hubrich

  1. By: Pitarakis, Jean-Yves
    Abstract: We explore the properties of a Wald type test statistic for detecting the presence of threshold effects in time series when the underlying process could be nearly integrated as opposed to having an exact unit root. We derive its limiting distribution and establish its equivalence to a normalised squared Brownian Bridge process. More importantly we show that the limiting random variable no longer depends on the noncentrality parameter characterising the nearly integrated DGP. This is an unusual occurrence which is in stark contrast with the existing literature on conducting inferences under persistent regressors where it is well known that the noncentrality parameter appears in the limiting distribution of test statistics, making them impractical for inference purposes. <br><br> Keynames; Threshold Autoregressive Models, Near Unit Root, Noncentrality Parameter, Nonlinear time series <br><br> JEL Classification: C22.
    Date: 2010–03–01
  2. By: Ji-Liang Shiu and Yingyao Hu
    Abstract: This paper considers nonparametric identification of nonlinear dynamic models for panel data with unobserved voariates. Including such unobserved covariates may control for both the individual-specific unobserved heterogeneity and the endogeneity of the explanatory variables. Without specifying the distribution of the initial condition with the unobserved variables, we show that the models are nonparametrically identified from three periods of data. The main identifying assumption requires the evolution of the observed covariates depends on the unobserved covariates but not on the lagged dependent variable. We also propose a sieve maximum likelihood estimator (MLE) and focus on two classes of nonlinear dynamic panel data models, i.e., dynamic discrete choice models and dynamic censored models. We present the asymptotic property of the sieve MLE and investigate the finite sample properties of these sieve-based estimator through a Monte Carlo study. An intertemporal female labor force participation model is estimated as an empirical illustration using a sample from the Panel Study of Income Dynamics (PSID).
    Date: 2010–04
  3. By: Giuseppe Cavaliere; A. M. Robert Taylor; Carsten Trenkler
    Abstract: In this paper we investigate the role of deterministic components and initial values in bootstrap likelihood ratio type tests of co-integration rank. A number of bootstrap procedures have been proposed in the recent literature some of which include estimated deterministic components and non-zero initial values in the bootstrap recursion while others do the opposite. To date, however, there has not been a study into the relative performance of these two alternative approaches. In this paper we fill this gap in the literature and consider the impact of these choices on both OLS and GLS de-trended tests, in the case of the latter proposing a new bootstrap algorithm as part of our analysis. Overall, for OLS de-trended tests our findings suggest that it is preferable to take the computationally simpler approach of not including estimated deterministic components in the bootstrap recursion and setting the initial values of the bootstrap recursion to zero. For GLS de-trended tests, we find that the approach of Trenkler (2009), who includes a restricted estimate of the deterministic component in the bootstrap recursion, can improve finite sample behaviour further.
    Keywords: Co-integration; trace tests; i.i.d. bootstrap; OLS and GLS de-trending
    Date: 2010–03
  4. By: Arvid Raknerud and Øivind Skare (Statistics Norway)
    Abstract: This paper extends the ordinary quasi-likelihood estimator for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck (OU) processes to vector processes. Despite the fact that multivariate modeling of asset returns is essential for portfolio optimization and risk management -- major areas of financial analysis -- the literature on multivariate modeling of asset prices in continuous time is sparse, both with regard to theoretical and applied results. This paper uses non-Gaussian OU-processes as building blocks for multivariate models for high frequency financial data. The OU framework allows exact discrete time transition equations that can be represented on a linear state space form. We show that a computationally feasible quasi-likelihood function can be constructed by means of the Kalman filter also in the case of high-dimensional vector processes. The framework is applied to Euro/NOK and US Dollar/NOK exchange rate data for the period 2.1.1989-4.2.2010.
    Keywords: multivariate stochastic volatility; exchange rates; Ornstein-Uhlenbeck processes; quasi-likelihood; factor models; state space representation
    JEL: C13 C22 C51 G10
    Date: 2010–03
  5. By: Gabriele Fiorentini (RCEA and Università di Firenze, Italy); Enrique Sentana (CEMFI, Madrid, Spain)
    Abstract: We derive computationally simple score tests of serial correlation in the levels and squares of common and idiosyncratic factors in static factor models. The implicit orthogonality conditions resemble the orthogonality conditions of models with observed factors but the weighting matrices refl ect their unobservability. We derive more powerful tests for elliptically symmetric distributions, which can be either parametrically or semipametrically specified, and robustify the Gaussian tests against general non-normality. Our Monte Carlo exercises assess the finite sample reliability and power of our proposed tests, and compare them to other existing procedures. Finally, we apply our methods to monthly US stock returns.
    Keywords: ARCH, Financial returns, Kalman filter, LM tests, Predictability
    JEL: C32 C13 C12 C14 C16
    Date: 2010–01
  6. By: David F. Hendry (Department of Economics, Oxford University, Manor Rd. Building, Oxford, OX1 3UQ, United Kingdom.); Kirstin Hubrich (Research Department, European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)
    Abstract: To forecast an aggregate, we propose adding disaggregate variables, instead of combining forecasts of those disaggregates or forecasting by a univariate aggregate model. New analytical results show the effects of changing coefficients, mis-specification, estimation uncertainty and mis-measurement error. Forecastorigin shifts in parameters affect absolute, but not relative, forecast accuracies; mis-specification and estimation uncertainty induce forecast-error differences, which variable-selection procedures or dimension reductions can mitigate. In Monte Carlo simulations, different stochastic structures and interdependencies between disaggregates imply that including disaggregate information in the aggregate model improves forecast accuracy. Our theoretical predictions and simulations are corroborated when forecasting aggregate US inflation pre- and post 1984 using disaggregate sectoral data. JEL Classification: C51, C53, E31.
    Keywords: Aggregate forecasts, disaggregate information, forecast combination, inflation.
    Date: 2010–02

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