nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒05‒24
eight papers chosen by
Yong Yin
SUNY at Buffalo

  1. The alternative of a smoother parameter in the Hodrick-Prescott filter By Miguel Angel Ramirez
  2. Common Correlated Effects Estimation of Heterogeneous Dynamic Panel Data Models with Weakly Exogenous Regressors By Pesaran, Hashem; Chudik, Alexander
  3. A Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection By Audrone Virbickaite; Concepción Ausín; Pedro Galeano
  4. One for all : nesting asymmetric stochastic volatility models By Xiuping Mao; Esther Ruiz; Helena Veiga
  5. A Regime Switching Skew-normal Model for Measuring Financial Crisis and Contagion By Joshua C.C. Chan; Cody Yu-Ling Hsiao; Renée A. Fry-McKibbin
  6. The Calculation of Some Limiting Distributions Arising in Near-Integrated Models with GLS Detrending By Marcus J. Chambers
  7. Time-varying structural vector autoregressions and monetary policy: a corrigendum By Marco Del Negro; Giorgio Primiceri
  8. Estimating dynamic equilibrium models with stochastic volatility By Jesús Fernández-Villaverde; Pablo Guerrón-Quintana; Juan F. Rubio-Ramírez

  1. By: Miguel Angel Ramirez (UNAM)
    Abstract: From its beginnings in the second half of the nineteenth century until today, the study of economic time series has involved a variety of research anchored in stylized mathematical methods in order to simulate, verify, test, and forecast the behavior of key economic variables to describe economic activity and its phenomena intertemporally. Despite the proliferation of studies, it was not until the 1980s that the procedure outlined by Robert James Hodrick and Edward Christian Prescott acquired special relevance. Their method, isolating the effects and trend-cycle series, denoted a turning point in modern econometric modeling. However, the spread of the "HP filter" in economic applications used by researchers, academics, students, and policy-makers has led to implausible results because of inadequate specifications in the decomposition of the series. In this context, this analysis attempts to overcome the methodological problems inherent in the filter, indicate a brief theoretical outline of the time series, formulate a sui generis consistent parameter of variables, and show in Stata a simulation of the real exchange rate in Norway.
    Date: 2013–05–13
  2. By: Pesaran, Hashem; Chudik, Alexander
    Abstract: This paper extends the Common Correlated Effects (CCE) approach developed by Pesaran (2006) to heterogeneous panel data models with lagged dependent variable and/or weakly exogenous regressors. We show that the CCE mean group estimator continues to be valid but the following two conditions must be satis?ed to deal with the dynamics: a sufficient number of lags of cross section averages must be included in individual equations of the panel, and the number of cross section averages must be at least as large as the number of unobserved common factors. We establish consistency rates, derive the asymptotic distribution, suggest using co- variates to deal with the effects of multiple unobserved common factors, and consider jackknife and recursive de-meaning bias correction procedures to mitigate the small sample time series bias. Theoretical ?findings are accompanied by extensive Monte Carlo experiments, which show that the proposed estimators perform well so long as the time series dimension of the panel is sufficiently large.
    Keywords: Large panels, lagged dependent variable, cross sectional dependence, coefficient heterogeneity, estimation and inference, common correlated effects, unobserved common factors.
    JEL: C31 C33
    Date: 2013–05–17
  3. By: Audrone Virbickaite; Concepción Ausín; Pedro Galeano
    Abstract: We use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH model takes into consideration the asymmetries in individual assets volatilities, as well as in the correlations. The errors are modeled using a flexible location-scale mixture of infinite Gaussian distributions and the inference and estimation is carried out by relying on Bayesian non-parametrics. Finally, we carry out a simulation study to illustrate the flexibility of the new method and present a financial application using Apple and NASDAQ Industrial index data to solve a portfolio allocation problem
    Keywords: Asymmetric dynamic condition correlation, Bayesian non-parametrics, Dirichlet process mixtures, Portfolio allocation
    Date: 2013–05
  4. By: Xiuping Mao; Esther Ruiz; Helena Veiga
    Abstract: This paper proposes a new stochastic volatility model to represent the dynamic evolution of conditionally heteroscedastic series with leverage effect. Although there are already several models proposed in the literature with the same purpose, our main justification for a further new model is that it nests some of the most popular stochastic volatility specifications usually implemented to real time series of financial returns. We derive closed-form expressions of its statistical properties and, consequently, of those of the nested specifications. Some of these properties were previously unknown in the literature although the restricted models are often fitted by empirical researchers. By comparing the properties of the restricted models, we are able to establish the advantages and limitations of each of them. Finally, we analyze the performance of a MCMC estimator of the parameters and volatilities of the new proposed model and show that it has appropriate finite sample properties. Furthermore, estimating the new model using the MCMC estimator, one can correctly identify the restricted specifications. All the results are illustrated by estimating the parameters and volatilities of simulated time series and of a series of daily S&P500 returns
    Keywords: EGARCH, Leverage effect, MCMC estimator, Stochastic News Impact Surface, Threshold Stochastic Volatility, WinBUGS
    Date: 2013–05
  5. By: Joshua C.C. Chan; Cody Yu-Ling Hsiao; Renée A. Fry-McKibbin
    Abstract: A regime switching skew-normal model for financial crisis and contagion is proposed in which we develop a new class of multiple-channel crisis and contagion tests. Crisis channels are measured through changes in ‘own’ moments of the mean, variance and skewness, while contagion is through changes in the covariance and co-skewness of the joint distribution of asset returns. In this framework: i) linear and non-linear dependence is allowed; ii) transmission channels are simultaneously examined; iii) crisis and contagion are distinguished and individually modeled; iv) the market that a crisis originates is endogenous; and v) the timing of a crisis is endogenous. In an empirical application, we apply the proposed model to equity markets during the Great Recession using Bayesian model comparison techniques to assess the multiple channels of crisis and contagion. The results generally show that crisis and contagion are pervasive across Europe and the US. The second moment channels of crisis and contagion are systematically more evident than the first and third moment channels.
    Keywords: Great Recession, Crisis tests, Contagion tests, Co-skewness, Regime switching skew-normal model, Gibbs sampling, Bayesian model comparison
    JEL: C11 C34 G15
    Date: 2013–03
  6. By: Marcus J. Chambers
    Abstract: Many unit root test statistics are nowadays constructed using detrended data, with the method of GLS detrending being popular in the setting of a near-integrated model. This paper determines the properties of some associated limiting distributions when the GLS detrending is based on a linear time trend. A fundamental result for the moment generating function of two key functionals of the relevant stochastic process is provided and used to compute probability density functions and cumulative distribution functions, as well as means and variances, of the limiting distributions of some statistics of interest. Some further applications, including a comparison of limiting power functions and the consideration of a more complicated statistic, are also provided.
    Date: 2013–05–21
  7. By: Marco Del Negro; Giorgio Primiceri
    Abstract: This note corrects a mistake in the estimation algorithm of the time-varying structural vector autoregression model of Primiceri (2005) and proposes a new algorithm that correctly applies the procedure proposed by Kim, Shephard, and Chib (1998) to the estimation of VAR or DSGE models with stochastic volatility. Relative to Primiceri (2005), the correct algorithm involves a different ordering of the various Markov Chain Monte Carlo steps.
    Keywords: Markov processes ; Regression analysis ; Econometric models
    Date: 2013
  8. By: Jesús Fernández-Villaverde; Pablo Guerrón-Quintana; Juan F. Rubio-Ramírez
    Abstract: We propose a novel method to estimate dynamic equilibrium models with stochastic volatility. First, we characterize the properties of the solution to this class of models. Second, we take advantage of the results about the structure of the solution to build a sequential Monte Carlo algorithm to evaluate the likelihood function of the model. The approach, which exploits the profusion of shocks in stochastic volatility models, is versatile and computationally tractable even in large-scale models, such as those often employed by policy-making institutions. As an application, we use our algorithm and Bayesian methods to estimate a business cycle model of the U.S. economy with both stochastic volatility and parameter drifting in monetary policy. Our application shows the importance of stochastic volatility in accounting for the dynamics of the data.
    Keywords: Stochastic analysis
    Date: 2013

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