nep-ecm New Economics Papers
on Econometrics
Issue of 2008‒04‒15
seventeen papers chosen by
Sune Karlsson
Orebro University

  1. Testing fractional order of long memory processes : a Monte Carlo study. By Laurent Ferrara; Dominique Guegan; Zhiping Lu
  2. Testing for Random Effects and Spatial Lag Dependence in Panel Data Models By Badi H. Baltagi; Long Liu
  3. Change in persistence tests for panels: An update and some new results By Cerqueti, Roy; Costantini, Mauro; Gutierrez, Luciano
  4. Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots and Cointegration By Dimitrios Thomakos
  5. Forecasting Realized Volatility: A Bayesian Model Averaging Approach By Chun Liu; John M Maheu
  6. A Note on Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots with Drift By Dimitrios Thomakos
  7. The Stochastic Fluctuation of the Quantile Regression Curve By Wolfgang Härdle; Song Song
  8. A Corrected Value-at-Risk Predictor By Lönnbark, Carl
  9. The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics. By Abdou Kâ Diongue; Dominique Guegan
  10. Finite-Sample Moments of the MLE for the Binary Logit Model By Qian Chen; David E. Giles
  11. Forecasting Australian Macroeconomic Variables Using a Large Dataset By Sarantis Tsiaplias; Chew Lian Chua
  12. Are Spectral Estimators Useful for Implementing Long-Run Restrictions in SVARs? By Elmar Mertens;
  13. Dynamic graphics of parametrically linked multivariate methods used in compositional data analysis By Michael Greenacre
  14. Modeling International Financial Returns with a Multivariate Regime Switching Copula By Chollete, Loran; Heinen, Andreas; Valdesogo, Alfonso
  15. Econometric Causality By Heckman, James J.
  16. Pricing bivariate option under GARCH processes with time-varying copula. By Jing Zhang; Dominique Guegan
  17. The Role of Heterogeneous Demand for Temporal and Structural Aggregation Bias By Bente Halvorsen and Bodil M. Larsen

  1. By: Laurent Ferrara (Centre d'Economie de la Sorbonne et DGEI-DAMEP, Banque de France); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics); Zhiping Lu (Centre d'Economie de la Sorbonne et East China Normal University)
    Abstract: Testing the fractionally integrated order of seasonal and non-seasonal unit roots is quite important for the economic and financial time series modelling. In this paper, Robinson test (1994) is applied to various well-known long memory models. Via Monte Carlo experiments, we study and compare the performances of this test using several sample sizes.
    Keywords: Long memory processes, test, Monte Carlo simulations.
    JEL: C12 C15 C22
    Date: 2008–02
  2. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Long Liu
    Abstract: This paper derives a joint Lagrande Multiplier (LM) test which simultaneously tests for the absence of spatial lag dependence and random individual effects in a panel data regression model. It turns out that this LM statistic is the sum of two standard LM statistics. The first one tests for the absence of spatial lag dependence ignoring the random individual effects, and the second one tests for the absence of random individual effects ignoring the spatial lag dependence. This paper also derives two conditional LM tests. The first one tests for the absence of random individual effects without ignoring the possible presence of spatial lag dependence. The second one tests for the absence of spatial lag dependence without ignoring the possible presence of random individual effects.
    Keywords: Panel data; spatial lag dependence; Lagrange Multiplier tests; random effects
    JEL: C12 C23
    Date: 2008–03
  3. By: Cerqueti, Roy; Costantini, Mauro; Gutierrez, Luciano
    Abstract: In this paper we propose a set of new panel tests to detect changes in persistence. The test statistics are used to test the null hypothesis of stationarity against the alternative of a change in persistence from I(0) to I(1), from I(1) to I(0) and in an unknown direction. The limiting distributions of the panel tests are derived, and small sample properties are investigated by Monte Carlo experiments under the hypothesis that the individual series are independently cross-section distributed. These tests have a good size and power properties. Cross-sectional dependence is also considered. A procedure of de-factorizing, proposed by Stock and Watson (2002), is applied. The defactored panel tests have good size and power. The empirical results obtained from applying these tests to a panel covering 21 OECD countries observed between 1970 and 2007 suggest that inflation rate changes from I(1) to I(0) when cross-correlation is considered.
    Keywords: Persistence, Stationarity, Panel data
    JEL: C12 C23
    Date: 2008–03–31
  4. By: Dimitrios Thomakos
    Abstract: In this paper I propose a novel optimal linear filter for smoothing, trend and signal extraction for time series with a unit root. The filter is based on the Singular Spectrum Analysis (SSA) methodology, takes the form of a particular moving average and is different from other linear filters that have been used in the existing literature. To best of my knowledge this is the first time that moving average smoothing is given an optimality justification for use with unit root processes. The frequency response function of the filter is examined and a new method for selecting the degree of smoothing is suggested. I also show that the filter can be used for successfully extracting a unit root signal from stationary noise. The proposed methodology can be extended to also deal with two cointegrated series and I show how to estimate the cointegrating coefficient using SSA and how to extract the common stochastic trend component. A simulation study explores some of the characteristics of the filter for signal extraction, trend prediction and cointegration estimation for univariate and bivariate series. The practical usefulness of the method is illustrated using data for the US real GDP and two financial time series.
    Keywords: cointegration, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root.
    Date: 2008
  5. By: Chun Liu; John M Maheu
    Abstract: How to measure and model volatility is an important issue in finance. Recent research uses high frequency intraday data to construct ex post measures of daily volatility. This paper uses a Bayesian model averaging approach to forecast realized volatility. Candidate models include autoregressive and heterogeneous autoregressive (HAR) specifications based on the logarithm of realized volatility, realized power variation, realized bipower variation, a jump and an asymmetric term. Applied to equity and exchange rate volatility over several forecast horizons, Bayesian model averaging provides very competitive density forecasts and modest improvements in point forecasts compared to benchmark models. We discuss the reasons for this, including the importance of using realized power variation as a predictor. Bayesian model averaging provides further improvements to density forecasts when we move away from linear models and average over specifications that allow for GARCH effects in the innovations to log-volatility.
    Keywords: power variation, bipower variation, Gibbs sampling, model risk
    JEL: C11 C22 G12
    Date: 2008–04–03
  6. By: Dimitrios Thomakos
    Abstract: In this note I show that the method proposed in Thomakos (2008) for optimal linear filtering, smoothing and trend extraction for a unit root process can be applied with no changes when a drift parameter is added to the process. The method in the aforementioned paper is based on Singular Spectrum Analysis (SSA) and here I also derive an SSA-based consistent estimator of the drift parameter.
    Keywords: drift, forecasting, linear filtering, singular spectrum analysis, smoothing, trend extraction and prediction, unit root.
    Date: 2008
  7. By: Wolfgang Härdle; Song Song
    Abstract: Let (X1, Y1), . . ., (Xn, Yn) be i.i.d. rvs and let l(x) be the unknown p-quantile regression curve of Y on X. A quantile-smoother ln(x) is a localised, nonlinear estimator of l(x). The strong uniform consistency rate is established under general conditions. In many applications it is necessary to know the stochastic fluctuation of the process {ln(x) - l(x)}. Using strong approximations of the empirical process and extreme value theory allows us to consider the asymptotic maximal deviation sup06x61 |ln(x)-l(x)|. The derived result helps in the construction of a uniform confidence band for the quantile curve l(x). This confidence band can be applied as a model check, e.g. in econometrics. An application considers a labour market discrimination effect.
    Keywords: Quantile Regression, Consistency Rate, Confidence Band, Check Function, Kernel Smoothing, Nonparametric Fitting
    JEL: C00 C14 J01 J31
    Date: 2008–03
  8. By: Lönnbark, Carl (Department of Economics, Umeå University)
    Abstract: In this note it is argued that the estimation error in Value-at-Risk predictors gives rise to underestimation of portfolio risk. We propose a simple correction and find in an empirical illustration that it is economically relevant.
    Keywords: Estimation Error; Finance; Garch; Prediction; Risk Management
    JEL: C32 C51 C53 G10
    Date: 2008–03–26
  9. By: Abdou Kâ Diongue (Université Gaston Berger, School of Economics and Finance et Centre d'Economie de la Sorbonne); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics)
    Abstract: Electricity spot prices exhibit a number of typical features that are not found in most financial time series, such as complex seasonality patterns, persistence (hyperbolic decay of the autocorrelation function), mean reversion, spikes, asymmetric behavior and leptokurtosis. Efforts have been made worldwide to model the behaviour of the electricity's market price. In this paper, we propose a new approach dealing with the stationary k-factor Gegenbauer process with asymmetric Power GARCH noise under conditional Student-t distribution, which can take into account the previous features. We derive the stationary and invertible conditions as well as the ?th-order moment of this model that we called GGk-APARCH model. Then we focus on the estimation parameters and provide the analytical from of the likelihood which permits to obtain consitent estimates. In order to characterize the properties of these estimates we perform a Monte Carlo experiment. Finally the previous approach is used to the model electricity spot prices coming from the Leipzig Power Exchange (LPX) in Germany, Powernext in France, Operadora del Mercado Espagñol de Electricidad (OMEL) in Spain and the Pennsylvania-New Jersey-Maryland (PJM) interconnection in United States. In terms of forecasting criteria we obtain very good results comparing with models using hederoscedastic asymmetric errors.
    Keywords: Asymmetric distribution function, electricity spot prices, Leptokurtosis, persistence, seasonality, GARMA, A-PARCH.
    JEL: C12 C15 C22
    Date: 2008–02
  10. By: Qian Chen (School of Public Finance & Public Policy, Central University of Finance & Economics, People's Republic of China); David E. Giles (Department of Economics, University of Victoria)
    Abstract: We examine the finite sample properties of the MLE for the Logit model with random covariates. We derive the second order bias and MSE function for the MLE in this model, and undertake some numerical evaluations to illustrate the analytic results. From these numerical results we find, for example, that the bias correction that we provide is effective, and that the bias-corrected estimator is more efficient than the uncorrected MLE.
    Keywords: Logit model, mean squared error, bias correction
    JEL: C01 C13 C25
    Date: 2008–02–01
  11. By: Sarantis Tsiaplias (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne); Chew Lian Chua
    Abstract: This paper investigates the forecasting performance of the diffusion index approach for the Australian economy, and considers the forecasting performance of the diffusion index approach relative to composite forecasts. Weighted and unweighted factor forecasts are benchmarked against composite forecasts, and forecasts derived from individual forecasting models. The results suggest that diffusion index forecasts tend to improve on the benchmark AR forecasts. We also observe that weighted factors tend to produce better forecasts than their unweighted counterparts. We find, however, that the size of the forecasting improvement is less marked than previous research, with the diffusion index forecasts typically producing mean square errors of a similar magnitude to the VAR and BVAR approaches. JEL Classification: C22; C53; E17
    Keywords: PDiffusion indexes; Forecasting; Australia.
    Date: 2008–02
  12. By: Elmar Mertens (Study Center Gerzensee and University of Lausanne);
    Abstract: No, not really. Responding to lingering concerns about the reliability of SVARs, Christiano et al (NBER Macro Annual, 2006, "CEV") propose to combine OLS estimates of a VAR with a spectral estimate of long-run variance. In principle, this could help alleviate specification problems of SVARs in identifying long-run shocks. But in practice, spectral estimators suffer from small sample biases similar to those from VARs. Moreover, the spectral estimates contain information about serial correlation in VAR residuals and the VAR dynamics must be adjusted accordingly. Otherwise, a naive application of the CEV procedure would misrepresent the data's variance.
    Date: 2008–03
  13. By: Michael Greenacre
    Abstract: Many multivariate methods that are apparently distinct can be linked by introducing one or more parameters in their definition. Methods that can be linked in this way are correspondence analysis, unweighted or weighted logratio analysis (the latter also known as "spectral mapping"), nonsymmetric correspondence analysis, principal component analysis (with and without logarithmic transformation of the data) and multidimensional scaling. In this presentation I will show how several of these methods, which are frequently used in compositional data analysis, may be linked through parametrizations such as power transformations, linear transformations and convex linear combinations. Since the methods of interest here all lead to visual maps of data, a "movie" can be made where where the linking parameter is allowed to vary in small steps: the results are recalculated "frame by frame" and one can see the smooth change from one method to another. Several of these "movies" will be shown, giving a deeper insight into the similarities and differences between these methods.
    Keywords: Compositional data, contingency tables, correspondence analysis, logratio transformation, singular value decomposition, spectral map, weighting
    JEL: C19 C88
    Date: 2008–04
  14. By: Chollete, Loran; Heinen, Andreas; Valdesogo, Alfonso
    Abstract: In order to capture observed asymmetric dependence in international financial returns, we construct a multivariate regime-switching model of copulas. We model dependence with one Gaussian and one canonical vine copula regime. Canonical vines are constructed from bivariate conditional copulas and provide a very flexible way of characterizing dependence in multivariate settings. We apply the model to returns from the G5 and Latin American regions, and document two main findings. First, we discover that models with canonical vines generally dominate alternative dependence structures. Second, the choice of copula is important for risk management, because it modifies the Value at Risk (VaR) of international portfolio returns.
    Keywords: Asymmetric dependence; Canonical vine copula; International returns; Regime-Switching; Risk Management; Value-at-Risk.
    JEL: C32 G1 C35
    Date: 2008–02
  15. By: Heckman, James J. (University of Chicago)
    Abstract: This paper presents the econometric approach to causal modeling. It is motivated by policy problems. New causal parameters are defined and identified to address specific policy problems. Economists embrace a scientific approach to causality and model the preferences and choices of agents to infer subjective (agent) evaluations as well as objective outcomes. Anticipated and realized subjective and objective outcomes are distinguished. Models for simultaneous causality are developed. The paper contrasts the Neyman-Rubin model of causality with the econometric approach.
    Keywords: anticipated vs. realized outcomes, subjective and objective evaluations, Neyman-Rubin model, Roy model, econometrics, causality, counterfactuals, treatment effects
    JEL: B41
    Date: 2008–03
  16. By: Jing Zhang (Centre d'Economie de la Sorbonne et East China Normal University); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics)
    Abstract: This paper develops a method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. As the association between the underlying assets may vary over time, the dynamic copula with time-varying parameter offers a better alternative to any static model for dependence structure and even to the dynamic copula model determined by dynamic dependence measure. Therefore, the proposed method proves to play an important role in pricing bivariate options. The approach is illustrated with one type of better-of-two-markets claims : call option on the better performer of Shanghai and Shenzhen stock composite indexes. Results show that the option prices obtained by the time-varying copula model differ substantially from the prices implied by the static copula model and even the dynamic copula model derived from the dynamic dependence measure. Moreover, the empirical work displays the advantages of the suggested method.
    Keywords: Call-on-max option, GARCH process, Kendall's tau, Copula, dynamic Copula, time-varying parameter.
    JEL: C02 C32 G13
    Date: 2008–02
  17. By: Bente Halvorsen and Bodil M. Larsen (Statistics Norway)
    Abstract: Differences in estimated parameters depending on the frequency of aggregate data have been reported in several fields of economic research. Some differences are due to seasonal variations in demand, but temporal aggregation bias is reported even in seasonally adjusted models. These biases have been explained by time-nonseparable preferences and excluded dynamic components. We show that it is possible to observe temporal aggregation bias in a seasonally adjusted static model even when preferences are time-separable. This is because of changes in the distribution of exogenous factors describing the variation in seasonal demand across consumers. To show this, we develop a method for aggregation based on an Almost Ideal Demand System, where demand response varies across both consumers and time
    Keywords: Temporal aggregation; Consumer demand; Heterogeneity
    JEL: C43 D1
    Date: 2008–04

This nep-ecm issue is ©2008 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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