nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒10‒18
nine papers chosen by
Yong Yin
SUNY at Buffalo

  1. Forecasting aggregate demand: analytical comparison of top-down and bottom-up approaches in a multivariate exponential smoothing framework By Giacomo Sbrana; Andrea Silvestrini
  2. Uncertainty and heterogeneity in factor models forecasting By Matteo Luciani; Libero Monteforte
  3. Nowcasting causality in mixed frequency vector autoregressive models By Götz T.B.; Hecq A.W.
  4. Detecting and Forecasting Large Deviations and Bubbles in a Near-Explosive Random Coefficient Model By Banerjee, Anurag N.; Chevillon, Guillaume; Kratz, Marie
  5. Entropy testing for nonlinearity in time series By SIMONE GIANNERINI; ESFANDIAR MAASOUMI; ESTELA BEE DAGUM
  6. Asymptotic Inference for Dynamic Panel Estimators of Innite Order Autoregressive Processes By Yoon-Jin Lee; Ryo Okui; Mototsugu Shintani
  7. Sign-based portmanteau test for ARCH-type models with heavy-tailed innovations By Chen, Min; Zhu, Ke
  8. Econometric Issues when Modelling with a Mixture of I(1) and I(0) Variables By Lance A Fisher; Syeon-seung Huh; Adrian Pagan
  9. Nets: Network estimation for time series By Matteo Barigozzi; Christian T. Brownlees

  1. By: Giacomo Sbrana (Rouen Business School); Andrea Silvestrini (Bank of Italy)
    Abstract: Forecasting aggregate demand is a crucial matter in all industrial sectors. In this paper, we provide the analytical prediction properties of top-down (TD) and bottom-up (BU) approaches when forecasting aggregate demand, using multivariate exponential smoothing as demand planning framework. We extend and generalize the results obtained by Widiarta, Viswanathan and Piplani (2009) by employing an unrestricted multivariate framework allowing for interdependency between the variables. Moreover, we establish the necessary and sufficient condition for the equality of mean squared errors (MSEs) of the two approaches. We show that the condition for the equality of MSEs also holds even when the moving average parameters of the individual components are not identical. In addition, we show that the relative forecasting accuracy of TD and BU depends on the parametric structure of the underlying framework. Simulation results confirm our theoretical findings. Indeed, the ranking of TD and BU forecasts is led by the parametric structure of the underlying data generation process, regardless of possible misspecification issues.
    Keywords: top-down and bottom-up forecasting, multivariate exponential smoothing.
    JEL: C32 C43
    Date: 2013–09
  2. By: Matteo Luciani (Université libre de Bruxelles); Libero Monteforte (Bank of Italy)
    Abstract: In this paper, we exploit the heterogeneity in the forecasts obtained by estimating different factor models to measure forecast uncertainty. Our approach is simple and intuitive. It consists first in selecting all the models that outperform some benchmark model, and then in constructing an empirical distribution of the forecasts produced by them. We interpret this distribution as a measure of uncertainty. We illustrate our methodology by means of a forecasting exercise using a large database of Italian data from 1982 to 2009.
    Keywords: factor models, model uncertainty, forecast combination, density forecast
    JEL: C13 C32 C33 C52 C53
    Date: 2013–09
  3. By: Götz T.B.; Hecq A.W. (GSBE)
    Abstract: This paper introduces the notion of nowcasting causality for mixed-frequency VARs as the mixed-frequency version of instantaneous causality. We analyze the relationship between nowcasting and Granger causality in the mixed-frequency VAR setting of Ghysels 2012 and illustrate that nowcasting causality can have a crucial impact on the significance of contemporaneous or lagged high-frequency variables in standard MIDAS regression models.
    Keywords: Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models;
    JEL: C21 C22 C32
    Date: 2013
  4. By: Banerjee, Anurag N. (Durham University Business School); Chevillon, Guillaume (ESSEC Business School); Kratz, Marie (ESSEC Business School et Mathématiques appliquées Paris 5 (MAP5))
    Abstract: This paper proposes a Near Explosive Random-Coefficient autoregressive model for asset pricing which accommodates both the fundamental asset value and the recurrent presence of autonomous deviations or bubbles. Such a process can be stationary with or without fat tails, unit-root nonstationary or exhibit temporary exponential growth. We develop the asymptotic theory to analyze ordinary least-squares (OLS) estimation. One important theoretical observation is that the estimator distribution in the random coefficient model is qualitatively different from its distribution in the equivalent fixed coefficient model. We conduct recursive and full-sample inference by inverting the asymptotic distribution of the OLS test statistic, a common procedure in the presence of localizing parameters. This methodology allows to detect the presence of bubbles and establish probability statements on their apparition and devolution. We apply our methods to the study of the dynamics of the Case-Shiller index of U.S. house prices. Focusing in particular on the change in the price level, we provide an early detection device for turning points of booms and bust of the housing market.
    Keywords: Bubbles; Random Coefficient Autoregressive Model; Local Asymptotics; Asset Prices
    JEL: C22 C53 C58 G12
    Date: 2013–09
    Abstract: We propose a test for identification of nonlinear serial dependence in time series against the 15 general “null” of linearity, in contrast to the more widely examined null of “independence”. The approach is based on a combination of an entropy dependence metric, possessing many desirable properties and used as a test statistic, together with i) a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity; ii) the use of a smoothed sieve bootstrap scheme. We show how the tests can be employed to detect the lags at which a 20 significant nonlinear relationship is expected in the same fashion as the autocorrelation function is used for linear models. We prove the asymptotic validity of the procedures proposed and of the corresponding inferences. The small sample size performance of the tests is assessed through a simulation study. Applications to real data sets of different kinds are also presented.
    Date: 2013–08
  6. By: Yoon-Jin Lee (Department of Economics, Indiana University); Ryo Okui (Institute of Economic Research, Kyoto University); Mototsugu Shintani (Department of Economics, Vanderbilt University)
    Abstract: In this paper we consider the estimation of a dynamic panel autoregressive (AR) process of possibly innite order in the presence of individual effects. We utilize the sieve AR approximation with its lag order increasing with the sample size. We establish the consistency and asymptotic normality of the standard dynamic panel data estimators, including the xed effects estimator, the gen- eralized methods of moments estimator and Hayakawa's instrumental variables estimator, using double asymptotics under which both the cross-sectional sam- ple size and the length of time series tend to innity. We also propose a bias- corrected xed effects estimator based on the asymptotic result. Monte Carlo simulations demonstrate that the estimators perform well and the asymptotic approximation is useful. As an illustration, proposed methods are applied to dynamic panel estimation of the law of one price deviations among US cities.
    Keywords: Autoregressive Sieve Estimation, Bias Correction, Double Asymptotics, Fixed Effects Estimator, GMM, Instrumental Variables Estimator.
    JEL: C13 C23 C26
    Date: 2013–10
  7. By: Chen, Min; Zhu, Ke
    Abstract: This paper proposes a sign-based portmanteau test for diagnostic checking of ARCH-type models estimated by the least absolute deviation approach. Under the strict stationarity condition, the asymptotic distribution is obtained. The new test is applicable for very heavy-tailed innovations with only finite fractional moments. Simulations are undertaken to assess the performance of the sign-based test, as well as a comparison with other two portmanteau tests. A real empirical example for exchange rates is given to illustrate the practical usefulness of the test.
    Keywords: ARCH-type model; heavy-tailed innovation; LAD estimator; model diagnostics; sign-based portmanteau test
    JEL: C1 C12
    Date: 2013–10–08
  8. By: Lance A Fisher (Macquarie University); Syeon-seung Huh (Yonsei University); Adrian Pagan (University of Sydney)
    Abstract: This paper considers structural models when both I(1) and I(0) variables are present. It is necessary to extend the traditional classification of shocks as permanent and transitory, and we do this by introducing a mixed shock. The extra shocks coming from introducing I(0) variables into a system are then classified as either mixed or transitory. Conditions are derived upon the nature of the SVAR in the event that these extra shocks are transitory. We then analyse what happens when there are mixed shocks, finding that it changes a number of ideas that have become established from the cointegration literature. The ideas are illustrated using a well-known SVAR where there are mixed shocks. This SVAR is re-formulated so that the extra shocks coming from the introduction of I(0) variables do not affect relative prices in the long-run and it is found that this has major implications for whether there is a price puzzle. It is also shown how to handle long-run parametric restrictions when some shocks are identified using sign restrictions.
    Keywords: Mixed models, transitory shocks, mixed shocks, long-run restrictions, sign restrictions, instrumental variables
    JEL: C32 C36 C51
    Date: 2013–10–09
  9. By: Matteo Barigozzi; Christian T. Brownlees
    Abstract: This work proposes novel network analysis techniques for multivariate time series. We define the network of a multivariate time series as a graph where vertices denote the components of the process and edges denote non zero long run partial correlations. We then introduce a two step LASSO procedure, called NETS, to estimate high dimensional sparse Long Run Partial Correlation networks. This approach is based on a VAR approximation of the process and allows to decompose the long run linkages into the contribution of the dynamic and contemporaneous dependence relations of the system. The large sample properties of the estimator are analysed and we establish conditions for consistent selection and estimation of the non zero long run partial correlations. The methodology is illustrated with an application to a panel of U.S. bluechips.
    Keywords: Networks, Multivariate Time Series, Long Run Covariance, LASSO
    JEL: C01 C32 C52
    Date: 2013–10

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