nep-ets New Economics Papers
on Econometric Time Series
Issue of 2006‒10‒07
seven papers chosen by
Yong Yin
SUNY at Buffalo

  1. Panels with Nonstationary Multifactor Error Structures By George Kapetanios; M. Hashem Pesaran; Takashi Yamagata
  2. Time-Series Models in Marketing By Dekimpe, M.G.; Franses, Ph.H.B.F.; Hanssens, D.M.; Naik, P.
  3. Wake me up before you GO-GARCH By H. Peter Boswijk; Roy van der Weide
  4. Testing for Cointegrating Rank via Model Selection: Evidence from 165 Data Sets By Badi H. Baltagi; Zijun Wang
  5. Forecast errors and the macroeconomy — a non-linear relationship? By Ulrich Fritsche; Joerg Doepke
  6. Spatial Time-Series Modeling: A review of the proposed methodologies By Yiannis Kamarianakis; Poulicos Prastacos

  1. By: George Kapetanios; M. Hashem Pesaran; Takashi Yamagata
    Abstract: The presence of cross-sectionally correlated error terms invalidates much inferential theory of panel data models. Recently work by Pesaran (2006) has suggested a method which makes use of cross-sectional averages to provide valid inference for stationary panel regressions with multifactor error structure. This paper extends this work and examines the important case where the unobserved common factors follow unit root processes and could be cointegrated. It is found that the presence of unit roots does not affect most theoretical results which continue to hold irrespective of the integration and the cointegration properties of the unobserved factors. This finding is further supported for small samples via an extensive Monte Carlo study. In particular, the results of the Monte Carlo study suggest that the cross-sectional average based method is robust to a wide variety of data generation processes and has lower biases than all of the alternative estimation methods considered in the paper.
    Keywords: cross section dependence, large panels, unit roots, principal components, common correlated effects
    JEL: C12 C13 C33
    Date: 2006
  2. By: Dekimpe, M.G.; Franses, Ph.H.B.F.; Hanssens, D.M.; Naik, P. (Erasmus Research Institute of Management (ERIM), RSM Erasmus University)
    Abstract: Marketing data appear in a variety of forms. An often-seen form is time-series data, like sales per month, prices over the last few years, market shares per week. Time-series data can be summarized in time-series models. In this chapter we review a few of these, focusing in particular on domains that have received considerable attention in the marketing literature. These are (1) the use of persistence modelling and (2) the use of state space models.
    Keywords: Time Series;Marketing;Persistence;State Space;
    Date: 2006–09–20
  3. By: H. Peter Boswijk (Universiteit van Amsterdam); Roy van der Weide (World Bank)
    Abstract: In this paper we present a new three-step approach to the estimation of Generalized Orthogonal GARCH (GO-GARCH) models, as proposed by van der Weide (2002). The approach only requires (non-linear) least-squares methods in combination with univariate GARCH estimation, and as such is computationally attractive, especially in larger-dimensional systems, where a full likelihood optimization is often infeasible. The effectiveness of the method is investigated using Monte Carlo simulations as well as a number of empirical applications.
    Keywords: Multivariate GARCH; Non-Linear Least-Squares; Maximum Likelihood
    JEL: C13 C32
    Date: 2006–09–19
  4. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, Syracuse, NY 13244-1020); Zijun Wang
    Abstract: The model selection approach has been proposed as an alternative to the popular tests for cointegration such as the residual-based ADF test and the system-based trace test. Using information criteria, we conduct cointegration tests on 165 data sets used in published studies. The empirical results demonstrate the usefulness of the model selection approach for applied researchers.
    JEL: C21 C23
    Date: 2006–07
  5. By: Ulrich Fritsche (Department for Economics and Politics, University of Hamburg, and DIW Berlin); Joerg Doepke (Fachhochschule Merseburg)
    Abstract: The paper analyses reasons for departures from strong rationality of growth and inflation forecasts based on annual observations from 1963 to 2004. We rely on forecasts from the joint forecast of the so-called "six leading" forecasting institutions in Germany and argue that violations of the rationality hypothesis are due to relatively few large forecast errors. These large errors are shown - based on evidence from probit models - to correlate with macroeconomic fundamentals, especially on monetary factors. We test for a non-linear relation between forecast errors and macroeconomic fundamentals and find evidence for such a non-linearity for inflation forecasts.
    Keywords: forecast error evaluation, non-linearities, business cycles
    JEL: E32 E37 C52 C53
    Date: 2006–02
  6. By: Yiannis Kamarianakis (Department of Economics, University of Crete); Poulicos Prastacos (Regional Analysis Division, Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas)
    Abstract: This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better suited for applications of large spatial and temporal dimension whereas the latter can be realistically performed when the number of locations of the study is rather small. Next, we consider models that aim to describe relationships between variables with a spatio-temporal reference and discuss the general class of dynamic space-time models in the framework presented by Elhorst (2001). Each model class is introduced through a motivating application.
    Keywords: spatial time-series, space-time models, STARIMA, Bayesian Vector Autoregressions
    Date: 2006–03
  7. By: Yiannis Kamarianakis (Regional Analysis Division, Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas)
    Abstract: Despite the fact that the amount of datasets containing long economic time series with a spatial reference has significantly increased during the years, the presence of integrated techniques that aim to describe the temporal evolution of the series while accounting for the location of the measurements and their neighboring relations is very sparse in the econometric literature. This paper shows how the Hierarchical Bayesian Space Time model presented by Wikle, Berliner and Cressie (Environmental and Ecological Statistics, l998) for temperature modeling, can be tailored to model relationships between variables that have both a spatial and a temporal reference. The first stage of the hierarchical model includes a set of regression equations (each one corresponding to a different location) coupled with a dynamic space-time process that accounts for the unexplained variation. At the second stage, the regression parameters are endowed with priors that reflect the neighboring relations of the locations under study; moreover, the spatio-temporal dependencies in the dynamic process for the unexplained variation are being established. Putting hyperpriors on previous stages’ parameters completes the Bayesian formulation, which can be implemented in a Markov Chain Monte Carlo framework. The proposed modeling strategy is useful in quantifying the temporal evolution in relations between economic variables and this quantification may serve for excess forecasting accuracy.
    Keywords: space-time models
    Date: 2006–03

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