nep-ecm New Economics Papers
on Econometrics
Issue of 2014‒04‒29
eight papers chosen by
Sune Karlsson
Orebro University

  1. Heteroskedasticity-and-Autocorrelation-Consistent Bootstrapping By Russel Davidson; Andrea Monticini
  2. A Factor Analytical Method to Interactive Effects Dynamic Panel Models with or without Unit Root By Westerlund, Joakim; Norkute, Milda
  3. Asymptotic Inferences for an AR(1) Model with a Change Point: Stationary and Nearly Non-stationary Cases By Pang, Tianxiao; Zhang, Danna; Chong, Terence Tai-Leung
  4. Boosting multi-step autoregressive forecasts By Souhaib Ben Taieb; Rob J Hyndman
  5. Cluster analysis of weighted bipartite networks: a new copula-based approach By Alessandro Chessa; Irene Crimaldi; Massimo Riccaboni; Luca Trapin
  6. Inference in the Presence of Redundant Moment Conditions and the Impact of Government Health Expenditure on Health Outcomes in England By Martyn Andrews; Obbey Elamin; Alastair R. Hall; Kostas Kyriakoulis; Matthew Sutton
  7. Measuring and testing spatial mass concentration with micro-geographic data By Thomas-Agnan, Christine; Bonneu, Florent
  8. Hierarchical maximum likelihood parameter estimation for cumulative prospect theory: Improving the reliability of individual risk parameter estimates By Ryan O. Murphy; Robert H.W. ten Brincke

  1. By: Russel Davidson (Department of Economics and CIREQ McGill University); Andrea Monticini (Dipartimento di Economia e Finanza, Università Cattolica del Sacro Cuore)
    Abstract: In many, if not most, econometric applications, it is impossible to estimate consistently the elements of the white-noise process or processes that underlie the DGP. A common example is a regression model with heteroskedastic and/or autocorrelated disturbances,where the heteroskedasticity and autocorrelation are of unknown form. A particular version of the wild bootstrap can be shown to work very well with many models, both univariate and multivariate, in the presence of heteroskedasticity. Nothing comparable appears to exist for handling serial correlation. Recently, there has been proposed something called the dependent wild bootstrap. Here, we extend this new method, and link it to the well-known HAC covariance estimator, in much the same way as one can link the wild bootstrap to the HCCME. It works very well even with sample sizes smaller than 50, and merits considerable further study.
    Keywords: Bootstrap, time series, wild bootstrap, dependent wild bootstrap,HAC covariance matrix estimator
    JEL: C12 C22 C32
    Date: 2014–03
  2. By: Westerlund, Joakim (Deakin University); Norkute, Milda (Department of Economics, Lund University)
    Abstract: In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.
    Keywords: Interactive fixed effects; Dynamic panel data models; Unit root; Factor analytical method
    JEL: C12 C13 C33 C36
    Date: 2014–04–14
  3. By: Pang, Tianxiao; Zhang, Danna; Chong, Terence Tai-Leung
    Abstract: This paper examines the asymptotic inference for AR(1) models with a possible structural break in the AR parameter β near the unity at an unknown time k₀. Consider the model y_{t}=β₁y_{t-1}I{t≤k₀}+β₂y_{t-1}I{t>k₀}+ε_{t}, t=1,2,⋯,T, where I{⋅} denotes the indicator function. We examine two cases: Case (I) |β₁|
    Keywords: AR(1) model, Change point, Domain of attraction of the normal law, Limiting distribution, Least squares estimator.
    JEL: C22
    Date: 2013–12–30
  4. By: Souhaib Ben Taieb; Rob J Hyndman
    Abstract: Multi-step forecasts can be produced recursively by iterating a one-step model, or directly using a specific model for each horizon. Choosing between these two strategies is not an easy task since it involves a trade-off between bias and estimation variance over the forecast horizon. Using a nonlinear machine learning model makes the tradeoff even more difficult. To address this issue, we propose a new forecasting strategy which boosts traditional recursive linear forecasts with a direct strategy using a boosting autoregression procedure at each horizon. First, we investigate the performance of the proposed strategy in terms of bias and variance decomposition of the error using simulated time series. Then, we evaluate the proposed strategy on real-world time series from two forecasting competitions. Overall, we obtain excellent performance with respect to the standard forecasting strategies.
    Keywords: Multi-step forecasting; forecasting strategies; recursive forecasting; direct forecasting; linear time series; nonlinear time series; boosting
    JEL: C22 C53 C14
    Date: 2014
  5. By: Alessandro Chessa (IMT Lucca Institute for Advanced Studies); Irene Crimaldi (IMT Lucca Institute for Advanced Studies); Massimo Riccaboni (IMT Lucca Institute for Advanced Studies); Luca Trapin (IMT Lucca Institute for Advanced Studies)
    Abstract: In this work we are interested in identifying clusters of "positional equivalent" actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. The main contribution of our work is twofold. First, we develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Second, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data.
    Keywords: Clustering, complex network, copula function, positional analysis, weighted bipartite network
    JEL: F1 C6
    Date: 2014–04
  6. By: Martyn Andrews; Obbey Elamin; Alastair R. Hall; Kostas Kyriakoulis; Matthew Sutton
    Date: 2014
  7. By: Thomas-Agnan, Christine; Bonneu, Florent
    Abstract: We address the question of measuring and testing industrial spatial concentration based on micro-geographic data with distance based methods. We discuss the basic requirements for such measures and we propose four additional requirements. We also discuss the null assumptions classically used for testing aggregation of a particular sector and propose an alternative point of view. Our general index measure involves a cumulative and a non-cumulative version. This allows us to propose an alternative version of the Duranton Overman index with a proper baseline as well as a cumulative version of this same index. We illustrate the approach with some simulated data.
    Keywords: Spatial concentration, marked point processes, agglomeration, spatial clusters.
    Date: 2014–01
  8. By: Ryan O. Murphy; Robert H.W. ten Brincke
    Abstract: Individual risk preferences can be identified by using decision models with tuned parameters that maximally fit a set of risky choices made by a decision maker. A goal of this model fitting procedure is to isolate parameters that correspond to stable risk preferences. These preferences can be modeled as an individual difference, indicating a particular decision maker's tastes and willingness to tolerate risk. Using hierarchical statistical methods we show significant improvements in the reliability of individual risk preference parameters over other common estimation methods. This hierarchal procedure uses population level information (in addition to an individual's choices) to break ties (or near-ties) in the fit quality for sets of possible risk preference parameters. By breaking these statistical ``ties'' in a sensible way, researchers can avoid overfitting choice data and thus better measure individual differences in people's risk preferences.
    Keywords: Prospect theory, Risk preference, Decision making under risk, Hierarchical parameter estimation, Maximum likelihood

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