nep-ecm New Economics Papers
on Econometrics
Issue of 2013‒03‒02
twelve papers chosen by
Sune Karlsson
Orebro University

  1. Composite Quantile Regression for the Single-Index Model By Yan Fan; Wolfgang Karl Härdle; Weining Wang; Lixing Zhu
  2. Parameter estimation for an affine two factor model By Matyas Barczy; Leif Doering; Zenghu Li; Gyula Pap
  3. Model Equivalence Tests in a Parametric Framework By Lavergne, Pascal
  4. Factor Sreening For Simulation With Multiple Responses: Sequential Bifurcation By Shi, W.; Kleijnen, Jack P.C.; Liu, Zhixue
  5. Modelling Firm-Product Level Trade: A Multi-Dimensional Random Effects Panel Data Approach By Daria Pus; László Mátyás; Cecilia Hornok
  6. Neither Fixed nor Random: Weighted Least Squares Meta-Analysis By T.D. Stanley; Hristos Doucouliagos
  7. External Validation of Voter Turnout Models by Concealed Parameter Recovery By Antonio Merlo; Thomas R.Palfrey
  8. Model Switching and Model Averaging in Time-Varying Parameter Regression Models By Miguel Belmonte; Gary Koop
  9. Gibbs Samplers for VARMA and Its Extensions By Joshua C.C. Chan; Eric Eisenstat
  10. Using VARs and TVP-VARs with Many Macroeconomic Variables By Gary Koop
  11. A comprehensive characterization of recurrences in time series By R\'emy Chicheportiche; Anirban Chakraborti
  12. The dynamics of co-jumps, volatility and correlation By Adam Clements; Yin Liao

  1. By: Yan Fan; Wolfgang Karl Härdle; Weining Wang; Lixing Zhu
    Abstract: Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a projection based single index model specifi- cation. For very high dimensional regressors X one faces yet another dimensionality problem and needs to balance precision vs. dimension. Such a balance may be achieved by combining semiparametric ideas with variable selection techniques.
    Keywords: Quantile Single-index Regression, Minimum Average Contrast Estimation, Co- VaR estimation, Composite quasi-maximum likelihood estimation, Lasso, Model selection
    JEL: C00 C14 C50 C58
    Date: 2013–02
  2. By: Matyas Barczy; Leif Doering; Zenghu Li; Gyula Pap
    Abstract: For an affine two factor model, we study the asymptotic properties of the maximum likelihood and least squares estimators of some appearing parameters in the so-called subcritical (ergodic) case based on continuous time observations. We prove strong consistency and asymptotic normality of the estimators in question.
    Date: 2013–02
  3. By: Lavergne, Pascal
    Abstract: In empirical research, one commonly aims to obtain evidence in favor of re- strictions on parameters, appearing as an economic hypothesis, a consequence of economic theory, or an econometric modeling assumption. I propose a new theoret- ical framework based on the Kullback-Leibler information to assess the approximate validity of multivariate restrictions in parametric models. I construct tests that are locally asymptotically maximin and locally asymptotically uniformly most powerful invariant. The tests are applied to three different empirical problems.
    Keywords: Hypothesis testing, Parametric methods.
    JEL: C12 C52
    Date: 2013–02
  4. By: Shi, W.; Kleijnen, Jack P.C.; Liu, Zhixue (Tilburg University, Center for Economic Research)
    Abstract: Abstract: Factor screening searches for the really important inputs (factors) among the many inputs that are changed in a realistic simulation experiment. Sequential bifurcation (SB) is a sequential method that changes groups of inputs simultaneously. SB is the most efficient and effective method if the following assumptions are satisfied: (i) second-order polynomials are adequate approximations of the input/output functions implied by the simulation model; (ii) the signs of all first-order effects are known; (iii) if two inputs have no important first-order effects, then they have no important second-order effects either (heredity property). This paper examines SB for random simulation with multiple responses (outputs), called multiresponse SB (MSB). This MSB selects groups of inputs such that within a group all inputs have the same sign for a specific type of output, so no cancellation of first-order effects occurs. MSB also applies Wald’s sequential probability ratio test (SPRT) to obtain enough replicates for correctly classifying a group effect or an individual effect as important or unimportant. MSB enables efficient selection of the initial number of replicates in SPRT. The paper also proposes a procedure to validate the three assumptions of MSB. The performance of MSB is examined through extensive Monte Carlo experiments that satisfy all MSB assumptions, and through a case study representing a logistic system in China; the MSB performance is very promising.
    Keywords: simulation;design of experiments;statistical analysis
    JEL: C0 C1 C9 C15 C44
    Date: 2013
  5. By: Daria Pus; László Mátyás; Cecilia Hornok
    Abstract: The paper deals with the problems of formalizing econometric models on firm-product level trade data sets, or similar economic flows. A multi-dimensional random effects panel data approach is adopted. Several models are introduced taking into account different types of specific effects, interactions and cross correlations. The respective covariance matrixes are derived, as well as procedures to estimate the unknown variance and covariance components, in order to make the Feasible Generalized Least Squares estimation operational. Whenever possible, the spectral decomposition of the covariance matrixes is also provided to make the estimation procedure simpler to implement. Both balanced and unbalanced data sets are considered.
    Date: 2013–02–21
  6. By: T.D. Stanley; Hristos Doucouliagos
    Abstract: We offer what might be regarded as an alternative approach to the conventional ‘fixed-effects’ and ‘random-effects’ meta-analysis (MA), weighted least squares meta-regression analysis (WLS-MRA). Although WLS has been well known for many decades, and its properties are well established, its implications for meta-analysis have yet to be fully explored or appreciated. Our simulations demonstrate that WLS-MRA is preferable to both fixed- and random-effects MA whether or not there is ‘excess’ heterogeneity. We show how a generalization of fixed-effects weighted averages is an improvement over random-effects in those exact cases for which random-effects meta-analysis is designed.
    Keywords: meta-analysis, meta-regression, weighted least squares, fixed-effects, random-effects
    Date: 2013–02–23
  7. By: Antonio Merlo (Department of Economics, University of Pennsylvania); Thomas R.Palfrey (Division of the Humanities and Social Sciences, California Institute of Technology)
    Abstract: We conduct a model validation analysis of several behavioral models of voter turnout, using laboratory data. We call our method of model validation concealed parameter recovery, where estimation of a model is done under a veil of ignorance about some of the experimentally controlled parameters — in this case voting costs. We use quantal response equilibrium as the underlying, common structure for estimation, and estimate models of instrumental voting, altruistic voting, expressive voting, and ethical voting. All the models except the ethical model recover the concealed parameters reasonably well. We also report the results of a counterfactual analysis based on the recovered parameters, to compare the policy implications of the different models about the cost of a subsidy to increase turnout.
    Keywords: Turnout, voting, model validation, parameter recovery, laboratory experiments
    JEL: D72 C52 C92
    Date: 2013–02–11
  8. By: Miguel Belmonte (Department of Economics, University of Strathclyde); Gary Koop (Department of Economics, University of Strathclyde)
    Abstract: This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA)in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in‡ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.
    Keywords: Model switching, forecast combination, switching state space model, infl‡ation forecasting
    JEL: C11 C52 E37 E47
    Date: 2013–01
  9. By: Joshua C.C. Chan; Eric Eisenstat
    Abstract: Empirical work in macroeconometrics has mostly restricted to using VARs, even though there are strong theoretical reasons to consider general VARMAs. This is perhaps because estimation of VARMAs is perceived to be challenging. In this article, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with stochastic volatility and time-varying parameters. We illustrate the methodology through a macroeconomic forecasting exercise. We show that VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.
    JEL: C11 C32 C53
    Date: 2013–02
  10. By: Gary Koop (Department of Economics, University of Strathclyde)
    Abstract: This paper discusses the challenges faced by the empirical macroeconomist and methods for surmounting them. These challenges arise due to the fact that macroeconometric models potentially include a large number of variables and allow for time variation in parameters. These considerations lead to models which have a large number of parameters to estimate relative to the number of observations. A wide range of approaches are surveyed which aim to overcome the resulting problems. We stress the related themes of prior shrinkage, model averaging and model selection. Subsequently, we consider a particular modelling approach in detail. This involves the use of dynamic model selection methods with large TVP-VARs. A forecasting exercise involving a large US macroeconomic data set illustrates the practicality and empirical success of our approach.
    Keywords: Bayesian VAR; forecasting; time-varying coefficients; state-space model
    JEL: C11 C52 E27 E37
    Date: 2013–01
  11. By: R\'emy Chicheportiche; Anirban Chakraborti
    Abstract: Study of recurrences in earthquakes, climate, financial time-series, etc. is crucial to better forecast disasters and limit their consequences. However, almost all the previous phenomenological studies involved only a long-ranged autocorrelation function, or disregarded the multi-scaling properties induced by potential higher order dependencies. Consequently, they missed the facts that non-linear dependences do impact both the statistics and dynamics of recurrence times, and that scaling arguments for the unconditional distribution may not be applicable. We argue that copulas is the correct model-free framework to study non-linear dependencies in time series and related concepts like recurrences. Fitting and/or simulating the intertemporal distribution of recurrence intervals is very much system specific, and cannot actually benefit from universal features, in contrast to the previous claims. This has important implications in epilepsy prognosis and financial risk management applications.
    Date: 2013–02
  12. By: Adam Clements (QUT); Yin Liao (QUT)
    Abstract: Understanding the dynamics of volatility and correlation is a crucially important issue. The literature has developed rapidly in recent years with more sophisticated estimates of volatility, and its associated jump and diffusion components. Previous work has found that jumps at an index level are not related to future volatility. Here we examine the links between co-jumps within a group of large stocks, the volatility of, and correlation between their returns. It is found that the occurrence of common, or co-jumps between the stocks are unrelated to the level of volatility or correlation. On the other hand, both volatility and correlation are lower subsequent to a co-jump. This indicates that co-jumps are a transient event but in contrast to earlier research have a greater impact that jumps at an index level.
    Keywords: Realized volatility, correlation, jumps, co-jumps, point process
    JEL: C22 G00
    Date: 2013–02–06

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