nep-ecm New Economics Papers
on Econometrics
Issue of 2012‒06‒05
eleven papers chosen by
Sune Karlsson
Orebro University

  1. Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates By Bartolucci, Francesco; Farcomeni, Alessio; Pennoni, Fulvia
  2. A New Semiparametric Volatility Model By Jiangyu Ji; Andre Lucas
  3. Conditional posteriors for the reduced rank regression model By Karlsson, Sune
  4. Valid Locally Uniform Edgeworth Expansions Under Weak Dependence and Sequences of Smooth Transformations By Antonis Demos; Stelios Arvanitis
  5. Two-step approach to Self-Selected Interval Data in Elicitation Surveys By Belyaev, Yuri; Kriström, Bengt
  6. Estimation of the Spatial Weights Matrix under Structural Constraints By Bhattacharjee, Arnab; Jensen-Butler, Chris
  7. FEVD: Just IV or Just Mistaken? By Trevor Breusch; Michael B. Ward; Hoa Thi Minh Nguyen; Tom Kompas
  8. Spatial Interactions in Hedonic Pricing Models: The Urban Housing Market of Aveiro, Portugal By Bhattacharjee, Arnab; de Castro, Eduardo Anselmo; Marques, João Lourenço
  9. Late Again with Defiers By Clément De Chaisemartin; Xavier D'Haultfoeuille
  10. Covariances and relationships between price indices: Notes on a theorem of Ladislaus von Bortkiewicz on linear index functions By von der Lippe, Peter
  11. Short-time asymptotics for marginal distributions of semimartingales By Amel Bentata; Rama Cont

  1. By: Bartolucci, Francesco; Farcomeni, Alessio; Pennoni, Fulvia
    Abstract: We provide a comprehensive overview of latent Markov (LM) models for the analysis of longitudinal data. The main assumption behind these models is that the response variables are conditionally independent given a latent process which follows a first-order Markov chain. We first illustrate the more general version of the LM model which includes individual covariates. We then illustrate several constrained versions of the general LM model, which make the model more parsimonious and allow us to consider and test hypotheses of interest. These constraints may be put on the conditional distribution of the response variables given the latent process (measurement model) or on the distribution of the latent process (latent model). For the general version of the model we also illustrate in detail maximum likelihood estimation through the Expectation-Maximization algorithm, which may be efficiently implemented by recursions known in the hidden Markov literature. We discuss about the model identifiability and we outline methods for obtaining standard errors for the parameter estimates. We also illustrate methods for selecting the number of states and for path prediction. Finally, we illustrate Bayesian estimation method. Models and related inference are illustrated by the description of relevant socio-economic applications available in the literature.
    Keywords: EM algorithm; Bayesian framework; Forward-Backward recursions; Hidden Markov models; Measurement errors; Panel data; Unobserved heterogeneity
    JEL: C10 C33
    Date: 2012–04–13
  2. By: Jiangyu Ji (VU University Amsterdam); Andre Lucas (VU University Amsterdam, and Duisenberg school of finance)
    Abstract: We propose a new semiparametric observation-driven volatility model where the form of the error density directly influences the volatility dynamics. This feature distinguishes our model from standard semiparametric GARCH models. The link between the estimated error density and the volatility dynamics follows from the application of the generalized autoregressive score framework of Creal, Koopman, and Lucas (2012). We provide simulated evidence for the estimation efficiency and forecast accuracy of the new model, particularly if errors are fat-tailed and possibly skewed. In an application to equity return data we find that the model also does well in density forecasting.
    Keywords: volatility clustering; Generalized Autoregressive Score model; kernel density estimation; density forecast evaluation
    JEL: C10 C14 C22
    Date: 2012–05–22
  3. By: Karlsson, Sune (Department of Business, Economics, Statistics and Informatics)
    Abstract: The multivariate reduced rank regression model plays an important role in econo- metrics. Examples include co-integration analysis and models with a factor struc- ture. Geweke (1996) provided the foundations for a Bayesian analysis of this model. Unfortunately several of the full conditional posterior distributions, which forms the basis for constructing a Gibbs sampler for the poster distribution, given by Geweke contains errors. This paper provides correct full conditional posteriors for the re- duced rank regression model under the prior distributions considered by Geweke.
    Keywords: Gibbs sampling; full conditional posterior
    JEL: C11 C30 C53
    Date: 2012–05–27
  4. By: Antonis Demos (; Stelios Arvanitis
    Abstract: In this paper we are concerned with the issue of the existence of locally uniform Edgeworth expansions for the distributions of random vectors. Our motivation resides on the fact that this could enable subsequent uniform approximations of analogous moments and their derivatives. We derive sufficient conditions either in the case of stochastic processes exhibiting weak dependence, or in the case of smooth transformations of such expansions. The combination of the results can lead to the establishment of high order asymptotic properties for estimators of interest.
    Keywords: Locally uniform Edgeworth expansion, formal Edgeworth distribution, weak dependence, smooth transformations, moment approximations, GMM estimators, Indirect estimators, GARCH model.
    JEL: C10 C13
    Date: 2012–05–28
  5. By: Belyaev, Yuri (Department of Forest Economics); Kriström, Bengt (CERE, Centre for Environmental and Resource Economics)
    Abstract: We propose a novel two-step approach to elicitation in surveys and provide supporting statistical theory for the models suggested. The essential idea is to combine self-selected intervals in a first step and then employ brackets generated from the intervals in a second step. In this way we combine the advantages of selfselected intervals, mainly related to the fact that individuals often fi nd it difficult to report a precise point-estimate of a quantity of interest, with the documented usefulness of brackets. Because the brackets are generated from the first sample we sidestep the thorny problem of the optimal design of brackets and additional assumptions on dependency between the self-selected intervals and their points of interest. Our set-up necessitates development of new statistical models. First, we propose a stopping rule for sampling in the first step. Second, Theorem 1 proves that the proposed non-parametric ML-estimator of the underlying distribution function is consistent. Third, a special recursion for quick estimation of the ML-estimators is suggested. Theorem 2 shows that the accuracy of the estimator can be consistently estimated by resampling. Fourth, we have developed an R-package for efficient application of the method. We illustrate the approach using the problem of eliciting willingness-to-pay for a public good.
    Keywords: Interval data; Maximum Likelihood; Turnbull estimator; willingness-to-pay; quantitative elicitation; resampling
    JEL: Q51
    Date: 2012–05–31
  6. By: Bhattacharjee, Arnab; Jensen-Butler, Chris
    Abstract: While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.
    Keywords: Spatial econometrics, Spatial autocorrelation, Spatial weights matrix, Spatial error model, Housing demand, Gradient projection,
    Date: 2011
  7. By: Trevor Breusch; Michael B. Ward; Hoa Thi Minh Nguyen; Tom Kompas
    Abstract: Fixed effects vector decomposition (FEVD) is simply an instrumental variables (IV) estimator with a particular choice of instruments and a special case of the well-known Hausman-Taylor IV procedure. Plümper and Troeger (PT) now acknowledge this point and disown the three-stage procedure that previously defined FEVD. Their old recipe for standard errors, which has regrettably been used in dozens of published research papers, produces dramatic overconfidence in the estimates. Again PT concede the point and now adopt the standard IV formula for standard errors. Knowing that FEVD is an application of IV also has the benefit of focusing attention on the choice of instruments. Now it seems PT claim that the FEVD instruments are always the best choice, on the grounds that one cannot know whether any potential instrument is correlated with the unit effect. One could just as readily make the same specious claim about other estimators, such as ordinary least squares, and support it with similar Monte Carlo assumptions and evidence.
    Date: 2011
  8. By: Bhattacharjee, Arnab; de Castro, Eduardo Anselmo; Marques, João Lourenço
    Abstract: Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes this approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, is applied to the urban housing market of Aveiro, Portugal at two different spatial scales.
    Keywords: Spatial econometrics, Spatial heterogeneity, Spatial dependence, Spatial scale, Hedonic pricing, Statistical factor analysis, Spatial weights matrix,
    Date: 2011
  9. By: Clément De Chaisemartin (EEP-PSE - Ecole d'Économie de Paris - Paris School of Economics - Ecole d'Économie de Paris, PSE - Paris-Jourdan Sciences Economiques - CNRS : UMR8545 - Ecole des Hautes Etudes en Sciences Sociales (EHESS) - Ecole des Ponts ParisTech - Ecole Normale Supérieure de Paris - ENS Paris - INRA); Xavier D'Haultfoeuille (CREST - Centre de Recherche en Économie et Statistique - INSEE - École Nationale de la Statistique et de l'Administration Économique)
    Abstract: We show that the Wald statistic still identifies a causal effect if instrument monotonicity is replaced by a weaker condition, which states that the potential propensities to be treated with or without the instrument should have the same distribution, conditional on potential outcomes. This holds for instance if the slippages between these potential propensities and the average propensity are independent of potential outcomes. In this framework, the Wald statistic identifies a LATE on a population which comprises both compliers and always takers.
    Keywords: Instrumental Variables ; Monotonicity ; Defiers ; Rank Invariance ; Exchangeable random variables
    Date: 2012–05
  10. By: von der Lippe, Peter
    Abstract: The note examines a generalization of a theorem of Bortkiewicz which relates the difference between a Paasche and a Laspeyres price index to a covariance between price and quantity relatives. The generalized theorem is used to demonstrate a number of interesting special applications. It turns out that some known relationships between two index functions can be expressed more elegantly. In other cases where not much is known yet about how the two functions are related to one another, we could establish an interesting equation on the basis of this theorem. This demonstrates the remarkable flexibility and usefulness of the generalized Bortkiewicz - theorem.
    Keywords: price indices of Laspeyres; Paasche; Drobisch; Carli; Dutot; covariance; Bortkiewicz; unit value index
    JEL: C43 E31 C81
    Date: 2012–05–04
  11. By: Amel Bentata (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot); Rama Cont (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - CNRS : UMR7599 - Université Paris VI - Pierre et Marie Curie - Université Paris VII - Paris Diderot)
    Abstract: We study the short-time asymptotics of conditional expectations of smooth and non-smooth functions of a (discontinuous) Ito semimartingale; we compute the leading term in the asymptotics in terms of the local characteristics of the semimartingale. We derive in particular the asymptotic behavior of call options with short maturity in a semimartingale model: whereas the behavior of out-of-the-money options is found to be linear in time, the short time asymptotics of at-the-money options is shown to depend on the fine structure of the semimartingale.
    Keywords: semimartingale ; short-time asymptotics ; marginal distribution ; short maturity asymptotics ; Levy process ; option pricing
    Date: 2012

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