nep-ecm New Economics Papers
on Econometrics
Issue of 2012‒02‒01
fourteen papers chosen by
Sune Karlsson
Orebro University

  1. Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach By Ching Wai (Jeremy) Chiu; Bjørn Eraker; Andrew T. Foerster; Tae Bong Kim; Hernán D. Seoane
  2. Nonparametric LAD Cointegrating Regression By Toshio Honda
  3. The Wavelet-based Estimation for Long Memory Signal Plus Noise Models By Kei Nanamiya
  4. A Moment-Matching Method for Approximating Vector Autoregressive Processes by Finite-State Markov Chains By Nikolay Gospodinov; Damba Lkhagvasuren
  5. "Combinatorial Bootstrap Inference IN in Prtially Identified Incomplete Structural Models" By Marc Henry; Romuald Méango; Maurice Queyranne
  6. A Nonlinear Panel Data Model of Cross-Sectional Dependence By James Mitchell; George Kapetanios; Yongcheol Shin
  7. Which Model to Match ? By Matteo Barigozzi; Roxana Halbleib; David Veredas
  8. Asymptotic Efficiency of the OLS Estimator with Singular Limiting Sample Moment Matrices By Yoshimasa Uematsu
  9. "Sharp Bounds in the Binary Roy Model" By Marc Henry; Ismael Mourifié; Marc Henry
  10. Bayesian estimation of NOEM models: identification and inference in small samples By Enrique Martínez-García; Diego Vilán; Mark Wynne
  11. Identification and Estimation of Gaussian Affine Term Structure Models By James D. Hamilton; Jing Cynthia Wu
  12. Regression with a Slowly Varying Regressor in the Presence of a Unit Root By Yoshimasa Uematsu
  13. What are the consequences of ignoring attributes in choice experiments? An application to ecosystem service values. By Christie, Mike; Colombo, Sergio; Hanley, Nick
  14. Efficient Aggregation of Panel Qualitative Survey Data By James Mitchell; Richard J. Smith; Martin R. Weale

  1. By: Ching Wai (Jeremy) Chiu; Bjørn Eraker; Andrew T. Foerster; Tae Bong Kim; Hernán D. Seoane
    Abstract: Economic data are collected at various frequencies but econometric estimation typically uses the coarsest frequency. This paper develops a Gibbs sampler for estimating VAR models with mixed and irregularly sampled data. The approach allows efficient likelihood inference even with irregular and mixed frequency data. The Gibbs sampler uses simple conjugate posteriors even in high dimensional parameter spaces, avoiding a non-Gaussian likelihood surface even when the Kalman filter applies. Two applications illustrate the methodology and demonstrate efficiency gains from the mixed frequency estimator: one constructs quarterly GDP estimates from monthly data, the second uses weekly financial data to inform monthly output.
    Date: 2011
  2. By: Toshio Honda
    Abstract: We deal with nonparametric estimation in a nonlinear cointegration model whose regressor and dependent variable can be contemporaneously correlated. The asymptotic properties of the Nadaraya-Watson estimator are already examined in the literature. In this paper, we consider nonparametric least absolute deviation (LAD) regression and derive the asymptotic distributions of the local constant and local linear estimators by appealing to the local time approach.
    Keywords: Nonlinear Cointegration, Integrated Process, Local Time, Least Absolute Deviation, Local Polynomial Regression, Bias
    Date: 2011–10
  3. By: Kei Nanamiya
    Abstract: We propose new wavelet-based procedure to estimate the memory parameter of an unobserved process from an observed process affected by noise in order to improve the performance of the estimator by taking into account the dependency of the wavelet coefficients of long memory processes. In our procedure, using the AR (1) approximation for the wavelet transformed long memory processes which is introduced by Craigmile, Guttorp and Percival (2005), we apply the ARMA (1, 1) approximation to the wavelet coefficients of the observed process at each level. We also compare this procedure to the usual wavelet-based procedure by numerical simulations.
    Keywords: Wavelet, Long Memory Process, Measurement Error Problem
    Date: 2011–12
  4. By: Nikolay Gospodinov (Concordia University and CIREQ); Damba Lkhagvasuren (Concordia University and CIREQ)
    Abstract: This paper proposes a moment-matching method for approximating vector autoregressions by finite-state Markov chains. The Markov chain is constructed by targeting the conditional moments of the underlying continuous process. The proposed method is more robust to the number of discrete values and tends to outperform the existing methods for approximating multivariate processes over a wide range of the parameter space, especially for highly persistent vector autoregressions with roots near the unit circle.
    Keywords: Markov Chain, Vector Autoregressive Processes, Functional Equation, Numerical Methods, Moment Matching
    JEL: C15 C60
    Date: 2011–06–08
  5. By: Marc Henry (Department of Economics, Université de Montréal); Romuald Méango (Department of Economics, Université de Montréal); Maurice Queyranne (Sauder School of Business)
    Abstract: We propose a computationally feasible inference method infinite games of complete information. Galichon and Henry (2011) and Beresteanu, Molchanov, and Molinari (2011) show that such models are equivalent to a collection of moment inequalities that increases exponentially with the number of discrete outcomes. We propose an equivalent characterization based on classical combinatorial optimization methods that alleviates this computational burden and allows the construction of confidence regions with an effcient combinatorial bootstrap procedure that runs in linear computing time. The method can also be applied to the empirical analysis of cooperative and noncooperative games, instrumental variable models of discrete choice and revealed preference analysis. We propose an application to the determinants of long term elderly care choices.
    Date: 2012–01
  6. By: James Mitchell; George Kapetanios; Yongcheol Shin
    Abstract: This paper proposes a nonlinear panel data model which can generate endogenously both `weak' and `strong' cross-sectional dependence. The model's distinguishing characteristic is that a given agent's behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as both a structural and reduced form vehicle to model different types of cross-sectional dependence, including evolving clusters.
    Keywords: Nonlinear Panel Data Model; Clustering; Cross-section Dependence; Factor Models; Monte Carlo Simulations; Application to Stock Returns and Inflation Expectations
    JEL: C31 C33 C51 E31 G14
    Date: 2012–01
  7. By: Matteo Barigozzi; Roxana Halbleib; David Veredas
    Abstract: The asymptotic efficiency of the indirect estimation methods, such as the efficient method of moments and indirect inference, depends on the choice of the auxiliary model. Up to date, this choice is somehow ad hoc and based on an educated guess of the researcher. In this article we develop three information criteria that help the user to optimize the choice among nested and non–nested auxiliary models. They are the indirect analogues of the widely used Akaike, Bayesian and Hannan–Quinn criteria. A thorough Monte Carlo study based on two simple and illustrative models shows the usefulness of the criteria.
    Keywords: Indirect inference; efficient method of moments; auxiliary model; information criteria; asymptotic efficiency
    JEL: C13 C52
    Date: 2012–01
  8. By: Yoshimasa Uematsu
    Abstract: This paper presents a time series model that has an asymptotically efficient ordinary least squares (OLS) estimator, irrespective of the singularity of its limiting sample moment matrices. In the literature on stationary time series analysis, Grenander and Rosenblatt's (1957) (G-R) classical result is used to judge the asymptotic efficiency of regression coefficients on deterministic regressors satisfying Grenander's condition. Without this condition, however, it is not obvious that the model is efficient. In this paper, we introduce such a model by proving the efficiency of the model with a slowly varying (SV) regressor under the same condition on error terms constrained in G-R. This kind of regressor is known to display asymptotic singularity in the sample moment matrices, as in Phillips (2007), such that Grenander's condition fails.
    Date: 2011–10
  9. By: Marc Henry (Département de sciences économiques, Université de Montréal); Ismael Mourifié (Département de sciences économiques, Université de Montréal); Marc Henry (Faculty of Economics, University of Tokyo)
    Abstract: We derive the empirical content of an instrumental variables model of sectorial choice with binary outcomes. Assumptions on selection include the simple, extended and generalized Roy models. The derived bounds are nonparametric intersection bounds and are simple enough to lend themselves to existing inference methods. Identification implications of exclusion restrictions are also derived.
    Date: 2012–01
  10. By: Enrique Martínez-García; Diego Vilán; Mark Wynne
    Abstract: The global slack hypothesis (e.g., Martínez-García and Wynne [2010]) is central to the discussion of the trade-offs monetary policy faces in an increasingly more open world economy. Open-Economy (forward-looking) New Keynesian Phillips curves describe how expected future inflation and a measure of global output gap (global slack) affect the current inflation rate.> ; This paper studies the (potential) weak identification of these relationships in the context of a fully specified structural model using Bayesian estimation techniques. We trace the problems to sample size, rather than misspecification bias. We conclude that standard macroeconomic time series with a coverage of less than forty years are subject to potentially serious identification issues, and also to model selection errors. We recommend estimation with simulated data prior to bringing the model to the actual data as a way of detecting parameters that are susceptible to weak identification in short samples.
    Keywords: Macroeconomics - Econometric models
    Date: 2012
  11. By: James D. Hamilton; Jing Cynthia Wu
    Abstract: This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.
    JEL: C13 E43 G12
    Date: 2012–01
  12. By: Yoshimasa Uematsu
    Abstract: This paper considers the regression model with a slowly varying (SV) regressor in the presence of a unit root in serially correlated disturbances. This regressor is known to be asymptotically collinear with the constant term; see Phillips (2007). Under nonstationarity, we find that the estimated coefficients of the constant term and the SV regressor are asymptotically normal, but neither is consistent. Further, we derive the limiting distribution of the unit root test statistic. We may here observe that the finite sample approximation to the limiting one is not monotone and it is poor due to the influence of the collinear regressor. In order to construct a well-behaved test statistic, we recommend dropping the constant term intentionally from the regression and computing the statistics, which are still consistent under the true model having the constant term. The powers and sizes of these statistics are found to be well-behaved through simulation studies. Finally, these results are extended to general Phillips and Perron-type statistics.
    Date: 2011–10
  13. By: Christie, Mike; Colombo, Sergio; Hanley, Nick
    Abstract: This paper investigates the sensitivity of choice experiment values for ecosystem services to "attribute non-attendance". We consider three cases of attendance, namely that people may always, sometimes or never pay attention to a given attribute in making their choices. This allows a series of models to be estimated which address the following questions: To what extent do respondents attend to attributes in choice experiments? What is the impact of alternative strategies for dealing with attribute non-attendance? Can respondents self-report non-attendance? Do respondents partially attend to attributes, and what are the implications of this for willingness to pay estimates? Our results show that allowing for the instance of "sometimes attending" to attributes in making choices offers advantages over methods employed thus far in the literature.
    Keywords: stated preference; ecosystem services; Biodiversity; attribute non-attendance; Choice experiments
    Date: 2011–12
  14. By: James Mitchell; Richard J. Smith; Martin R. Weale
    Abstract: Qualitative business survey data are used widely to provide indicators of economic activity ahead of the publication of official data. Traditional indicators exploit only aggregate survey information, namely the proportions of respondents who report “up” and “down”. This paper examines disaggregate or firm-level survey responses. It considers how the responses of the individual firms should be quantified and combined if the aim is to produce an early indication of official output data. Having linked firms’ categorical responses to official data using ordered discrete choice models, the paper proposes a statistically efficient means of combining the disparate estimates of aggregate output growth which can be constructed from the responses of individual firms. An application to firm-level survey data from the Confederation of British Industry shows that the proposed indicator can provide early estimates of output growth more accurately than traditional indicators.
    Keywords: Survey Data; Indicators; Quantification; Forecasting; Forecast Combination
    JEL: C35 C53 C80
    Date: 2011–12

This nep-ecm issue is ©2012 by Sune Karlsson. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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