nep-ecm New Economics Papers
on Econometrics
Issue of 2013‒05‒05
thirteen papers chosen by
Sune Karlsson
Orebro University

  1. Econometrics of co-jumps in high-frequency data with noise By Markus Bibinger; Lars Winkelmann; ;
  2. Moment-Based Tests for Discrete Distributions By Bontemps, Christian
  3. Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency By Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
  4. A New Linear Estimator for Gaussian Dynamic Term Structure Models By Antonio Diez de los Rios
  5. Non-parametric transformation regression with non-stationary data By Oliver Linton; Qiying Wang
  6. Nonparametric estimation of multivariate elliptic densities via finite mixture sieves By Heather Battey; Oliver Linton
  7. Comparing the Accuracy of Copula-Based Multivariate Density Forecasts in Selected Regions of Support By Cees Diks; Valentyn Panchenko; Oleg Sokolinskiy; Dick van Dijk
  8. Weak exogeneity in the financial point processes By Xu, Yongdeng
  9. Maximum score estimation of preference parameters for a binary choice model under uncertainty By Le-Yu Chen; Sokbae 'Simon' Lee; Myung Jae Sung
  10. Emprical Relevance of Ambiguity in First Price Auction Models By Gaurab Aryal; Dong-Hyuk Kim
  11. Solution-Driven Specification of DSGE Models By Francisco Blasques
  12. Forecasting with Many Models: Model Confidence Sets and Forecast Combination By Jon D. Samuels; Rodrigo Sekkel
  13. The dynamics of trading duration, volume and price volatility – a vector MEM model By Xu, Yongdeng

  1. By: Markus Bibinger; Lars Winkelmann; ;
    Abstract: We establish estimation methods to determine co-jumps in multivariate high-frequency data with nonsynchronous observations and market microstructure noise. The ex-post quadratic covariation of the signal part, which is modeled by an Itˆo-semimartingale, is estimated with a locally adaptive spectral approach. Locally adaptive thresholding allows to disentangle the co-jump and continuous part in quadratic covariation. Our estimation procedure implicitly renders spot (co-)variance estimators. We derive a feasible stable limit theorem for a truncated spectral estimator of integrated covariance. A test for common jumps is obtained with a wild bootstrap strategy. We give an explicit guideline how to implement the method and test the algorithm in Monte Carlo simulations. An empirical application to intra-day tick-data demonstrates the practical value of the approach.
    Keywords: co-jumps, covolatility estimation, jump detection, microstructure noise, non-synchronous observations, quadratic covariation, spectral estimation, truncation
    JEL: C14 G32 E58
    Date: 2013–05
  2. By: Bontemps, Christian
    Abstract: In this paper, we develop moment-based tests for parametric discrete distributions. Momentbased test techniques are attractive as they provide easy-to-implement test statistics. We propose a general transformation that makes the moments of interest insensitive to the parameter estimation uncertainty. This transformation is valid in some extended family of non differentiable moments that are of great interest in the case of discrete distributions. We compare this strategy with the one which consists in correcting for the parameter uncertainty considering the power function under local alternatives. The special example of the backtesting of VaR forecasts is treated in detail, and we provide simple moments that have good size and power properties in Monte Carlo experiments. Additional examples considered are discrete counting processes and the geometric distribution. We finally apply our method to the backtesting of VaR forecasts derived from a T-GARCH(1,1) model estimated on foreign exchange rate data.
    Keywords: moment-based tests; parameter uncertainty; discrete distributions; Valueat- Risk; backtesting.
    JEL: C12 C15
    Date: 2013–04
  3. By: Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
    Abstract: An efficient estimator is constructed for the quadratic covariation or integrated covolatility matrix of a multivariate continuous martingale based on noisy and non-synchronous observations under high-frequency asymptotics. Our approach relies on an asymptotically equivalent continuous-time observation model where a local generalised method of moments in the spectral domain turns out to be optimal. Asymptotic semiparametric efficiency is established in the Cramér-Rao sense. Main findings are that non-synchronicity of observation times has no impact on the asymptotics and that major efficiency gains are possible under correlation. Simulations illustrate the finite-sample behaviour.
    Keywords: adaptive estimation, asymptotic equivalence, asynchronous observations, integrated covolatility matrix, quadratic covariation, semiparametric efficiency, microstructure noise, spectral estimation
    JEL: C14 C32 C58 G10
    Date: 2013–04
  4. By: Antonio Diez de los Rios
    Abstract: This paper proposes a novel regression-based approach to the estimation of Gaussian dynamic term structure models that avoids numerical optimization. This new estimator is an asymptotic least squares estimator defined by the no-arbitrage conditions upon which these models are built. We discuss some efficiency considerations of this estimator, and show that it is asymptotically equivalent to maximum likelihood estimation. Further, we note that our estimator remains easy-to-compute and asymptotically efficient in a variety of situations in which other recently proposed approaches lose their tractability. We provide an empirical application in the context of the Canadian bond market.
    Keywords: Asset Pricing; Econometric and statistical methods; Interest rates
    JEL: E43 C13 G12
    Date: 2013
  5. By: Oliver Linton (Institute for Fiscal Studies and Cambridge University); Qiying Wang
    Abstract: We examine a kernel regression smoother for time series that takes account of the error correlation structure as proposed by Xiao et al. (2008). We show that this method continues to improve estimation in the case where the regressor is a unit root or near unit root process.
    Date: 2013–04
  6. By: Heather Battey; Oliver Linton (Institute for Fiscal Studies and Cambridge University)
    Abstract: This paper considers the class of p-dimensional elliptic distributions (p ≥ 1) satisfying the consistency property (Kano, 1994) and within this general framework presents a two-stage semiparametric estimator for the Lebesgue density based on Gaussian mixture sieves. Under the online Exponentiated Gradient (EG) algorithm of Helmbold et al. (1997) and without restricting the mixing measure to have compact support, the estimator produces estimates converging uniformly in probability to the true elliptic density at a rate that is independent of the dimension of the problem, hence circumventing the familiar curse of dimensionality inherent to many semiparametric estimators. The rate performance of our estimator depends on the tail behaviour of the underlying mixing density (and hence that of the data) rather than smoothness properties. In fact, our method achieves a rate of at least Op(n-1/4), provided only some positive moment exists. When further moments exist, the rate improves reaching Op(n-3/8) as the tails of the true density converge to those of a normal. Unlike the elliptic density estimator of Liebscher (2005), our sieve estimator always yields an estimate that is valid density, and is also attractive from a practical perspective as it accepts data as a stream, thus significantly reducing computational and storage requirements. Monte Carlo experimentation indicates encouraging finite sample performance over a range of elliptic densities. The estimator is also implemented in a binary classification task using the well-known Wisconsin breast cancer dataset.
    Date: 2013–04
  7. By: Cees Diks (CeNDEF, University of Amsterdam); Valentyn Panchenko (University of New South Wales); Oleg Sokolinskiy (Rutgers Business School); Dick van Dijk (Econometric Institute, Erasmus University Rotterdam)
    Abstract: This paper develops a testing framework for comparing the predictive accuracy of copula-based multivariate density forecasts, focusing on a specific part of the joint distribution. The test is framed in the context of the Kullback-Leibler Information Criterion, but using (out-of-sample) conditional likelihood and censored likelihood in order to focus the evaluation on the region of interest. Monte Carlo simulations document that the resulting test statistics have satisfactory size and power properties in small samples. In an empirical application to daily exchange rate returns we find evidence that the dependence structure varies with the sign and magnitude of returns, such that different parametric copula models achieve superior forecasting performance in different regions of the support. Our analysis highlights the importance of allowing for lower and upper tail dependence for accurate forecasting of common extreme appreciation and depreciation of different currencies.
    Keywords: Copula-based density forecast; Kullback-Leibler Information Criterion; out-of-sample forecast evaluation
    JEL: C12 C14 C32 C52 C53
    Date: 2013–04–19
  8. By: Xu, Yongdeng
    Abstract: This paper analyses issues related to weak exogeneity in a financial point process. We extend the Hausman test of weak exogeneity in a time series model and propose three cases in which weak exogeneity conditions will break down. The simulation study suggested that a failure of the exogeneity assumption implied biased estimators. The bias is very large in the third case non-weak exogeneity, which makes the econometric inferences on the parameters unreliable or even misleading. We then derive an LM test for weak exogeneity. The LM test is attractive because it only requires estimation of the restricted model. The empirical results indicate that the weak exogneity of duration is often rejected for frequently traded stocks, but is less likely to be rejected for infrequently traded stocks.
    Keywords: Weak exogeneity; ACD model; LM test; point process; market microstructure
    Date: 2013–04
  9. By: Le-Yu Chen; Sokbae 'Simon' Lee (Institute for Fiscal Studies and Seoul National University); Myung Jae Sung
    Abstract: This paper develops maximum score estimation of preference parameters in the binary choice model under uncertainty in which the decision rule is affected by conditional expectations. The preference parameters are estimated in two stages: we estimate conditional expectations nonparametrically in the first stage and the preference parameters in the second stage based on Manski (1975, 1985)'s maximum score estimator using the choice data and first stage estimates. The paper establishes consistency and derives the rate of convergence of the corresponding two-stage estimator, which is of independent interest for maximum score estimation with generated regressors. The paper also provides results of some Monte Carlo experiments.
    Keywords: discrete choice, maximum score estimation, generated regressor, preference parameters, M-estimation, cube root asymptotics
    JEL: C12 C13 C14
    Date: 2013–04
  10. By: Gaurab Aryal; Dong-Hyuk Kim
    Abstract: We study the identification and estimation of first-price auction models with independent private values where bidders are risk averse and there is ambiguity about the valuation distribution. When bidders' preferences are represented by the maxmin expected utility of [Gilboa and Schmeidler, 1989], we provide sufficient conditions for nonparametric identification of the valuation distribution and bidders' attitude toward ambiguity, separately from the risk aversion (CRRA, CARA). We propose a semi-parametric method and apply it to two datasets, one from experimental auctions and the other from USFS timber auctions. We find, for both cases, that bidders are not only risk averse but also ambiguity averse. In addition, we consider the multiplier preferences of [Hansen and Sargent, 2001] and identify the valuation distribution using the same conditions, and show that normalizing, additionally, (any) one quantile of the value, e.g. upper bound of the support, is sufficient to identify the ambiguity parameter separately from the nonparametric utility.
    Keywords: first-price auction, identification, Bayesian econometrics, ambiguity aversion
    JEL: C11 C44 D44 E61
    Date: 2013–04
  11. By: Francisco Blasques (VU University Amsterdam)
    Abstract: This paper proposes a functional specification approach for dynamic stochastic general equilibrium (DSGE) models that explores the properties of the solution method used to approximate policy functions. In particular, the solution-driven specification takes the properties of the solution method directly into account when designing the structural model in order to deliver enhanced flexibility and facilitate parameter identification within the structure imposed by the underlying economic theory. A prototypical application reveals the importance of this method in improving the specification of functional nonlinearities that are consistent with economic theory. The solution-driven specification is also shown to have the potential to greatly improve model fit and provide alternative policy recommendations when compared to standard DSGE model designs.
    Keywords: Nonlinear Model Specification; DSGE; Perturbation Solutions
    JEL: C51 E17 E37
    Date: 2013–04–19
  12. By: Jon D. Samuels; Rodrigo Sekkel
    Abstract: A longstanding finding in the forecasting literature is that averaging forecasts from different models often improves upon forecasts based on a single model, with equal weight averaging working particularly well. This paper analyzes the effects of trimming the set of models prior to averaging. We compare different trimming schemes and propose a new one based on Model Confidence Sets that take into account the statistical significance of historical out-of-sample forecasting performance. In an empirical application of forecasting U.S. macroeconomic indicators, we find significant gains in out-of-sample forecast accuracy from our proposed trimming method.
    Keywords: Econometric and statistical methods
    JEL: C53
    Date: 2013
  13. By: Xu, Yongdeng
    Abstract: We propose a general form of vector Multiplicative Error Model (MEM) for the dynamics of duration, volume and price volatility. The vector MEM relaxes the two restrictions often imposed by previous empirical work in market microstructure research, by allowing interdependence among the variables and relaxing weak exogeneity restrictions. We further propose a multivariate lognormal distribution for the vector MEM. The model is applied to the trade and quote data from the New York Stock Exchange (NYSE). The empirical results show that the vector MEM captures the dynamics of the trivariate system successfully. We find that times of greater activity or trades with larger size coincide with a higher number of informed traders present in the market. But we highlight that it is unexpected component of trading duration or trading volume that carry the information content. Moreover, our empirical results also suggest a significant feedback effect from price process to trading intensity, while the persistent quote changes and transient quote changes affect trading intensity in different direction, confirming Hasbrouck (1988,1991).
    Keywords: Vector MEM; ACD; GARCH; intraday trading process; duration; volume; volatility
    JEL: C15 C32 C52
    Date: 2013–04

This nep-ecm issue is ©2013 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.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.