Econometrics
http://lists.repec.org/mailman/listinfo/nep-ecm
Econometrics2014-10-22Sune KarlssonTesting for unit roots in panels with structural changes, spatial and temporal dependence when the time dimension is finite.
http://d.repec.org/n?u=RePEc:not:notgts:14/03&r=ecm
Finite T panel data unit root tests allowing for structural breaks, spatial cross section dependence, heteroscedasticity, serial correlation, heterogeneity and non-linear trends are proposed. The structural breaks can be at known or unknown dates. For the latter, analytic probability density functions of the asymptotic distributions of the tests are provided based on a minimum order statistic. The tests can accommodate general forms of spatial dependence for which the spatial weights matrix does not have to be de?ned due to the utilization of a non-parametric estimator. A set of sufficient conditions which determines admissible deterministic trend functions is also provided. Finally, extensive Monte Carlo experiments show the usefulness of the new tests.Yiannis Karavias, Elias TzavalisPanel data; Unit roots; Structural breaks; Spatial dependence; Serial correlation; Fixed T JEL classification: C22, C23Specification Testing in Nonstationary Time Series Models
http://d.repec.org/n?u=RePEc:yor:yorken:14/19&r=ecm
In this paper, we consider a specification testing problem in nonlinear time series models with nonstationary regressors and propose using a nonparametric kernel-based test statistic. The nullasymptotics for the proposed nonparametric test statistic have been well developed in the existing literature such as Gao et al (2009b) and Wang and Phillips (2012). In this paper, we study the local asymptotics of the test statistic, i.e., the asymptotic properties of the test statistic under a sequence of general nonparametric local alternatives, and show that the asymptotic distribution depends on the asymptotic behaviour of the distance function which is the local deviation from the parametrically specified model in the null hypothesis. In order to implement the proposed test in practice, we introduce a bootstrap procedure to approximate the critical values of the test statistic and establish a novel result of Edgeworth expansion which is used to justify the use of such an approximation. Based on the approximate critical values, we develop a bandwidth selection method, which chooses the optimal bandwidth that maximises the local power of the test while its size is controlled at a given significance level. The local power is defined as the power of the proposed test for a given sequence of local alternatives. Such a bandwidth selection is made feasible by an approximate expression for the local power of the test as a function of the bandwidth. A Monte-Carlo simulation study is provided to illustrate the finite sample performance of the proposed test.Jia Chen, Jiti Gao, Degui Li, Zhengyan Lin2014-09Asymptotic distribution; Edgeworth expansion; local power function; nonlinear time series; quadratic form; size function; specification testing; unit root.A Class of Indirect Inference Estimators: Higher Order Asymptotics and Approximate Bias Correction (Revised)
http://d.repec.org/n?u=RePEc:aue:wpaper:1411&r=ecm
In this paper we define a set of Indirect Inference estimators based on moment approximations of the auxiliary ones. Their introduction is motivated by reasons of analytical and computational facilitation. Their definition provides an indirect inference framework for some "classical" bias correction procedures. We derive higher order asymptotic properties of these estimators. We demonstrate that under our assumption framework and in the special case of deterministic weighting and affinity of the binding function these are second order unbiased. Moreover their second order approximate Mean Square Errors do not depend on the cardinality of the Monte Carlo or Bootstrap samples that our definition may involve. Consequently, the second order Mean Square Error of the auxiliary estimator is not altered. We extend this to a class of multistep Indirect Inference estimators that have zero higher order bias without increasing the approximate Mean Squared Error, up to the same order. Our theoretical results are also validated by three Monte Carlo experiments.Stelios Arvanitis, Antonis Demos2014-09-23Recursive Indirect Estimator, Binding Function, Edgeworth Expansion, Moment Approximation, Higher Order Bias Approximation, Higher Order Mean Square Error Approximation, Approximate Bias Correction, Monte Carlo, Bootstrap, GARCH model, Stationary GaussiaAsymptotic Distribution and Finite-Sample Bias Correction of QML Estimators for Spatial Error Dependence Model
http://d.repec.org/n?u=RePEc:siu:wpaper:15-2014&r=ecm
In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the application of this model. Contrary to the common perceptions, both the large and small sample behaviors of the QML estimators for the SED model can be different from those for the SLD model in terms of the rate of convergence and the magnitude of bias. Monte Carlo results show that the bias can be severe and the proposed bias correction procedure is very effective.Shew Fan Liu, Zhenlin Yang2014-09Asymptotics; Bias Correction; Bootstrap; Concentrated estimating equation; Monte Carlo; Spatial layout; Stochastic expansionEstimation of Spatial Models with Endogenous Weighting Matrices and an Application to a Demand Model for Cigarettes
http://d.repec.org/n?u=RePEc:rri:wpaper:2013wp02&r=ecm
Weighting matrices are typically assumed to be exogenous. However, in many cases this exogeneity assumption may not be reasonable. In these cases, typical model specifications and corresponding estimation procedures will no longer be valid. In this paper we specify a spatial panel data model which contains a spatially lagged dependent variable in terms of an endogenous weighting matrix. We suggest an estimator for the regression parameters, and demonstrate its consistency and asymptotic normality. We also suggest an estimator for the large sample variance-covariance matrix of that distribution. We then apply our results to an interstate panel data cigarette demand model which contains an endogenous weighting matrix. Among other things, our results suggest that, if properly accounted for, the bootlegging effect of buyers, or “agents” for them, crossing state borders to purchase cigarette turns out to be positive and significant.Harry H. Kelejian, Gianfranco Piras2013-02weighting matrices, econometrics, estimation,Structural Analysis of Nonlinear Pricing
http://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-518&r=ecm
This paper proposes a methodology for analyzing nonlinear pricing data with an illustration on cellular phone. The model incorporates consumer exclusion. Assuming a known tariff, we establish identification of the model primitives using the first-order conditions of both the firm and the consumer up to a cost parameterization. Next, we propose a new one-step quantile-based nonparametric method to estimate the consumers’ inverse demand and their type distribution. We show that our nonparametric estimator is root-N-consistent. We then introduce unobserved product heterogeneity with an unknown tariff. We show how our identification and estimation results extend. Our analysis of cellular phone consumption data assesses the performance of alternative pricing strategies relative to nonlinear pricing.Yao Luo, Isabelle Perrigne, Quang Vuong2014-10-01Nonlinear Pricing, Nonparametric Identification, Empirical Processes, Quantile, Transformation Model, Unobserved Heterogeneity, TelecommunicationUnbalanced Fractional Cointegration and the No-Arbitrage Condition on Commodity Markets
http://d.repec.org/n?u=RePEc:aim:wpaimx:1445&r=ecm
A necessary condition for two time series to be nontrivially cointegrated is the equality of their respective integration orders. Nonetheless, in some cases, the apparent unbalance of integration orders of the observables can be misleading and the cointegration theory applies all the same. This situation refers to unbalanced cointegration in the sense that balanced long run relationship can be recovered by an appropriate filtering of one of the time series. In this paper, we suggest a local Whittle estimator of bivariate unbalanced fractional cointegration systems. Focusing on a degenerating band around the origin, it estimates jointly the unbalance parameter, the long run coefficient and the integration orders of the regressor and the cointegrating errors. Its consistency is demonstrated for the stationary regions of the parameter space and a finite sample analysis is conducted by means of Monte Carlo experiments. An application to the no-arbitrage condition between crude oil spot and futures prices is proposed to illustrate the empirical relevance of the developed estimator. Non-technical abstract:The no-arbitrage condition between spot and future prices implies an analogous condition on their underlying volatilities. Interestingly, the long memory behavior of the volatility series also involves a long-run relationship that allows to test for the no-arbitrage condition by means of cointegration techniques. Unfortunately, the persistent nature of the volatility can vary with the future maturity, thereby leading to unbalanced integration orders between spot and future volatility series. Nonetheless, if a balanced long-run relationship can be recovered by an appropriate filtering of one of the time series, the cointegration theory applies all the same and unbalanced cointegration operates between the raw series. In this paper, we introduce a new estimator of unbalanced fractional cointegration systems that allows to test for the no-arbitrage condition between the crude oil spot and futures volatilities.Gilles de Truchis, Florent Duboisunbalanced cointegration, Fractional cointegration, no-arbitrage condition, local Whittle likelihood, commodity marketsComparing Implementations of Estimation Methods for Spatial Econometrics
http://d.repec.org/n?u=RePEc:rri:wpaper:2013wp01&r=ecm
Recent advances in the implementation of spatial econometrics model estimation techniques have made it desirable to compare results, which should correspond between implementations across software applications for the same data. These model estimation techniques are associated with methods for estimating impacts (emanating effects), which are also presented and compared. This review constitutes an up to date comparison of generalized method of moments (GMM) and maximum likelihood (ML) implementations now available. The comparison uses the cross sectional US county data set provided by Drukker, Prucha, and Raciborski (2011c, pp. 6-7). The comparisons will be cast in the context of alternatives using the MATLAB Spatial Econometrics toolbox, Stata, Python with PySAL (GMM) and R packages including sped, sphet and McSpatial.Roger Bivand, Gianfranco Piras2013-01spatial econometrics, maximum likelihood, generalized method of moments, estimation, R, Stata, Python, MATLABFlexible Modelling in Statistics: Past, present and Future
http://d.repec.org/n?u=RePEc:eca:wpaper:2013/175715&r=ecm
Christophe Ley2014-09heavy and light tails; skewness and kurtosis; skew-normal distributions; symmetry and normality tests; transformation approach; two-piece distributionsThe Fourier estimation method with positive semi-definite estimators
http://d.repec.org/n?u=RePEc:arx:papers:1410.0112&r=ecm
In this paper we present a slight modification of the Fourier estimation method of the spot volatility (matrix) process of a continuous It\^o semimartingale where the estimators are always non-negative definite. Since the estimators are factorized, computational cost will be saved a lot.Jir\^o Akahori, Nien-Lin Liu, Maria Elvira Mancino, Yukie Yasuda2014-10IMPROVED VOLATILITY ESTIMATION BASED ON LIMIT ORDER BOOKS
http://d.repec.org/n?u=RePEc:hum:wpaper:sfb649dp2014-053&r=ecm
For a semi-martingale Xt, which forms a stochastic boundary, a rate-optimal estimator for its quadratic variation hX;Xit is con- structed based on observations in the vicinity of Xt. The problem is embedded in a Poisson point process framework, which reveals an interesting connection to the theory of Brownian excursion ar- eas. A major application is the estimation of the integrated squared volatility of an ecient price process Xt from intra-day order book quotes. We derive n1=3 as optimal convergence rate of integrated squared volatility estimation in a high-frequency framework with n observations (in mean). This considerably improves upon the classi- cal n1=4-rate obtained from transaction prices under microstructure noise.Markus Bibinger, Moritz Jirak, Markus Reiss, , 2014-09Brownian excursion area, limit order book, integrated volatility, Feynman{Kac, high-frequency data, Poisson point processDiscrete choice estimation of risk aversion
http://d.repec.org/n?u=RePEc:upf:upfgen:1443&r=ecm
We analyze the use of discrete choice models for the estimation of risk aversion and show a fundamental flaw in the standard random utility model which is commonly used in the literature. Specifically, we find that given two gambles, the probability of selecting the riskier gamble may be larger for larger levels of risk aversion. We characterize when this occurs. By contrast, we show that the alternative random preference approach is free of such problems.Jose Apesteguia, Miguel A. Ballester2014-09Discrete Choice; Structural Estimation; Risk Aversion; Random Utility Models; Random Preference Models.Mean of Ratios or Ratio of Means: statistical uncertainty applied to estimate Multiperiod Probability of Defaul
http://d.repec.org/n?u=RePEc:arx:papers:1409.4896&r=ecm
The estimate of a Multiperiod probability of default applied to residential mortgages can be obtained using the mean of the observed default, so called the Mean of ratios estimator, or aggregating the default and the issued mortgages and computing the ratio of their sum, that is the Ratio of means. This work studies the statistical properties of the two estimators with the result that the Ratio of means has a lower statistical uncertainty. The application on a private residential mortgage portfolio leads to a lower probability of default on the overall portfolio by eleven basis points.Matteo Formenti2014-09