nep-ecm New Economics Papers
on Econometrics
Issue of 2012‒01‒18
fifteen papers chosen by
Sune Karlsson
Orebro University

  1. Estimation of Binary Choice Models with Linear Index and Dummy Endogenous Variables By Nese Yildiz
  2. One for all and all for one: regression checks with many regressors By Lavergne, Pascal; Patilea, Valentin
  3. Specification Sensitivity in Right-Tailed Unit Root Testing for Explosive Behavior By Peter C.B. Phillips; Shu-Ping Shi; Jun Yu
  4. Bayesian adaptive Lasso quantile regression By R. ALHAMZAWI; K. YU; D. F. BENOIT
  5. Data-driven estimation of diurnal duration patterns By Yuanhua Feng
  6. "Tests for Multivariate Analysis of Variance in High Dimension Under Non-Normality" By Muni S. Srivastava; Tatsuya Kubokawa
  7. The Power of Unit Root Tests Against Nonlinear Local Alternatives By Matei Demetrescu; Robinson Kruse
  8. VARs with Mixed Roots Near Unity By Peter C.B. Phillips; Ji Hyung Lee
  9. Identification via completeness for discrete covariates and orthogonal polynomials By Yevgeniy Kovchegov; Nese Yildiz
  10. Testing for Structural Instability in Moment Restriction Models: an Info?metric Approach By Alastair R. Hall; Yuyi Li; Chris D. Orme
  11. Testing for a rational bubble under long memory By M. FRÖMMEL; R. KRUSE
  12. Cointegration in Panel Data with Breaks and Cross-section Dependence By Anindya Banerjee; Josep Lluis Carrion-i-Silvestre
  13. A Donsker Theorem for Lévy Measures By Richard Nickl; Markus Reiß
  14. Futures pricing in electricity markets based on stable CARMA spot models By Fred Espen Benth; Claudia Kl\"uppelberg; Gernot M\"uller; Linda Vos
  15. Bayesian Learning of Impacts of Self-Exciting Jumps in Returns and Volatility By Andras Fulop; Junye Li; Jun Yu

  1. By: Nese Yildiz (University of Rochester, Department of Economics)
    Abstract: This paper presents computationally simple estimators for the index coefficients in a binary choice model with a binary endogenous regressor without relying on distributional assumptions or on large support conditions and yields root-n consistent and asymptotically normal estimators. We develop a multi-step method for estimating the parameters in a triangular, linear index, threshold-crossing model with two equations. Such an econometric model might be used in testing for moral hazard while allowing for asymmetric information in insurance markets. In outlining this new estimation method two contributions are made. The first one is proposing a novel ”matching” estimator for the coefficient on the binary endogenous variable in the outcome equation. Second, in order to establish the asymptotic properties of the proposed estimators for the coefficients of the exogenous regressors in the outcome equation, the results of Powell, Stock and Stoker (1989) are extended to cover the case where the average derivative estimation requires a first step semi-parametric procedure.
    Date: 2012–01
  2. By: Lavergne, Pascal; Patilea, Valentin
    Abstract: We develop a novel approach to build checks of parametric regression models when many regressors are present, based on a class of sufficiently rich semiparametric alternatives, namely single-index models. We propose an omnibus test based on the kernel method that performs against a sequence of directional nonparametric alternatives as if there was one regressor only, whatever the number of regressors. This test can be viewed as a smooth version of the integrated conditional moment (ICM) test of Bierens. Qualitative information can be easily incorporated into the procedure to enhance power. In an extensive comparative simulation study, we find that our test is little sensitive to the smoothing parameter and performs well in multidimensional settings. We then apply it to a cross-country growth regression model.
    Keywords: Dimensionality; Hypothesis testing; Nonparametric methods
    JEL: C14 C12
    Date: 2011
  3. By: Peter C.B. Phillips (Cowles Foundation, Yale University); Shu-Ping Shi (Australian National University); Jun Yu (Singapore Management University)
    Abstract: Right-tailed unit root tests have proved promising for detecting exuberance in economic and financial activities. Like left-tailed tests, the limit theory and test performance are sensitive to the null hypothesis and the model specification used in parameter estimation. This paper aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focussing on the sup ADF test of Phillips, Wu and Yu (2011), which implements a right-tailed ADF test repeatedly on a sequence of forward sample recursions. We analyze and compare the limit theory of the sup ADF test under different hypotheses and model specifications. The size and power properties of the test under various scenarios are examined in simulations and some recommendations for empirical practice are given. Empirical applications to the Nasdaq and to Australian and New Zealand housing data illustrate these specification issues and reveal their practical importance in testing.
    Keywords: Unit root test, Mildly explosive process, Recursive regression, Size and power
    JEL: C15 C22
    Date: 2012–01
    Abstract: Recently, variable selection by penalized likelihood has attracted much research interest. In this paper, we propose adaptive Lasso quantile regression (BALQR) from a Bayesian perspective. The method extends the Bayesian Lasso quantile regression by allowing different penalization parameters for different regression coefficients. Inverse gamma prior distributions are placed on the penalty parameters. We treat the hyperparameters of the inverse gamma prior as unknowns and estimate them along with the other parameters. A Gibbs sampler is developed to simulate the parameters from the posterior distributions. Through simulation studies and analysis of a prostate cancer data set, we compare the performance of the BALQR method proposed with six existing Bayesian and non-Bayesian methods. The simulation studies and the prostate cancer data analysis indicate that the BALQR method performs well in comparision to the other approaches.
    Keywords: Gibbs sampler, Lasso, Quantile regression, Skewed Laplace distribution.
    Date: 2011–07
  5. By: Yuanhua Feng (University of Paderborn)
    Abstract: This paper proposes a local linear estimator for diurnal patterns of transaction durations under a special nonparametric regression model, whose asymptotics are different to any known results. An iterative plug-in algorithm is developed for selecting the bandwidth. The ACD model is then applied to analyze the standardized durations. Data examples show that the proposals work well in practice.
    Keywords: Autoregressive conditional duration, diurnal duration patterns, local linear estimator, bandwidth selection, iterative plug-in.
    JEL: C14 C41
    Date: 2011–12
  6. By: Muni S. Srivastava (Department of Statistics, University of Toronto); Tatsuya Kubokawa (Faculty of Economics, University of Tokyo)
    Abstract: In this article, we consider the problem of testing the equality of mean vectors of dimension ρ of several groups with a common unknown non-singular covariance matrix Σ, based on <em>N</em> independent observation vectors where <em>N</em> may be less than the dimension ρ. This problem, known in the literature as the Multivariate Analysis of variance (MANOVA) in high-dimension has recently been considered in the statistical literature by Srivastava and Fujikoshi[7], Srivastava [5] and Schott[3]. All these tests are not invariant under the change of units of measurements. On the lines of Srivastava and Du[8] and Srivastava[6], we propose a test that has the above invariance property. The null and the non-null distributions are derived under the assumption that (<em>N</em>, ρ) → ∞ and <em>N</em> may be less than ρ and the observation vectors follow a general non-normal model.
    Date: 2011–12
  7. By: Matei Demetrescu (University of Bonn); Robinson Kruse (Leibniz University Hannover and CREATES)
    Abstract: This article extends the analysis of local power of unit root tests in a nonlinear direction by considering local nonlinear alternatives and tests built specically against stationary nonlinear models. In particular, we focus on the popular test proposed by Kapetanios et al. (2003, Journal of Econometrics 112, 359-379) in comparison to the linear Dickey-Fuller test. To this end, we consider different adjustment schemes for deterministic terms. We provide asymptotic results which imply that the error variance has a severe impact on the behavior of the tests in the nonlinear case; the reason for such behavior is the interplay of nonstationarity and nonlinearity. In particular, we show that nonlinearity of the data generating process can be asymptotically negligible when the error variance is moderate or large (compared to the "amount of nonlinearity"), rendering the linear test more powerful than the nonlinear one. Should however the error variance be small, the nonlinear test has better power against local alternatives. We illustrate this in an asymptotic framework of what we call persistent nonlinearity. The theoretical findings of this article explain previous results in the literature obtained by simulation. Furthermore, our own simulation results suggest that the user-specied adjustment scheme for deterministic components (e.g. OLS, GLS, or recursive adjustment) has a much higher impact on the power of unit root tests than accounting for nonlinearity, at least under local (linear or nonlinear) alternatives.
    Keywords: Nonlinear models, Stochastic trend, Near integration, Persistent nonlinearity, Local power
    JEL: C12 C22
    Date: 2012–01–04
  8. By: Peter C.B. Phillips (Cowles Foundation, Yale University); Ji Hyung Lee (Dept. of Economics, Yale University)
    Abstract: Limit theory is developed for nonstationary vector autoregression (VAR) with mixed roots in the vicinity of unity involving persistent and explosive components. Statistical tests for common roots are examined and model selection approaches for discriminating roots are explored. The results are useful in empirical testing for multiple manifestations of nonstationarity -- in particular for distinguishing mildly explosive roots from roots that are local to unity and for testing commonality in persistence.
    Keywords: Common roots, Local to unity, Mildly explosive, Mixed roots, Model selection, Persistence, Tests of common roots
    JEL: C22
    Date: 2012–01
  9. By: Yevgeniy Kovchegov (University of Rochester, Department of Mathematics); Nese Yildiz (Oregon State University, Department of Economics)
    Abstract: We solve a class of identification problems for nonparametric and semiparametric models when the endogenous covariate is discrete with unbounded support. Then we proceed with an approach that resolves a polynomial basis problem for the above class of discrete distributions, and for the distributions given in the sufficient condition for completeness in Newey and Powell (2003). Thus, in addition to extending the set of econometric models for which nonparametric or semiparametric identification of structural functions is guaranteed to hold, our approach provides a natural way of estimating these functions. Finally, we extend our polynomial basis approach to Pearson-like and Ord-like families of distributions.
    Keywords: nonparametric methods, identification, instrumental variables.
    Date: 2012–01
  10. By: Alastair R. Hall; Yuyi Li; Chris D. Orme
    Date: 2012
  11. By: M. FRÖMMEL; R. KRUSE
    Abstract: We analyze the time series properties of the S&P500 dividend-price ratio in the light of long memory, structural breaks and rational bubbles. We find an increase in the long memory parameter in the early 1990s by applying a recently proposed test by Sibbertsen and Kruse (2009). An application of the unit root test against long memory by Demetrescu et al. (2008) suggests that the pre-break data can be characterized by long memory, while the post-break sample contains a unit root. These results reconcile two empirical findings which were seen as contradictory so far: on the one hand they confirm the existence of fractional integration in the S&P500 log dividend-price ratio and on the other hand they are consistent with the existence of a rational bubble. The result of a changing memory parameter in the dividend-price ratio has an important implication for the literature on return predictability: the shift from a stationary dividend-price ratio to a unit root process in 1991 is likely to have caused the well-documented failure of conventional return prediction models since the 1990s.
    Keywords: Rational bubbles, dividend-price ratio, fractional integration, changing persistence.
    JEL: C12 C22 G12
    Date: 2011–05
  12. By: Anindya Banerjee; Josep Lluis Carrion-i-Silvestre
    Abstract: Panel cointegration, structural break, common factors, cross-section dependence
    Keywords: Panel cointegration, structural break, common factors, cross-section dependence
    JEL: C12 C22
    Date: 2011–12
  13. By: Richard Nickl; Markus Reiß
    Abstract: Given n equidistant realisations of a Lévy process (Lt; t >= 0), a natural estimator for the distribution function N of the Lévy measure is constructed. Under a polynomial decay restriction on the characteristic function, a Donsker-type theorem is proved, that is, a functional central limit theorem for the process in the space of bounded functions away from zero. The limit distribution is a generalised Brownian bridge process with bounded and continuous sample paths whose covariance structure depends on the Fourier-integral operator. The class of Lévy processes covered includes several relevant examples such as compound Poisson, Gamma and self-decomposable processes. Main ideas in the proof include establishing pseudo-locality of the Fourier-integral operator and recent techniques from smoothed empirical processes.
    Keywords: uniform central limit theorem, nonlinear inverse problem, smoothed empirical processes, pseudo-differential operators, jump measure
    JEL: C14 C22
    Date: 2012–01
  14. By: Fred Espen Benth; Claudia Kl\"uppelberg; Gernot M\"uller; Linda Vos
    Abstract: We present a new model for the electricity spot price dynamics, which is able to capture seasonality, low-frequency dynamics and the extreme spikes in the market. Instead of the usual purely deterministic trend we introduce a non-stationary independent increments process for the low-frequency dynamics, and model the large fluctuations by a non-Gaussian stable CARMA process. The model allows for analytic futures prices, and we apply these to model and estimate the whole market consistently. Besides standard parameter estimation, an estimation procedure is suggested, where we fit the non-stationary trend using futures data with long time until delivery, and a robust $L^1$-filter to find the states of the CARMA process. The procedure also involves the empirical and theoretical risk premiums which -- as a by-product -- are also estimated. We apply this procedure to data from the German electricity exchange EEX, where we split the empirical analysis into base load and peak load prices. We find an overall negative risk premium for the base load futures contracts, except for contracts close to delivery, where a small positive risk premium is detected. The peak load contracts, on the other hand, show a clear positive risk premium, when they are close to delivery, while the contracts in the longer end also have a negative premium.
    Date: 2012–01
  15. By: Andras Fulop (Finance Department, ESSEC Business School, Paris-Singapore, Cergy-Pontoise Cedex, France 95021); Junye Li (Finance Department, ESSEC Business School, Paris-Singapore, 100 Victoria Street, Singapore 188064); Jun Yu (Sim Kee Boon Institute for Financial Economics, School of Economics, and Lee Kong Chian School of Business, Singapore Management University, 90 Stamford Road, Singapore 178903)
    Abstract: The paper proposes a new class of continuous-time asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating self-exciting co-jumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and in-sample over-fitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and non-nested models, but also generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows that volatility jumps at the same time as negative jumps in asset returns mainly through jumps in diffusion volatility. We find substantial evidence for jump clustering, in particular, after the recent financial crisis in 2008, even though parameters driving dynamics of the jump intensity remain difficult to identify.
    Keywords: Self-Excitation, Volatility Jump, Jump Clustering, Extreme Events, Parameter Learning, Particle Filters, Sequential Bayes Factor, Risk Management
    JEL: C11 C13 C32 G12
    Date: 2012–01

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