nep-ecm New Economics Papers
on Econometrics
Issue of 2014‒06‒22
twenty-two papers chosen by
Sune Karlsson
Orebro University

  1. Quasi-Maximum Likelihood Estimation of Heteroskedastic Fractional Time Series Models By Giuseppe Cavaliere; Morten Ørregaard Nielsen; A. M. Robert Taylor
  2. Sign-based specification tests for martingale difference with conditional heteroscedasity By Chen, Min; Zhu, Ke
  3. Small Sample Properties of Bayesian Estimators of Labor Income Processes By Nakata, Taisuke; Tonetti, Christopher
  4. A One Line Derivation of EGARCH By Michael McAleer; Christian M. Hafner
  5. On consistency issues in Bayesian nonparametric testing - a review By Rousseau, Judith
  6. The finite sample performance of estimators for mediation analysis under sequential conditional independence By Huber, Martin; Mellace, Giovanni; Lechner, Michael
  7. On Convergence Rates of Empirical Bayes Procedures By Scricciolo, Catia; Rousseau, Judith; Rivoirard, Vincent; Donnet, Sophie
  8. Structural VARs, Deterministic and Stochastic Trends: Does Detrending Matter? By Varang Wiriyawit; Benjamin Wong
  9. On Trend, Breaks and Initial Condition in Unit Root Testing By Anton Skrobotov
  10. A Quadratic Kalman Filter By Monfort, A.; Renne, J.-P.; Roussellet, G.
  11. On diversity under a Bayesian nonparametric dependent model By Rousseau, Judith; Mengersen, Kerrie; Arbel, Julyan
  12. Jeffreys Priors for Mixture Models By Robert, Christian P.; Grazian, Clara
  13. Identifying inliers By Michael Greenacre; H. Öztaç Ayhan
  14. A procedure for combining zero and sign restrictions in a VAR-identification scheme By Alex Haberis; Andrej Sokol
  15. Structural Vector Autoregressions with Smooth Transition in Variances - The Interaction Between U.S. Monetary Policy and the Stock Market By Helmut Lütkepohl; Aleksei Netsunajev; ;
  16. Estimating capabilities with structural equation models: How well are we doing in a 'real' world? By Jaya Krishnakumar; Florian Wendelspiess Chávez Juárez
  17. Testing Spatial Causality in Cross-section Data By Herrera Gómez, Marcos; Ruiz Marín, Manuel; Mur Lacambra, Jesús
  18. Factor Models for Alpha Streams By Zura Kakushadze
  19. Calibration of a stock's beta using options prices By Sofiene El Aoud; Frédéric Abergel
  20. Trend Mis-specifications and Estimated Policy Implications in DSGE Models By Varang Wiriyawit
  21. A One-Factor Conditionally Linear Commodity Pricing Model under Partial Information By Takashi Kato; Jun Sekine; Hiromitsu Yamamoto
  22. Markov Switching GARCH models for Bayesian Hedging on Energy Futures Markets By Roberto Casarin; Monica Billio; Anthony Osuntuyi

  1. By: Giuseppe Cavaliere (University of Bologna); Morten Ørregaard Nielsen (Queen's University and CREATES); A. M. Robert Taylor (University of Essex)
    Abstract: In a recent paper Hualde and Robinson (2011) establish consistency and asymptotic normality for conditional sum-of-squares estimators, which are equivalent to conditional quasi-maximum likelihood estimators, in parametric fractional time series models driven by conditionally homoskedastic shocks. In contrast to earlier results in the literature, their results apply over an arbitrarily large set of admissible parameter values for the (unknown) memory parameter covering both stationary and non-stationary processes and invertible and non-invertible processes. In this paper we extend their results to the case where the shocks can display conditional and unconditional heteroskedasticity of a quite general and unknown form. We establish that the consistency result presented in Hualde and Robinson (2011) continues to hold under heteroskedasticity as does asymptotic normality. However, we demonstrate that the covariance matrix of the limiting distribution of the estimator now depends on nuisance parameters which derive both from the weak dependence in the process (as is also the case for the corresponding result in Hualde and Robinson, 2011) but additionally from the heteroskedasticity present in the shocks. Asymptotically pivotal inference can be performed on the parameters of the heteroskedastic model, provided a conventional "sandwich" estimator of the variance is employed.
    Keywords: (un)conditional heteroskedasticity, conditional sum-of-squares, fractional integration, quasi-maximum likelihood estimation
    JEL: C13 C22
    Date: 2014–06
  2. By: Chen, Min; Zhu, Ke
    Abstract: This article proposes Cramer-von Mises (CM) and Kolmogrove-Smirnov (KS) test statistics based on the signs of a time series to test the null hypothesis that the series is a martingale difference sequence (MDS) with conditional heteroscedasity. Both of test statistics allowing for heavy-tailedness, non-stationarity, and nonlinear serial dependence of unknown forms, are easy-to-implement. Unlike the sign-based variance-ratio test in Wright (2000), our sign-based CM and KS tests have no need to select the lag. Unlike other often used specification tests for MDS, our sign-based CM and KS tests are robust and have exact distributions which can be simulated easily. Simulation studies and applications further demonstrate the importance of our sign-based CM and KS tests.
    Keywords: Conditional heteroscedasity; Cramer-von Mises test; Kolmogrove-Smirnov test; Martingale difference; Robustness.
    JEL: C1 C12
    Date: 2014–06–01
  3. By: Nakata, Taisuke (Board of Governors of the Federal Reserve System (U.S.)); Tonetti, Christopher (Stanford GSB)
    Abstract: There exists an extensive literature estimating idiosyncratic labor income processes. While a wide variety of models are estimated, GMM estimators are almost always used. We examine the validity of using likelihood based estimation in this context by comparing the small sample properties of a Bayesian estimator to those of GMM. Our baseline studies estimators of a commonly used simple earnings process. We extend our analysis to more complex environments, allowing for real world phenomena such as time varying and heterogeneous parameters, missing data, unbalanced panels, and non-normal errors. The Bayesian estimators are demonstrated to have favorable bias and efficiency properties.
    Keywords: Labor income process; small sample properties; GMM; bayesian estimation; error component models
    Date: 2014–03–31
  4. By: Michael McAleer (University of Canterbury); Christian M. Hafner
    Abstract: One of the most popular univariate asymmetric conditional volatility models is the exponential GARCH (or EGARCH) specification. In addition to asymmetry, which captures the different effects on conditional volatility of positive and negative effects of equal magnitude, EGARCH can also accommodate leverage, which is the negative correlation between returns shocks and subsequent shocks to volatility. However, there are as yet no statistical properties available for the (quasi-) maximum likelihood estimator of the EGARCH parameters. It is often argued heuristically that the reason for the lack of statistical properties arises from the presence in the model of an absolute value of a function of the parameters, which does not permit analytical derivatives or the derivation of statistical properties. It is shown in this paper that: (i) the EGARCH model can be derived from a random coefficient complex nonlinear moving average (RCCNMA) process; and (ii) the reason for the lack of statistical properties of the estimators of EGARCH is that the stationarity and invertibility conditions for the RCCNMA process are not known.
    Keywords: Leverage, asymmetry, existence, random coefficient models, complex nonlinear moving average process
    JEL: C22 C52 C58 G32
    Date: 2014–06–16
  5. By: Rousseau, Judith
    Abstract: Although there have been a lot of developpements in the recent years on estimation in Bayesian nonparametric models, from a theoretical point view as well as from a methodological point of view, little has been done on Bayesian testing in nonparametric frameworks. In this talk I will be interested on asymptotic properties of Bayesian tests when at least one of the hypotheses is nonparametric. I will first give some results on goodness of fit types of tests where one is interested in testing a parametric model against a nonparametric alternative embedding the parametric model. Then I will discuss the more delicate problem where both hypotheses are nonparametric. Such cases involve in particular tests for monotonicity, two-sample tests and estimation of the number of components in nonparametric mixture models. It will be shown that the Bayes factor or equivalently the 0-1 loss function might not be appropriate in such cases and that modifications need to be considered.
    Keywords: consistency; Bayes factors; tests; Bayesian nonparametrics;
    JEL: C11
    Date: 2014–06
  6. By: Huber, Martin; Mellace, Giovanni; Lechner, Michael
    Abstract: Using a comprehensive simulation study based on empirical data, this paper investigates the finite sample properties of different classes of parametric and semi-parametric estimators of (natural or pure) direct and indirect causal effects used in mediation analysis under sequential conditional independence assumptions. The estimators are based on regression, inverse probability weighting, and combinations thereof. Our simulation design uses a large population of Swiss jobseekers and considers variations of several features of the data generating process and the implementation of the estimators that are of practical relevance. We find that no estimator performs uniformly best (in terms of root mean squared error) in all simulations. Overall, so-called ‘g-computation’ dominates. However, differences between estimators are often (but not always) minor in the various setups and the relative performance of the methods often (but not always) varies with the features of the data generating process.
    Keywords: Causal mechanisms, direct effects, indirect effects, simulation, empirical Monte Carlo Study, causal channels, mediation analysis, causal pathways
    JEL: C21
    Date: 2014–06
  7. By: Scricciolo, Catia; Rousseau, Judith; Rivoirard, Vincent; Donnet, Sophie
    Abstract: Empirical Bayes procedures are commonly used based on the supposed asymptotic equivalence with fully Bayesian procedures, which, however, has not so far received full theoretical support in terms of uncertainty quantification. In this note, we provide some results on contraction rates of empirical Bayes posterior distributions which are illustrated in nonparametric curve estimation using Dirichlet process mixture models.
    Keywords: empirical Bayes selection of prior hyperparameters; nonparametric curve estimation; Dirichlet process mixtures;
    JEL: C11
    Date: 2014–06
  8. By: Varang Wiriyawit; Benjamin Wong
    Abstract: We highlight how detrending within Structural Vector Autoregressions (SVAR) is directly linked to the shock identification. Consequences of trend misspecification are investigated using a prototypical Real Business Cycle model as the Data Generating Process. Decomposing the different sources of biases in the estimated impulse response functions, we find the biases arising directly from trend misspecification are not trivial when compared to other widely studied misspecifications. Our example also illustrates how misspecifying the trend can also distort impulse response functions of even the correctly detrended variable within the SVAR system.
    Keywords: Structural VAR, Identification, Detrending, Bias
    JEL: C15 C32 C51 E37
    Date: 2014–06
  9. By: Anton Skrobotov (Gaidar Institute for Economic Policy)
    Abstract: Recent approaches in unit root testing that take into account the influences of the initial condition, trend, and breaks in the data using pre-testing and performing the union of rejection testing strategies based on the information obtained. This allows for the use of more powerful tests, if there is uncertainty about some of the parameters in the model. This paper proposes the extension of the Harvey et al. (2012b) approach to the case of uncertainty over the initial condition. It has been shown that the procedures of Harvey et al. (2012b) have low power under a large initial condition because they include GLS-based tests. Therefore, the efficiency of some ADF-type unit root tests with breaks under various magnitudes of initial condition will be investigated, and the decision rule based on pre-testing for a magnitude of the initial condition and simultaneous use of tests based on both GLS and OLS detrending is proposed. Additionally, the modification of the proposed algorithm using pre-testing for the trend coefficient will be analyzed. Analysis of a situation with the possible presence of multiple structural breaks in trend will also be conducted in the paper. Two algorithms are proposed: the first involves only pre-testing the initial condition, while the second involves pre-testing the number of breaks based on the Kejriwal and Perron (2010) test. The asymptotic behavior of all tests is analyzed under both a local-to-unity representation of the autoregressive root and a local-to- zero representation of trend and breaks magnitudes. The proposed modifications save the high power for small initial conditions/trend/breaks and at the same time lead to the power close to one of the effective tests for large initial condition/trend/breaks.
    Keywords: unit root test, infimum Dickey-Fuller tests, local trend, local trend break, asymptotic local power, union of rejection, pre-testing, multiple breaks in trend.
    JEL: C12 C22
    Date: 2014
  10. By: Monfort, A.; Renne, J.-P.; Roussellet, G.
    Abstract: We propose a new filtering and smoothing technique for non-linear state-space models. Observed variables are quadratic functions of latent factors following a Gaussian VAR. Stacking the vector of factors with its vectorized outer-product, we form an augmented state vector whose first two conditional moments are known in closed-form. We also provide analytical formulae for the unconditional moments of this augmented vector. Our new quadratic Kalman filter (Qkf) exploits these properties to formulate fast and simple filtering and smoothing algorithms. A first simulation study emphasizes that the Qkf outperforms the extended and unscented approaches in the filtering exercise showing up to 70% RMSEs improvement of filtered values. Second, we provide evidence that Qkf-based maximum-likelihood estimates of model parameters always possess lower bias or lower RMSEs that the alternative estimators.
    Keywords: non-linear filtering, non-linear smoothing, quadratic model, Kalman filter, pseudo-maximum likelihood.
    JEL: C32 C46 C53
    Date: 2014
  11. By: Rousseau, Judith; Mengersen, Kerrie; Arbel, Julyan
    Abstract: We present a dependent Bayesian nonparametric model for the proba- bilistic modelling of species-by-site data, i.e. population data where observations at different sites are classified into distinct species. We use a dependent version of the Griffiths-Engen-McCloskey distribution, the distribution of the weights of the Dirichlet process, in the same lines as the Dependent Dirichlet process is defined. The prior is thus defined via the stick-breaking construction. Some distributional properties of this model are presented.
    Keywords: Stick-breaking representation; Griffiths-Engen- McCloskey distribution; Covariate-dependent mode; Bayesian nonparametrics;
    JEL: C11
    Date: 2014–06
  12. By: Robert, Christian P.; Grazian, Clara
    Abstract: Mixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper. This feature leads to problems in carry out an objective Bayesian approach. In this work an analysis of Jeffreys priors in the setting of finite mixture models will be presented.
    Keywords: Objective Bayes; Mixture models; Jeffreys prior;
    JEL: C11
    Date: 2014–06
  13. By: Michael Greenacre; H. Öztaç Ayhan
    Abstract: The problem of outliers is well-known in statistics: an outlier is a value that is far from the general distribution of the other observed values, and can often perturb the results of a statistical analysis. Various procedures exist for identifying outliers, in case they need to receive special treatment, which in some cases can be exclusion from consideration. An inlier, by contrast, is an observation lying within the general distribution of other observed values, generally does not perturb the results but is nevertheless non-conforming and unusual. For single variables, an inlier is practically impossible to identify, but in the multivariate case, thanks to interrelationships between variables, values can be identified that are observed to be more central in a distribution but would be expected, based on the other information in the data matrix, to be more outlying. We propose an approach to identify inliers in a data matrix, based on the singular value decomposition. An application is presented using a table of economic indicators for the 27 member countries of the European Union in 2011, where inlying values are identified for some countries such as Estonia and Luxembourg.
    Keywords: imputation, inlier, outlier, singular value decomposition
    JEL: C19 C88
    Date: 2014–06
  14. By: Alex Haberis (Bank of England; Centre for Macroeconomics (CFM)); Andrej Sokol (Bank of England)
    Abstract: In this paper we describe a procedure for implementing zero restrictions within the context of a sign restrictions identification scheme for VARs. The procedure introduces an additional step into the algorithm outlined in Fry and Pagan (2011) and Rubio-Ramirez et al. (2006) for implementing sign restrictions. This extra step involves rotating a candidate identification matrix using Givens rotation matrices to introduce zero restrictions. We then check whether the elements of the candidate matrix satisfy the sign restrictions as usual. We illustrate how our procedure works by generating artificial data from the theoretical model of An and Schorfheide (2007), which implies certain restrictions on the impact of its structural shocks on the model's endogenous variables. We exploit our knowledge of that pattern to identify structural shocks from the reduced-form errors of a VAR estimated on the simulated data.
    JEL: C32 C51 E12
    Date: 2014–06
  15. By: Helmut Lütkepohl; Aleksei Netsunajev; ;
    Abstract: In structural vector autoregressive analysis identifying the shocks of interest via heteroskedasticity has become a standard tool. Unfortunately, the approaches currently used for modelling heteroskedasticity all have drawbacks. For instance, assuming known dates for variance changes is often unrealistic while more exible models based on GARCH or Markov switching residuals are dicult to handle from a statistical and computational point of view. Therefore we propose a model based on a smooth change in variance that is exible as well as relatively easy to estimate. The model is applied to a five-dimensional system of U.S. variables to explore the interaction between monetary policy and the stock market. It is found that previously used conventional identification schemes in this context are rejected by the data if heteroskedasticity is allowed for. Shocks identified via heteroskedasticity have a different economic interpretation than the shocks identified using conventional methods.
    Keywords: Structural vector autoregressions, heteroskedasticity, smooth transition VAR models, identification via heteroskedasticity
    JEL: C32
    Date: 2014–06
  16. By: Jaya Krishnakumar; Florian Wendelspiess Chávez Juárez
    Abstract: Measuring capabilities is a major challenge for the operationalization of the capability approach. Structural equation models (SEM) are being increasingly used as one possible methodology for estimating capabilities, but a certain skepticism remains about their appropriateness. In this paper, we perform a unique simulation experiment for testing the validity of such estimators. Using an agent-based modeling tool, we simulate a 'real' life scenario with individuals of heterogeneous characteristics and behaviors, having different capability sets, and making different decisions. We then run a SEM (MIMIC) model on the data generated in this simulated world to estimate the individual capabilities. Our results support the idea that SEM can coherently estimate the true capabilities. We find that using the linear predictor from the structural part of the SEM provides better results than using the 'classical' factor scores based on the full model.
    Keywords: latent variable model, MIMIC, SEM, simulation, capability approach
    JEL: C10 C15 D63 I00 I20
    Date: 2014–06
  17. By: Herrera Gómez, Marcos; Ruiz Marín, Manuel; Mur Lacambra, Jesús
    Abstract: The paper shows a new non-parametric test, based on symbolic entropy, which permits detect spatial causality in cross-section data. The test is robust to the functional form of the relation and has a good behaviour in samples of medium to large size. We illustrate the use of test with the case of relationship between migration and unemployment, using data on 3,108 U.S. counties for the period 2003-2008.
    Keywords: Spatial Econometrics, Causality, Non-parametric method
    JEL: C01 C21 C46
    Date: 2014
  18. By: Zura Kakushadze
    Abstract: We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor model alpha covariance matrix. We discuss various risk factors for alphas such as: style risk factors; cluster risk factors based on alpha taxonomy; principal components; and also using the underlying tradables (stocks) as alpha risk factors, for which computing the factor loadings and factor covariance matrices does not involve any correlations with alphas, and their number is much larger than that of the relevant principal components. We draw insight from stock factor models, but also point out substantial differences.
    Date: 2014–06
  19. By: Sofiene El Aoud (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris); Frédéric Abergel (FiQuant - Chaire de finance quantitative - Ecole Centrale Paris, MAS - Mathématiques Appliquées aux Systèmes - EA 4037 - Ecole Centrale Paris)
    Abstract: We present in our work a continuous time Capital Asset Pricing Model where the volatilities of the market index and the stock are both stochastic. Using a singular perturbation technique, we provide approximations for the prices of european options on both the stock and the index. These approximations are functions of the model parameters. We show then that existing estimators of the parameter beta, proposed in the recent literature, are biased in our setting because they are all based on the assumption that the idiosyncratic volatility of the stock is constant. We provide then an unbiased estimator of the parameter beta using only implied volatility data. This estimator is a forward measure of the parameter beta in the sense that it represents the information contained in derivatives prices concerning the forward realization of this parameter, we test then its capacity of prediction of forward beta and we draw a conclusion concerning its predictive power.
    Date: 2014–02–28
  20. By: Varang Wiriyawit
    Abstract: Extracting a trend component from nonstationary data is one of the first challenges in estimating a DSGE model. The misspecification of the component can distort structural parameter estimates and translate into a bias in policy-relevant statistic estimates. This paper investigates how important this bias is to estimated policy implications within a DSGE framework. The quantitative results suggest the bias in parameter estimates due to trend misspecification can result in significant inaccuracies in estimating statistics of interest. This then misleads policy conclusions. Particularly, a misspecified model is estimated using a deterministic-trend specification when the true process is a random-walk with drift.
    JEL: C51 C52 E37
    Date: 2014–04
  21. By: Takashi Kato; Jun Sekine; Hiromitsu Yamamoto
    Abstract: A one-factor asset pricing model with an Ornstein--Uhlenbeck process as its state variable is studied under partial information: the mean-reverting level and the mean-reverting speed parameters are modeled as hidden/unobservable stochastic variables. No-arbitrage pricing formulas for derivative securities written on a liquid asset and exponential utility indifference pricing formulas for derivative securities written on an illiquid asset are presented. Moreover, a conditionally linear filtering result is introduced to compute the pricing/hedging formulas and the Bayesian estimators of the hidden variables.
    Date: 2014–06
  22. By: Roberto Casarin (Department of Economics, University of Venice Cà Foscari); Monica Billio (Department of Economics, University of Venice, Cà Foscari); Anthony Osuntuyi (Department of Mathematics, Obafemi Awolowo University)
    Abstract: A new Bayesian multi-chain Markov Switching GARCH model for dynamic hedging in energy futures markets is developed by constructing a system of simultaneous equations for the return dynamics on the hedged portfolio and futures. More specifically, both the mean and variance of the hedged portfolio are assumed to be governed by two unobserved discrete state processes, while the futures dynamics is driven by a univariate hidden state process. The noise in both processes are characterized by a MS-GARCH model. This formulation has two main practical and conceptual advantages. First, the different states of the discrete processes can be identified as different volatility regimes. Secondly, the parameters can be easily interpreted as different hedging components. Our formulation also provides an avenue to analyze the contribution of the volatility dynamics and state probabilities to the optimal hedge ratio at each point in time. Moreover, the combination of the expected utility framework with regime-switching models allows the definition of a robust minimum variance hedging strategy to also account for parameter uncertainty. Evidence of changes in the optimal hedging strategies before and after the financial crisis is found when the proposed robust hedging strategy is applied to crude oil spot and futures markets.
    Keywords: Energy futures; GARCH; Hedge ratio; Markov-switching.
    JEL: C1 C11 C15 C32 F31 G15
    Date: 2014

This nep-ecm issue is ©2014 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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