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on Econometrics |
| By: | Xun Lu (Department of Finance, Hong Kong University of Science and Technology); Liangjun Su (School of Economics, Singapore Management University, Singapore, 178903) |
| Abstract: | In this paper we consider the problem of frequentist model averaging for quantile regression (QR) when all the M models under investigation are potentially misspecified and the number of parameters in some or all models is diverging with the sample size n. To allow for the dependence between the error terms and the regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate that the jackknife selected weight vector is asymptotically optimal in terms of minimizing the out-of-sample final prediction error among the given set of weight vectors. We conduct Monte Carlo simulations to demonstrate the finite-sample performance of the proposed JMA QR estimator and compare it with other model selection and averaging methods. We find that in terms of out-of-sample forecasting, the JMA QR estimator can achieve significant efficiency gains over the other methods, especially for extreme quantiles. We apply our JMA method to forecast quantiles of excess stock returns and wages. |
| Keywords: | Final prediction error; High dimensionality; Model averaging; Model selection; Misspecification; Quantile regression |
| JEL: | C51 C52 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:11-2014&r=ecm |
| By: | Guo, Xu; Li, Gao Rong; Wong, Wing Keung |
| Abstract: | Parametric production frontier function has been commonly employed in stochas-tic frontier model but there was no proper test statistic for its plausibility. To fill into this gap, this paper develops two test statistics to test for a hypothesized parametric production frontier function based on local smoothing and global smoothing, respectively. We then pro-pose the residual-based wild bootstrap approach to compute the p-values of our proposed test statistics. Our proposed test statistics are robust to heteroscedasticity. Simulation studies are carried out to examine the infinite sample performance of the sizes and powers of the test statistics. |
| Keywords: | Stochastic frontier; Specification testing; Wild bootstrap. |
| JEL: | C13 C14 |
| Date: | 2014–08–18 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:57999&r=ecm |
| By: | Liangjun Su (Singapore Management University, School of Economics); Sainan Jin (Singapore Management University, School of Economics); Yonghui Zhang (School of Economics, Renmin University of China) |
| Abstract: | In this paper, we propose a consistent nonparametric test for linearity in a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives by using the concept of conditional strong mixing that was recently introduced by Prakasa Rao (2009). To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap p-value. A small set of Monte Carlo simulations illustrates that our test performs well in finite samples. An application to an economic growth panel dataset indicates significant nonlinear relationships between economic growth, initial income level and capital accumulation. |
| Keywords: | Common factors; Conditional strong mixing; Cross-sectional dependence; Economic Growth; Interactive fixed effects; Linearity; Panel data models; Specification test. |
| JEL: | C12 C14 C23 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:08-2014&r=ecm |
| By: | Yan Li (Department of Finance, Temple University, Philadelphia, PA 19122); Liangjun Su (School of Economics, Singapore Management University, Singapore, 178903); Yuewu Xu (School of Business, Fordham University, New York, NY 10019) |
| Abstract: | This paper develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature–the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first stage is correctly specified. Simulations indicate that our estimates and tests perform well in finite samples. In our empirical analysis, we use our new method to examine the performance of the conditional CAPM, which has generated controversial results in the recent asset-pricing literature. |
| Keywords: | Conditional Factor Models, Specification Tests, Semiparametric Method, Nonparametric Method, Conditional CAPM. |
| JEL: | C51 C52 G12 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:siu:wpaper:10-2014&r=ecm |
| By: | Mikkel Bennedsen (Aarhus University and CREATES); Asger Lunde (Aarhus University and CREATES); Mikko S. Pakkanen (Aarhus University and CREATES) |
| Abstract: | Motivated by the construction of the Itô stochastic integral, we consider a step function method to discretize and simulate volatility modulated Lévy semistationary processes. Moreover, we assess the accuracy of the method with a particular focus on integrating kernels with a singularity at the origin. Using the simulation method, we study the finite sample properties of some recently developed estimators of realized volatility and associated parametric estimators for Brownian semistationary processes. Although the theoretical properties of these estimators have been established under high frequency asymptotics, it turns out that the estimators perform well also in a low frequency setting. |
| Keywords: | Stochastic simulation, discretization, Lévy semistationary processes, stochastic volatility, estimation, finite sample properties |
| JEL: | C13 C15 C63 |
| Date: | 2014–11–08 |
| URL: | http://d.repec.org/n?u=RePEc:aah:create:2014-21&r=ecm |
| By: | Sheng Wang; Jun Shao; Jae Kwang Kim |
| Keywords: | Consistency and asymptotic normality , generalized method of moments, missing not at random, nonparametric distribution, nonresponse instrument, parametric propensity |
| JEL: | C |
| Date: | 2014–07–01 |
| URL: | http://d.repec.org/n?u=RePEc:mpr:mprres:8160&r=ecm |
| By: | Trojan, Sebastian |
| Abstract: | WA multivariate stochastic volatility (MSV) model based on a Cholesky-type decomposition of the covariance matrix to model dynamic correlation in the observation and transition error as well as in cross leverage terms is proposed. The empirically relevant asymmetric concept of cross leverage is defined as a nonzero correlation between the ith asset return at time t and the jth log-volatility at time t+1. Volatilities and covariances are modeled separately, which makes an interpretation of leverage parameters straightforward. The model is applied on a three-dimensional portfolio consisting of the S&P 500 sector indices Financials, Industrials and Healthcare, spanning the recent financial crisis 2008/09. During and in the aftermath of market turmoil, increased cross leverage effects, higher unconditional kurtosis and stronger correlated information flow are observed. However, there is risk of overfitting and restricting time variation to elements governing dynamics of the observation error may be advisable. |
| Keywords: | Multivariate stochastic volatility, dynamic correlation, cross leverage, Cholesky decomposition, nonlinear state space model, Markov chain Monte Carlo, block sampler, particle filter |
| JEL: | C11 C15 C32 C58 |
| Date: | 2014–08 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2014:24&r=ecm |