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References


CSDA Bibliography on Computational and Financial Econometrics
The journal Computational Statistics and Data Analysis aims to have regular issues on computational and financial econometrics. Of particular interest are papers in important areas of econometric applications where both computational techniques and numerical methods have a major impact. The goal is to provide sources of information about the most recent developments in computational and financial econometrics that are currently scattered throughout publications in specialized areas. During the last several years, CSDA issues have included a number of articles related to econometrics and financial econometrics. As a convenience, the following bibliography of these articles is provided [1-107], which indicates the increasing importance of econometrics in CSDA.

References
[1] A. Amendola, C. Francq and S.J. Koopman, Special issue on nonlinear modelling and financial econo-metrics. Computational Statistics and Data Analysis, 51(4):2115-2117, 2006. [2] M. Aminghafari, N. Cheze and J.-M. Poggi, Multivariate denoising using wavelets and principal component analysis. Computational Statistics and Data Analysis, 50(9):2381-2398, 2006. [3] J. Arteche, Semiparametric estimation in perturbed long memory series. Computational Statistics and Data Analysis, 51(4):2118-2141, 2006. [4] F. Audrino, The impact of general non-parametric volatility functions in multivariate GARCH models. Computational Statistics and Data Analysis, 50(11):3032-3052, 2006. [5] F. Audrino and G. Barone-Adesi, A dynamic model of expected bond returns: a functional gradient descent approach. Computational Statistics and Data Analysis, 51(4):2267-2277, 2006. [6] M. C. Ausin and P. Galeano, Bayesian estimation of the Gaussian mixture GARCH model. Computational Statistics and Data Analysis, 51(5):2636-2652, 2006. [7] G. Barone-Adesi, H. Rasmussen and C. Ravanelli, An option pricing formula for the GARCH defusion model. Computational Statistics and Data Analysis, 49(2): 287-310, 2005. [8] F. Bartolucci and G. De Luca, Likelihood-based inference for asymmetric stochastic volatility models. Computational Statistics and Data Analysis, 42(3):445–449, 2003. [9] F. Bartolucci and V. Nigro, Maximum likelihood estimation of an extended latent Markov model for clustered binary panel data. Computational Statistics and Data Analysis, 51(7):3470-3483, 2007. [10] L. Bauwens, C.M. Hafner and J.V.K. Rombouts, Multivariate mixed normal conditional heteroskedasticity. Computational Statistics and Data Analysis, 51(7):3551-3566, 2007. [11] D.A. Belsley and E.J. Kontoghiorghes, Special issue on computational econometrics. Computational Statistics and Data Analysis, 42(3):277–278, 2003. [12] D.A. Belsley and E.J Kontoghiorghes, Second special issue on computational econometrics. Computational Statistics and Data Analysis, 2005. 49(2): 283-285.

[13] D.A. Belsley, E.J Kontoghiorghes and J.R. Magnus, The third special issue on computational econometrics. Computational Statistics and Data Analysis, 51(7): 3258-3505, 2007. [14] S. Broda, K. Carstensen and M.S. Paolella, Bias-Adjusted estimation and unit root testing in ARX(1). Computational Statistics and Data Analysis, 51(7):3355-3367, 2007. [15] C. Brownless and G. Gallo, Financial econometric analysis at ultra-high frequency: data handling concerns. Computational Statistics and Data Analysis, 51(4):2232-2245, 2006. [16] C. Broto and E. Ruiz, Unobserved component models with asymmetric conditional variances. Computational Statistics and Data Analysis, 50(9):2146-2166, 2006. [17] J. Bulla and I. Bulla, Stylized facts of financial time series and hidden semi-Markov models. Computational Statistics and Data Analysis, 51(4):2192-2209, 2006. [18] E. Capobianco, Independent multiresolution component analysis and matching pursuit. Computational Statistics and Data Analysis, 42(3):385–402, 2003. [19] C.W.S. Chen, R. Gerlach and M.K.P. So, Comparison of non-nested asymmetric heteroskedastic models. Computational Statistics and Data Analysis, 51(4):2164-2178, 2006. [20] J.S. Chipman and P.Winker, Optimal aggregation of linear time series models. Computational Statistics and Data Analysis, 49(2): 311-331, 2005. [21] M. Clements and J.H. Kim, Bootstrap prediction intervals for autoregressive time series. Computational Statistics and Data Analysis, 51(7):3580-3594, 2007. [22] J. Coakley, A.M. Fuertes and R. Smith, Unobserved heterogeneity in panel time series models. Computational Statistics and Data Analysis, 50(9):2361-2380, 2006. [23] G. Cubadda and P. Omtzigt, Small-sample improvements in the statistical analysis of seasonally coin-tegrated systems. Computational Statistics and Data Analysis, 49(2): 333-348, 2005. [24] R. Davidson and J.G. MacKinnon, Improving the reliability of bootstrap tests with the fast double bootstrap. Computational Statistics and Data Analysis, 51(7):3259-3281, 2007. [25] G. De Luca and P. Zuccolotto, Regime-switching Pareto distributions for ACD models. Computational Statistics and Data Analysis, 51(4):2179-2191, 2006. [26] J. Detemple and M. Rindisbacher, Monte Carlo methods for derivatives of options with discontinuous payoffs. Computational Statistics and Data Analysis, 51(7):3393-3417, 2007. [27] J.A. Doornik and M. Ooms, Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models. Computational Statistics and Data Analysis, 42(3):333–348, 2003. [28] P. Duchesne, Testing for multivariate autoregressive conditional heteroskedasticity using wavelets. Computational Statistics and Data Analysis, 51(4):2142-2163, 2006. [29] B. Eklund, Estimating confidence regions over bounded domains. Computational Statistics and Data Analysis, 49(2): 349-360, 2005. [30] J.C. Escanciano and C. Velasco, Testing the martingale difference hypothesis using integrated regression functions. Computational Statistics and Data Analysis, 51(4):2278-2294, 2006.

[31] M.E. Esteban-Bravo and J.M.V. Sanz, Worst-case estimation for econometric models with unobservable components. Computational Statistics and Data Analysis, 51(7):3330-3354, 2007. [32] E. Flachaire. Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap. Computational Statistics and Data Analysis, 49(2): 361-376, 2005. [33] E. Flachaire and O. Nunez, Estimation of income distribution and detection of subpopulations: an explanatory model. Computational Statistics and Data Analysis, 51(7):3368-3380, 2007. [34] P.W. Fong, W.K. Li and H.-Z. An, A simple multivariate ARCH model specified by random coefficients. Computational Statistics and Data Analysis, 51(3):1779-1802, 2006. [35] P. Foschi, D.A. Belsley and E.J. Kontoghiorghes, A comparative study of algorithms for solving seem-ingly unrelated regressions models. Computational Statistics and Data Analysis, 44(12):3–35, 2003. [36] P. Foschi and E.J. Kontoghiorghes, Seemingly unrelated regressionmodel with unequal size observations: computational aspects. Computational Statistics and Data Analysis, 41(1):211–229, 2002. [37] R. Fried, T. Bernholt and U. Gather, Repeated median and hybrid filters. Computational Statistics and Data Analysis, 50(9):2313-2338, 2006. [38] B. Frijns and P.C. Schotman, Nonlinear dynamics in Nasdaq dealer quotes. Computational Statistics and Data Analysis, 51(4):2246-2266, 2006. [39] S. Fruhwirth-Schnatter and R. Fruhwirth, Auxiliary mixture sampling with applications to logistic models. Computational Statistics and Data Analysis, 51(7):3509-3528, 2007. [40] A.-M. Fuertes and E. Kalotychou, Early warning systems for sovereign debt crises: The role of hetero-geneity. Computational Statistics and Data Analysis, 51(2):1420-1441, 2006. [41] A.-M. Fuertes and E. Kalotychou, On sovereign credit migration: A study of alternative estimators and rating dynamics. Computational Statistics and Data Analysis, 51(7):3448-3469, 2007. [42] D. Gamerman, A.R.B. Moreira and H. Rue, Space-varying regression models: specifications and simulation. Computational Statistics and Data Analysis, 42(3):513–533, 2003. [43] A.E. Gelfand, S. Banerjee, C.F. Sirmans and Y. Tu and S.E. Ong, Multilevel modelling using spatial processes: application to the Singapore housing market. Computational Statistics and Data Analysis, 51(7):3567-3579, 2007. [44] S. Gelper and C. Croux, Multivariate out-of-sample tests for Granger causality. Computational Statistics and Data Analysis, 51(7):3319-3329, 2007. [45] J. Geweke, Interpretation and inference in mixture models: simple MCMC works. Computational Statistics and Data Analysis, 51(7):3529-3550, 2007. [46] J. Geweke, P.J.F. Groenen, R. Paap and H.K. van Dijk, Editorial Introduction - Computational techniques for applied econometric analysis of macroeconomic and financial processes. Computational Statistics and Data Analysis, 51(7):3506-3508, 2007. [47] M. Gilli and P. Winker, A global optimization heuristic for estimating agent based models. Computational Statistics and Data Analysis, 42(3):299–312, 2003.

[48] L.G. Godfrey, Tests for regression models with heteroskedasticity of unknown form. Computational Statistics and Data Analysis, 50(10):2715-2733, 2006. [49] L.G. Godfrey, Alternative approaches to implementing Lagrange multiplier tests for serial correlation in dynamic regression models. Computational Statistics and Data Analysis, 51(7):32823295, 2007. [50] L.G. Godfrey and A.R. Tremayne, The wild bootstrap to implement heteroskedasticity-robust tests for serial correlation in dynamic regression models. Computational Statistics and Data Analysis, 49(2):377-395, 2005 [51] E.J. Godolphin and K. Triantafyllopoulos, Decomposition of time series models in state-space form. Computational Statistics and Data Analysis, 50(9):2232-2246, 2006. [52] N. Gulpinar and B. Rustem, Robust optimal decisions with erroneous data and imprecise forecasts. Computational Statistics and Data Analysis, 51(7):3595-3611, 2007. [53] C. Hartz, S. Mittnik and M. Paolella, Accurate value at risk forecasting based on the normalGARCH model. Computational Statistics and Data Analysis, 51(4):2295-2312, 2006. [54] D.I. Harvey and D. van Dijk Sample size, lag order and critical values of seasonal unit root tests. Computational Statistics and Data Analysis, 50(10):2734-2751, 2006. [55] C. Heij, P.J.F. Groenen and D. van Dijk, Forecast comparison of principal component regression and principal covariate regression. Computational Statistics and Data Analysis, 51(7):3612-3625, 2007. [56] H. Herwartz, Testing for random effects in panel data under cross sectional error correlation– A bootstrap approach to the Breusch Pagan test. Computational Statistics and Data Analysis, 50(12):3567–3591, 2006. [57] Z. Hl?avka, Fast algorithm for nonparametric arbitrage-free SPD estimation. Computational Statistics and Data Analysis, 51(4):2339-2349, 2006. [58] G. Jewitt and J.R. McCrorie, Computing estimates of continuous time macroeconometric models on the basis of discrete data. Computational Statistics and Data Analysis, 49(2):397-416, 2005. [59] M. Jhun, S.H. Song and B.C. Jung, BLUP in the nested panel regression model with serially correlated errors. Computational Statistics and Data Analysis, 44(1-2):77–88, 2003. [60] A. Joseph and J.F. Kiviet, Viewing the relative efficiency of IV estimators in models with lagged and instantaneous feedbacks. Computational Statistics and Data Analysis, 49(2): 417-444, 2005. [61] R.C. Jung, M. Kukuk and R. Liesenfeld, Time series of count data: modelling, estimation and diagnostics. Computational Statistics and Data Analysis, 51(4):2350-2364, 2006. [62] L. Khalaf and M. Kichian, Exact tests of the stability of the Phillip’s curve: The Canadian case. Computational Statistics and Data Analysis, 49(2):445-460, 2005. [63] J.H. Kim, P. Silvapulle and R.J. Hyndman, Half-life estimation based on the bias-corrected bootstrap: A highest density region approach. Computational Statistics and Data Analysis, 51(7):3418-3432, 2007.

[64] M.S. Kim and S. Wang, On the applicability of a stochastic volatility models. Computational Statistics and Data Analysis, 51(4):2210-2217, 2006. [65] J.F. Kiviet and J. Niemczyk, The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations. Computational Statistics and Data Analysis, 51(7):3296-3318, 2007. [66] E.J. Kontoghiorghes andM.R.B. Clarke, An alternative approach for the numerical solution of seemingly unrelated regression equations models. Computational Statistics and Data Analysis, 19(4):369–377, 1995. [67] S.J. Koopman and M. Ooms, Forecasting daily time series using periodic unobserved components time series models. Computational Statistics and Data Analysis, 51(2):885-903, 2006. [68] T. Krink, S. Paterlini and A. Resti, Using differential evolution to improve the accuracy of bank rating systems. Computational Statistics and Data Analysis, 2007 (In press). [69] A. Maravall, An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment. Computational Statistics and Data Analysis, 50(9):2167-2190, 2006. [70] J.A. Mauricio, Exact maximum likelihood estimation of partially nonstationary vector ARMA models. Computational Statistics and Data Analysis, 50(12):3644-3662, 2006. [71] M. Mazzocchi, Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm. Computational Statistics and Data Analysis, 50(9):2191-2205, 2006. [72] T. McElroy and A. Sutcliffe, An iterated parametric approach to nonstationary signal extraction. Computational Statistics and Data Analysis, 50(9):2206-2231, 2006. [73] T. Miazhynskaia, S. Fruhwirth-Schnatter and G. Dorffner, Bayesian testing for non-linearity in volatility modeling. Computational Statistics and Data Analysis, 51(1):2029-2042, 2006. [74] D. Miles and J. Mora, On the performance of nonparametric specification tests in regression models. Computational Statistics and Data Analysis, 42(3):477–490, 2003. [75] C. Monfardini. An illustration of Cox’s non-nested testing procedure for logit and probit models. Computational Statistics and Data Analysis, 42(3):425–444, 2003. [76] M.K. Munkin, The MCMC and SML estimation of a self-selection model with two outcomes. Computational Statistics and Data Analysis, 42(3):403–424, 2003. [77] J.C. Nankervis, Computational algorithms for double bootstrap confidence intervals. Computational Statistics and Data Analysis, 49(2): 461-475, 2005. [78] H. Ombao and M.R. Ho, Time-dependent frequency domain principal components analysis of multichannel non-stationary signals. Computational Statistics and Data Analysis, 50(9):2339-2360, 2006. [79] S. Orbe, E. Ferreira, and J. Rodriguez-Poo, An algorithm to estimate time-varying parameter SURE models under different types of restriction. Computational Statistics and Data Analysis, 42(3):363–383, 2003. [80] M.S. Paolella, Computing moments of ratios of quadratic forms in normal variables. Computational Statistics and Data Analysis, 42(3):313–331, 2003.

[81] L. Pascual, J. Romo and E. Ruiz, Bootstrap prediction for returns and volatilities in GARCH models. Computational Statistics and Data Analysis, 50(9):2293-2312, 2006. [82] F. Pattarin, S. Paterlini and T. Minerva, Clustering financial time series: an application to mutual funds style analysis. Computational Statistics and Data Analysis, 47(2):353-372, 2004. [83] A. Perez-Alonso, A bootstrap approach to test the conditional symmetry in time series models. Computational Statistics and Data Analysis, 51(7):3484-3504, 2007. [84] D.S.G. Pollock, Improved frequency selective filters. Computational Statistics and Data Analysis, 42(3):279–297, 2003. [85] D.S.G. Pollock, Recursive estimation in econometrics. Computational Statistics and Data Analysis, 44(1-2):37–75, 2003. [86] D.S.G. Pollock, Econometric methods of signal extraction. Computational Statistics and Data Analysis, 50(9):2268-2292, 2006. [87] D.S.G. Pollock, Introduction to the special issue on statistical signal extraction and filtering. Computational Statistics and Data Analysis, 50(9):2137-2145, 2006. [88] T. Proietti, Forecasting the US unemployment rate. Computational Statistics and Data Analysis, 42(3):451–476, 2003. [89] T. Proietti, New algorithms for dating the business cycle. Computational Statistics and Data Analysis, 49(2):477-498, 2005. [90] D.P. Reagle and H.D. Vinod, Inference for negativist theory using numerically computed rejection regions. Computational Statistics and Data Analysis, 42(3):491–512, 2003. [91] M. Rekkas and A. Wong, Third-order inference for the Weibull distribution. Computational Statistics and Data Analysis, 49(2):499-525, 2005. [92] A.D. Sanford and G.M. Martin, Simulation-based Bayesian estimation of affine term structure models. Computational Statistics and Data Analysis, 49(2):527-554, 2005. [93] C. Siani and C. de Peretti, Analysing the performance of bootstrap neural tests for conditional heteroskedasticity in ARCH-M models. Computational Statistics and Data Analysis, 51(5):2442-2460, 2007. [94] M.B. Stewart, A comparison of semiparametric estimators for the ordered response model. Computational Statistics and Data Analysis, 49(2):555-573, 2005. [95] G. Storti, Minimum distance estimation of GARCH(1,1) models. Computational Statistics and Data Analysis, 51(3):1803-1821, 2006. [96] C.M. Strickland, C.S. Forbes and G.M. Martin, Bayesian analysis of the stochastic conditional duration model. Computational Statistics and Data Analysis, 50(9):2247-2267, 2006. [97] P.A.V.B. Swamy, G.S. Tavlas and I.-L. Chang, How stable are monetary policy rules: Estimating the time-varying coefficients in monetary policy reaction function for the U.S. Computational Statistics and Data Analysis, 49(2):575-590, 2005.

[98] P.A.V.B. Swamy, W. Yaghia, J.S. Mehta and I.-L. Chang, Empirical best linear unbiased prediction in misspecified and improved panel data models with an application to gasoline demand. Computational Statistics and Data Analysis, 51(7):3381-3392, 2007. [99] A.A. Trindade and Y. Zhu, Approximating distributions of estimators of financial risk under an asymmrtric Laplace Law. Computational Statistics and Data Analysis, 51(7):3433-3447, 2007. [100] C. Varin and P. Vidoni, Pairwise likelihood inference for ordinal categorical time series. Computational Statistics and Data Analysis, 51(4):2365-2373, 2006. [101] E. Vassiliou and I.C. Demetriou, An adaptive algorithm for least squares piecewise monotonic datafitting. Computational Statistics and Data Analysis, 49(2):591-609, 2005. [102] E. Xekalaki and S. Degiannakis, Evaluating volatility forecasts in option pricing in the context of a simulated options market. Computational Statistics and Data Analysis, 49(2):611-629, 2005. [103] J. Yu, Z. Yang and X. Zhang, A Class of nonlinear stochastic volatility models and its implications on pricing currency options. Computational Statistics and Data Analysis, 51(4):22182231, 2006. [104] A. Zeileis, Implementing a class of structural change tests: An econometric computing approach. Computational Statistics and Data Analysis, 50(11):2987-3008, 2006. [105] A. Zeileis, C. Kleiber, W. Kramer and K. Hornik, Testing and dating of structural changes in practice. Computational Statistics and Data Analysis, 44(1-2):109-123, 2003. [106] Z. Zhang and J. Huang, Extremal financial risk models and portfolio evaluation. Computational Statistics and Data Analysis, 51(4):2313-2338, 2006. [107] X. Zhang, M.L. King and R.J. Hyndman, A Bayesian approach to bandwidth selection for multivariate kernel density estimation. Computational Statistics and Data Analysis, 50(11):30093031, 2006.



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