References

  • Aiken, R. C. (1985), Stiff Computation, New York: Oxford University Press.

  • Amemiya, T. (1974), “The Nonlinear Two-Stage Least-Squares Estimator,” Journal of Econometrics, 2, 105–110.

  • Amemiya, T. (1977), “The Maximum Likelihood Estimator and the Nonlinear Three-Stage Least Squares Estimator in the General Nonlinear Simultaneous Equation Model,” Econometrica, 45, 955–968.

  • Amemiya, T. (1985), Advanced Econometrics, Cambridge, MA: Harvard University Press.

  • Andersen, T. G., Chung, H.-J., and Sorensen, B. E. (1999), “Efficient Method of Moments Estimation of a Stochastic Volatility Model: A Monte Carlo Study,” Journal of Econometrics, 91, 61–87.

  • Andersen, T. G. and Sorensen, B. E. (1996), “GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study,” Journal of Business and Economic Statistics, 14, 328–352.

  • Andrews, D. W. K. (1991), “Heteroscedasticity and Autocorrelation Consistent Covariance Matrix Estimation,” Econometrica, 59, 817–858.

  • Andrews, D. W. K. and Monahan, J. C. (1992), “Improved Heteroscedasticity and Autocorrelation Consistent Covariance Matrix Estimator,” Econometrica, 60, 953–966.

  • Bansal, R., Gallant, A. R., Hussey, R., and Tauchen, G. E. (1993), “Computational Aspects of Nonparametric Simulation Estimation,” in D. A. Belsey, ed., Computational Techniques for Econometrics and Economic Analysis, 3–22, Boston: Kluwer Academic.

  • Bansal, R., Gallant, A. R., Hussey, R., and Tauchen, G. E. (1995), “Nonparametric Estimation of Structural Models for High-Frequency Currency Market Data,” Journal of Econometrics, 66, 251–287.

  • Bard, Y. (1974), Nonlinear Parameter Estimation, New York: Academic Press.

  • Bates, D. M. and Watts, D. G. (1981), “A Relative Offset Orthogonality Convergence Criterion for Nonlinear Least Squares,” Technometrics, 23, 179–183.

  • Belsley, D. A., Kuh, E., and Welsch, R. E. (1980), Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, New York: John Wiley & Sons.

  • Binkley, J. K. and Nelson, G. (1984), “Impact of Alternative Degrees of Freedom Corrections in Two and Three Stage Least Squares,” Journal of Econometrics, 24, 223–233.

  • Bowden, R. J. and Turkington, D. A. (1984), Instrumental Variables, Cambridge: Cambridge University Press.

  • Bratley, P., Fox, B. L., and Niederreiter, H. (1992), “Implementation and Tests of Low-Discrepancy Sequences,” ACM Transactions on Modeling and Computer Simulation, 2, 195–213.

  • Breusch, T. S. and Pagan, A. R. (1979), “A Simple Test for Heteroscedasticity and Random Coefficient Variation,” Econometrica, 47, 1287–1294.

  • Breusch, T. S. and Pagan, A. R. (1980), “The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics,” Review of Econometric Studies, 47, 239–253.

  • Byrne, G. D. and Hindmarsh, A. C. (1975), “A Polyalgorithm for the Numerical Solution of ODEs,” ACM Transactions on Mathematical Software, 1, 71–96.

  • Calzolari, G. and Panattoni, L. (1988), “Alternative Estimators of FIML Covariance Matrix: A Monte Carlo Study,” Econometrica, 56, 701–714.

  • Chan, K. C., Karolyi, G. A., Longstaff, F. A., and Sanders, A. B. (1992), “An Empirical Comparison of Alternate Models of the Short-Term Interest Rate,” Journal of Finance, 47, 1209–1227.

  • Christensen, L. R., Jorgenson, D. W., and Lau, L. J. (1975), “Transcendental Logarithmic Utility Functions,” American Economic Review, 65, 367–383.

  • Dagenais, M. G. (1978), “The Computation of FIML Estimates as Iterative Generalized Least Squares Estimates in Linear and Nonlinear Simultaneous Equation Models,” Econometrica, 46, 1351–1362.

  • Davidian, M. and Giltinan, D. M. (1995), Nonlinear Models for Repeated Measurement Data, New York: Chapman & Hall.

  • Davidson, R. and MacKinnon, J. G. (1993), Estimation and Inference in Econometrics, New York: Oxford University Press.

  • Duffie, D. and Singleton, K. J. (1993), “Simulated Moments Estimation of Markov Models of Asset Prices,” Econometrica, 61, 929–952.

  • Dulmage, A. L. and Mendelsohn, N. F. (1958), “Coverings of Bipartite Graphs,” Canadian Journal of Mathematics, 10, 517–534.

  • Fair, R. C. (1984), Specification, Estimation, and Analysis of Macroeconometric Models, Cambridge, MA: Harvard University Press.

  • Ferson, W. E. and Foerster, S. R. (1994), “Finite Sample Properties of the Generalized Method of Moments in Tests of Conditional Asset Pricing Models,” Journal of Financial Economics, 36, 29–56, previously released as Working Paper No. 77, University of Washington.

  • Fox, B. L. (1986), “Algorithm 647: Implementation and Relative Efficiency of Quasirandom Sequence Generators,” ACM Transactions on Mathematical Software, 12, 362–276.

  • Gallant, A. R. (1987), Nonlinear Statistical Models, New York: John Wiley & Sons.

  • Gallant, A. R. and Holly, A. (1980), “Statistical Inference in an Implicit, Nonlinear, Simultaneous Equation Model in the Context of Maximum Likelihood Estimation,” Econometrica, 48, 697–720.

  • Gallant, A. R. and Jorgenson, D. W. (1979), “Statistical Inference for a System of Simultaneous, Nonlinear, Implicit Equations in the Context of Instrumental Variables Estimation,” Journal of Econometrics, 11, 275–302.

  • Gallant, A. R. and Long, J. R. (1997), “Estimating Stochastic Differential Equations Efficiently by Minimum Chi-Squared,” Biometrika, 84, 125–141.

  • Gallant, A. R. and Tauchen, G. E. (2001), “Efficient Method of Moments,” Working Paper.
    URL http://www.econ.duke.edu/~get/wpapers/ee.pdf

  • Gill, P. E., Murray, W., and Wright, M. H. (1981), Practical Optimization, New York: Academic Press.

  • Godfrey, L. G. (1978a), “Testing against General Autoregressive and Moving Average Error Models When the Regressors Include Lagged Dependent Variables,” Econometrica, 46, 1293–1301.

  • Godfrey, L. G. (1978b), “Testing for Higher Order Serial Correlation in Regression Equations When the Regressors Include Lagged Dependent Variables,” Econometrica, 46, 1303–1310.

  • Goldfeld, S. M. and Quandt, R. E. (1972), Nonlinear Methods in Econometrics, Amsterdam: North-Holland.

  • Goldfeld, S. M. and Quandt, R. E. (1973a), “The Estimation of Structural Shifts by Switching Regressions,” Annals of Economic and Social Measurement, 2, 475–486.

  • Goldfeld, S. M. and Quandt, R. E. (1973b), “A Markov Model for Switching Regressions,” Journal of Econometrics, 3–16.

  • Goldfeld, S. M. and Quandt, R. E. (1976), Studies in Nonlinear Estimation, Cambridge, MA: Ballinger.

  • Goodnight, J. H. (1979), “A Tutorial on the Sweep Operator,” American Statistician, 33, 149–158.

  • Gourieroux, C. and Monfort, A. (1993), “Simulation Based Inference: A Survey with Special Reference to Panel Data Models,” Journal of Econometrics, 59, 5–33.

  • Greene, W. H. (1993), Econometric Analysis, 2nd Edition, New York: Macmillan.

  • Greene, W. H. (2004), Econometric Analysis, New York: Macmillan.

  • Gregory, A. W. and Veall, M. R. (1985), “On Formulating Wald Tests for Nonlinear Restrictions,” Econometrica, 53, 1465–1468.

  • Grunfeld, Y. and Griliches, Z. (1960), “Is Aggregation Necessarily Bad?” Review of Economics and Statistics, 113–134.

  • Hansen, L. P. (1982), “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, 50, 1029–1054.

  • Hansen, L. P. (1985), “A Method for Calculating Bounds on the Asymptotic Covariance Matrices of Generalized Method of Moments Estimators,” Journal of Econometrics, 30, 203–238.

  • Hatanaka, M. (1978), “On the Efficient Estimation Methods for the Macro-economic Models Nonlinear in Variables,” Journal of Econometrics, 8, 323–356.

  • Hausman, J. A. (1978), “Specification Tests in Econometrics,” Econometrica, 46, 1251–1271.

  • Hausman, J. A. and Taylor, W. E. (1982), “A Generalized Specification Test,” Economics Letters, 8, 239–245.

  • Henze, N. and Zirkler, B. (1990), “A Class of Invariant Consistent Tests for Multivariate Normality,” Communications in Statistics—Theory and Methods, 19, 3595–3617.

  • Johnston, J. (1984), Econometric Methods, 3rd Edition, New York: McGraw-Hill.

  • Jorgenson, D. W. and Laffont, J. (1974), “Efficient Estimation of Nonlinear Simultaneous Equations with Additive Disturbances,” Annals of Social and Economic Measurement, 3, 615–640.

  • Joy, C., Boyle, P. P., and Tan, K. S. (1996), “Quasi-Monte Carlo Methods in Numerical Finance,” Management Science, 42, 926–938.

  • LaMotte, L. R. (1994), “A Note on the Role of Independence in t Statistics Constructed from Linear Statistics in Regression Models,” American Statistician, 48, 238–240.

  • Lee, B. and Ingram, B. (1991), “Simulation Estimation of Time Series Models,” Journal of Econometrics, 47, 197–205.

  • MacKinnon, J. G. and White, H. (1985), “Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties,” Journal of Econometrics, 29, 305–325.

  • Maddala, G. S. (1977), Econometrics, New York: McGraw-Hill.

  • Mardia, K. V. (1970), “Measures of Multivariate Skewness and Kurtosis with Applications,” Biometrika, 57, 519–530.

  • Mardia, K. V. (1974), “Applications of Some Measures of Multivariate Skewness and Kurtosis in Testing Normality and Robustness Studies,” Indian Journal of Statistics, 36, 115–128.

  • Matis, J. H., Miller, T. H., and Allen, D. M. (1991), Metal Ecotoxicology: Concepts and Applications, Chelsea, MI: Lewis Publishers.

  • Matsumoto, M. and Nishimura, T. (1998), “Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-random Number Generator,” ACM Transactions on Modeling and Computer Simulation, 8, 3–30.

  • McFadden, D. (1989), “A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration,” Econometrica, 57, 995–1026.

  • McNeil, A., Frey, R., and Embrechts, P. (2005), Quantitative Risk Management: Concepts, Techniques, and Tools, Princeton, NJ: Princeton University Press.

  • Messer, K. and White, H. (1994), “A Note on Computing the Heteroskedasticity Consistent Covariance Matrix Using Instrumental Variable Techniques,” Oxford Bulletin of Economics and Statistics, 46, 181–184.

  • Mikhail, W. M. (1975), “A Comparative Monte Carlo Study of the Properties of Economic Estimators,” Journal of the American Statistical Association, 70, 94–104.

  • Miller, D. M. (1984), “Reducing Transformation Bias in Curve Fitting,” American Statistician, 38, 124–126.

  • Morelock, M. M., Pargellis, C. A., Graham, E. T., Lamarre, D., and Jung, G. (1995), “Time-Resolved Ligand Exchange Reactions: Kinetic Models for Competitive Inhibitors with Recombinant Human Renin,” Journal of Medical Chemistry, 38, 1751–1761.

  • Nelsen, R. B. (1999), An Introduction to Copulas, New York: Springer-Verlag.

  • Newey, W. K. and West, D. W. (1987), “A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55, 703–708.

  • Noble, B. and Daniel, J. W. (1977), Applied Linear Algebra, Englewood Cliffs, NJ: Prentice-Hall.

  • Ortega, J. M. and Rheinbolt, W. C. (1970), Iterative Solution of Nonlinear Equations in Several Variables, Burlington, MA: Academic Press.

  • Pakes, A. and Pollard, D. (1989), “Simulation and the Asymptotics of Optimization Estimators,” Econometrica, 57, 1027–1057.

  • Parzen, E. (1957), “On Consistent Estimates of the Spectrum of a Stationary Time Series,” Annals of Mathematical Statistics, 28, 329–348.

  • Pearlman, J. G. (1980), “An Algorithm for the Exact Likelihood of a High-Order Autoregressive–Moving Average Process,” Biometrika, 67, 232–233.

  • Petzold, L. R. (1982), “Differential/Algebraic Equations Are Not ODEs,” SIAM Journal on Scientific and Statistical Computing, 3, 367–384.

  • Phillips, C. B. and Park, J. Y. (1988), “On Formulating Wald Tests of Nonlinear Restrictions,” Econometrica, 56, 1065–1083.

  • Pindyck, R. S. and Rubinfeld, D. L. (1981), Econometric Models and Econometric Forecasts, 2nd Edition, New York: McGraw-Hill.

  • Pothen, A. and Fan, C.-J. (1990), “Computing the Block Triangular Form of a Sparse Matrix,” ACM Transactions on Mathematical Software, 16, 303–324.

  • Rebonato, R. and Jäckel, P. (1999), “The Most General Methodology for Creating Valid Correlation Matrix for Risk Management and Option Pricing Purposes,” Journal of Risk, 2, 17–27.

  • Savin, N. E. and White, K. J. (1978), “Testing for Autocorrelation with Missing Observations,” Econometrica, 46, 59–67.

  • Sobol, I. M. (1994), A Primer for the Monte Carlo Method, Boca Raton, FL: CRC Press.

  • Srivastava, V. and Giles, D. E. A. (1987), Seemingly Unrelated Regression Equation Models, New York: Marcel Dekker.

  • Theil, H. (1971), Principles of Econometrics, New York: John Wiley & Sons.

  • Thursby, J. (1982), “Misspecification, Heteroscedasticity, and the Chow and Goldfield-Quandt Test,” Review of Economics and Statistics, 64, 314–321.

  • Venzon, D. J. and Moolgavkar, S. H. (1988), “A Method for Computing Profile-Likelihood Based Confidence Intervals,” Applied Statistics, 37, 87–94.

  • White, H. (1980), “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity,” Econometrica, 48, 817–838.

  • Wu, D. M. (1973), “Alternative Tests of Independence between Stochastic Regressors and Disturbances,” Econometrica, 41, 733–750.