The MODEL Procedure


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 Computational Techniques for Econometrics and Economic Analysis, edited by D. A. Belsey, 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.

  • Chambers, R. G. (1988). Applied Production Analysis: A Dual Approach. New York: Cambridge University Press.

  • 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 12:362–376.

  • 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. 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 ed. 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 42:1–13.

  • 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 ed. 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. Edited by M. C. Newman and A. W. McIntosh. 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.

  • Parresol, B. R. (1999). “Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons.” Forest Science 45:573–593.

  • 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 ed. 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.” Journal of the Royal Statistical Society, Series C 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.