2046 Pages
    by Routledge

    In the memorable words of Ragnar Frisch, econometrics is ‘a unification of the theoretical–quantitative and the empirical–quantitative approach to economic problems’. Beginning to take shape in the 1930s and 1940s, econometrics is now recognized as a vital subdiscipline supported by a vast—and still rapidly growing—body of literature.

    Following the positive reception of The Rise of Econometrics (2013) (978-0-415-61678-2), Routledge now announces a new collection bringing together the best that has been published on the practical application and functional use of economic metrics and measurements. With a comprehensive introduction, newly written by the editor, which places the assembled materials in their historical and intellectual context, Applied Econometrics is an essential work of reference. This fully indexed collection will be particularly useful as an indispensable database allowing scattered and often fugitive material to be easily located. It will also be welcomed as a crucial tool permitting rapid access to less familiar—and sometimes overlooked—texts. For researchers and students, as well as economic policy-makers, it is a vital one-stop research and pedagogic resource.


    Part 1: Methodology—Foundation

    1. R. Frisch and F. Waugh, ‘Partial Time Regressions as Compared with Individual Trends’, Econometrica, 1933, 1, 387–401.

    2. E. Working, ‘What Do Statistical Demand Curves Show’, Quarterly Journal of Economics, 1926, 41, 212–35.

    Part 2: Methodology—Modern Practice

    3. D. F. Hendry, ‘Modelling UK Inflation, 1875–1991’, Journal of Applied Econometrics, 2001, 16, 255–75.

    4. L. Hansen, ‘Large Sample Properties of Generalized Method of Moments Estimators’, Econometrica, 1982, 50, 1029–54.

    5. C. Manski, ‘Nonparametric Bounds on Treatment Effects’, American Economic Review, 1990, 80, 319–23.

    6. H. White, ‘Maximum Likelihood Estimation of Misspecified Models’, Econometrica, 1982, 53, 1–16.

    7. J. Heckman, ‘Sample Selection as a Specification Error’, Econometrica, 1979, 47, 153–61.

    8. Joshua D. Angrist and Jörn-Steffen Pischke, ‘The Credibility Revolution in Empirical Economics: How Better Research Design is Taking the Con Out of Econometrics’, Journal of Economic Perspectives, 2010, 24, 2, 3–30.

    9. M. Keane, ‘A Structuralist Perspective on the Experimentalist School’, Journal of Economic Perspectives, 2010, 24, 47–58.

    Part 3: Microeconometrics

    10. D. McFadden, ‘Conditional Logit Analysis of Qualitative Choice’, in P. Zarembka (ed.), Frontiers of Econometrics (Academic Press1973), pp. 105–42.

    11. A. Cameron and P. Trivedi, ‘Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests’, Journal of Applied Econometrics, 1986, 1, 29–54.

    12. O. Ashenfelter and J. Heckman, ‘The Estimation of Income and Substitution Effects in a Model of Family Labor Supply’, Econometrica, 1974, 42, 73–85.

    Part 4: Macroeconometrics

    13. R. Engle, ‘Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflations’, Econometrica, 1982, 50, 987–1008.

    14. J. H. Stock and M. W. Watson, ‘Vector Autoregressions’, Journal of Economic Perspectives, 2001, 15, 101–16.

    15. R. Litterman, ‘Forecasting with Bayesian Vector Autoregressions: Five Years of Experience’, Journal of Business and Economic Statistics, 1986, 4, 25–38.

    16. R. Engle and C. Granger, ‘Co-Integration and Error Correction’ Representation, Estimation and Testing’, Econometrica, 1987, 35, 251–76.

    17. S. Cecchetti and R. Rich, ‘Structural Estimates of the U.S. Sacrifice Ratio’, Journal of Business and Economic Statistics, 2001, 19, 4, 416–27.

    18. J. H. Stock, M. Yogo, and J. Wright, ‘A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments’, Journal of Business and Economic Statistics, 2002, 20, 518–29.


    Part 5: Model Specification

    19. C. Cobb and P. Douglas, ‘A Theory of Production’, American Economic Review, 1928, 18, 139–65.

    20. K. Arrow, H. Chenery, B. Minhas, and R. Solow, ‘Capital-Labor Substitution and Economic Efficiency’, Review of Economics and Statistics, 1961, 45, 225–47.

    21. L. Christensen, D. Jorgenson, and L. Lau, ‘Transcendental Logarithmic Utility Functions’, American Economic Review, 1975, 65, 367–83.

    22. L. Christensen and W. Greene, ‘Economies of Scale in U.S. Electric Power Generation’, Journal of Political Economy, 1976, 84, 655–76.

    23. J. Tobin, ‘Estimation of Relationships for Limited Dependent Variables’, Econometrica, 1958, 26, 24–36.

    24. T. Amemiya, ‘Qualitative Response Models: A Survey’, Journal of Econometric Literature, 1981, 4, 481–536.

    25. A. Harvey, ‘Estimating Regression Models with Multiplicative Heteroscedasticity’, Econometrica, 1976, 44, 461–5.

    26. R. Zavoina and W. McKelvey, ‘A Statistical Model for the Analysis of Ordinal Level Dependent Variables’, Journal of Mathematical Sociology, Summer 1975, 103–20.

    27. L. Lee, ‘Generalized Econometric Models with Selectivity’, Econometrica, 1983, 51, 507–12.

    28. D. McFadden and K. Train, ‘Mixed Multinomial Logit Models for Discrete Response’, Journal of Applied Econometrics, 2000, 15, 447–70.

    29. A. Zellner, ‘An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests of Aggregation Bias’, Journal of the American Statistical Association, 1962, 57, 500–9.

    30. R. Koenker and G. Bassett, ‘Regression Quantiles’, Econometrica, 1978, 46, 107–12.

    31. A. Raftery, D. Madigan, and J. Hoeting, ‘Bayesian Model Averaging for Linear Regression Models’, Journal of the American Statistical Association, 1997, 92, 179–91.

    32. X. Sala-i-Martin, ‘I Just Ran Two Million Regressions’, American Economic Review, 1997, 87, 178–83.

    Part 6: Model Estimation

    33. R. Basmann, ‘A General Classical Method of Linear Estimation of Coefficients in a Structural Equation’, Econometrica, 1957, 25, 77–83.

    34. J. Terza, A. Basu, and R. Bathouz, ‘Two-Stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling’, Journal of Health Economics, 2008, 27, 531–43.

    35. D. Card and A. Krueger, ‘Minimum Wages and Employment: A Case Study of the Fast-Food Industry in New Jersey and Pennsylvania’, American Economic Review, 1994, 84, 772–93.

    Part 7: Inference

    36. B. Efron, ‘Bootstrap Methods: Another Look at the Jackknife’, The Annals of Statistics, 1979, 7, 1–26.

    37. H. White, ‘A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity’, Econometrica, 1980, 48, 817–38.

    38. W. Newey and K. West, ‘A Simple Positive Semi-Definite, Heteroscedasticity and Autocorrelation Consistent Covariance Matrix’, Econometrica, 1987, 55, 703–8.

    39. E. Berndt, B. Hall, R. Hall, and J. Hausman, ‘Estimation and Inference in Nonlinear Structural Models’, Annals of Economic and Social Measurement, 1974, 3/4, 653–65.

    40. J. M. Wooldridge, ‘Cluster-Sample Methods in Applied Econometrics’, American Economic Review, 2003, 93, 133–8.

    41. I. Krinsky and L. Robb, ‘On Approximating the Statistical Properties of Elasticities’, Review of Economics and Statistics, 1986, 68, 715–19.

    42. K. Murphy and R. Topel, ‘Estimation and Inference in Two Step Econometrics Models’, Journal of Business and Economic Statistics, 2002, 20, 88–97.


    Part 8: Testing

    43. J. Hausman, ‘Specification Tests in Econometrics’, Econometrica, 1978, 46, 1251–71.

    44. T. Breusch and A. Pagan, ‘The LM Test and its Applications to Model Specification in Econometrics’, Review of Economic Studies, 1980, 47, 239–54.

    45. R. Davidson and J. MacKinnon, ‘Several Tests for Model Specification in the Presence of Alternative Hypotheses’, Econometrica, 1981, 49, 781–93.

    46. D. Dickey and W. Fuller, ‘Distribution of the Estimators for Autoregressive Time Series with a Unit Root’, Journal of the American Statistical Association, 1979, 74, 427–31.

    47. R. Davidson and J. MacKinnon, ‘Alternative Tests of Independence Between Stochastic Regressors and Disturbances’, Econometrica, 1985, 41, 193, 733–50.

    48. J. Sargan, ‘The Estimation of Economic Relationships Using Instrumental Variables’, Econometrica, 1958, 26, 393–415.

    Part 9: Fields—Industrial Organization

    49. J. Asker, ‘A Study of the Internal Organization of a Bidding Cartel’, American Economic Review, 2010, 100, 724–62.

    50. S. Berry, J. Levinsohn, and A. Pakes, ‘Automobile Prices in Market Equilibrium’, Econometrica, 1995, 63, 841–90.

    Part 10: Fields—Health Econometrics

    51. Michael Grossman, ‘On the Concept of Health Capital and the Demand for Health’, Journal of Political Economy, 1972, 80, 2, 223–55.

    Part 11: Fields—Labour Economics

    52. J. Heckman, ‘Shadow Prices, Market Wages and Labor Supply’, Econometrica, 1974, 42, 679–94.

    Part 12: Fields—Production and Productivity

    53. D. Aigner, K. Lovell, and P. Schmidt, ‘Formulation and Estimation of Stochastic Frontier Production Models’, Journal of Econometrics, 1977, 6, 21–37.

    54. J. Jondrow, K. Lovell, I. Materov, and P. Schmidt, ‘On the Estimation of Technical Inefficiency in the Stochastic Frontier Production Model’, Journal of Econometrics, 1982, 19, 233–8.

    Part 13: Fields—Financial Econometrics

    55. E. Fama and J. MacBeth, ‘Risk, Return and Equilibrium: Empirical Tests’, Journal of Political Economy, 1973, 81, 3, 607–36.


    Part 14: Fields—Treatment, Evaluation, and Causal Inference

    56. R. LaLonde, ‘Evaluating the Econometric Evaluations of Training Programs with Experimental Data’, American Economic Review, 1986, 76, 604–20.

    57. J. Heckman, H. Ichimura, and P. Todd, ‘Matching as an Econometric Evaluation Estimator’, Review of Economic Studies, 1998, 65, 261–94.

    58. J. Terza and D. Kenkel, ‘The Effect of Physician Advice on Alcohol Consumption: Count Regression with an Endogenous Treatment Effect’, Journal of Applied Econometrics, 2001, 16, 163–84.

    59. P. Holland, ‘Statistics and Causal Inference’, Journal of the American Statistical Association, 1986, 81, 945–70.

    60. J. Angrist, G. Imbens, and D. Rubin, ‘Identification of Causal Effects Using Instrumental Variables’, Journal of the American Statistical Association, 1996, 91, 444–69.

    Part 15: Panel Data

    61. P. Balestra and M. Nerlove, ‘Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas’, Econometrica, 1966, 34, 585–612.

    62. Y. Mundlak, ‘On the Pooling of Time Series and Cross Sectional Data’, Econometrica, 1978, 56, 69–86.

    63. J. Hausman and W. Taylor, ‘Panel Data and Unobservable Individual Effects’, Econometrica, 1981, 49, 1377–98.

    64. M. Arellano and S. Bond, ‘Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations’, Review of Economic Studies, 1991, 58, 277–97.

    65. S. Nickell, ‘Biases in Dynamic Models with Fixed Effects’, Econometrica, 1981, 49, 1399–416.

    66. I. Fernandez Val, ‘Fixed Effects Estimation of Structural Parameters and Marginal Effects in Panel Probit Models’, Journal of Econometrics, 2009, 150, 71–85.

    67. D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin, ‘Testing the Null Hypothesis of Stationarity Against the Alternative of a Unit Root’, Journal of Econometrics, 1992, 54, 159–78.

    68. J. M. Wooldridge, ‘Simple Solutions to the Initial Conditions Problem Indynamic, Nonlinear Panel-Data Models With Unobserved Heterogeneity’, Journal of Applied Econometrics, 2005, 20, 39–54.

    69. J. Hausman, B. Hall, and Z. Griliches, ‘Economic Models for Count Data with an Application to the Patents—R&D Relationship’, Econometrica, 1984, 52, 909–38.

    70. D. Revelt and K. Train, ‘Mixed Logit with Repeated Choices: Households’ Choices of Appliance Efficiency Level’, Review of Economics and Statistics, 1998, 80, 647–57.

    Part 16: Spatial Econometrics

    71. L. Anselin, ‘Spatial Econometrics’, in B. Baltagi (ed.), A Companion to Theoretical Econometrics (Blackwell, 2001), pp. 310–30.

    72. K. Bell and N. Bockstael, ‘Applying the Generalized Method of Moments Approach to Spatial Problems Involving Micro-Level Data’, Review of Economics and Statistics, 2000, 82, 72–82.

    Part 17: Tools

    73. C. Ai and E. C. Norton, ‘Interaction Terms in Logit and Probit Models’, Economics Letters, 2003, 80, 123–9.

    74. R. Olsen, ‘A Note on the Uniqueness of the Maximum Likelihood Estimator of the Tobit Model’, Econometrica, 1978, 46, 1211–15.

    75. Rolf Sundberg, ‘Maximum Likelihood Theory for Incomplete Data from an Exponential Family’, Scandinavian Journal of Statistics, 1974, 1, 2, 49–58.

    76. J. Butler and R. Moffitt, ‘A Computationally Efficient Quadrature Procedure for the One Factor Multinomial Probit Model’, Econometrica, 50, 1982, pp. 761-764.

    77. J. Geweke, M. Keane, and D. Runkle, ‘Alternative Computational Approaches to Statistical Inference in the Multinomial Probit Model’, Review of Economics and Statistics, 1994, 76, 609–32.

    78. T. Bago d’Uva and A. Jones, ‘Health Care Utilization in Europe: New Evidence from the ECHP’, Journal of Health Economics, 2009, 28, 2, 265–79.

    79. Alan E. Gelfand and Adrian F. M. Smith, ‘Sampling-Based Approaches to Calculating Marginal Densities’, Journal of the American Statistical Association, 1990, 85, 398–409.