Time Series Econometrics: 1st Edition (Hardback) book cover

Time Series Econometrics

1st Edition

Edited by Terence Mills


1,872 pages

Purchasing Options:$ = USD
Hardback: 9780415718271
pub: 2015-03-27

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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 from its Critical Concepts in Economics series. With a comprehensive introduction, newly written by the editor, which places the assembled materials in their historical and intellectual context, Time Series Econometrics is an essential work of reference. This fully indexed collection will be particularly useful as an essential 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.

Table of Contents

Volume I: Laying the Foundations

Part 1: Correlation and Detrending

1. Reginald H. Hooker, ‘Correlation of the marriage-rate with trade’, Journal of the Royal Statistical Society, 1901, 64(3) (September, 1901), 485–92.

2. Reginald H. Hooker, ‘On the correlation of successive observations’, Journal of the Royal Statistical Society, 68(4) (December, 1905), 696–703.

3. Student (W. S. Gosset), ‘The elimination of spurious correlation due to position in time and space’, Biometrika, 10(1) (April, 1914), 179–80.

4. Warren M. Persons, ‘On the variate difference correlation method and curve fitting’, Journal of the American Statistical Association, 15(118) (June, 1917), 602–42.

5. G. Udny Yule, ‘On the time-correlation problem, with especial reference to the variate-difference correlation method’, Journal of the Royal Statistical Society, 84(4) (July, 1921), 497–537.

Part 2: Spurious Correlations, Random Shocks, and Induced Cycles

6. G. Udny Yule, ‘Why do we sometimes get nonsense-correlations between time-series? A study in sampling and the nature of time series’, Journal of the Royal Statistical Society, 89 (1) (January, 1926), 1–63.

7. Eugen Slutzky, ‘The summation of random causes as the source of cyclic processes’, Econometrica, 5(2) (April, 1937), 105–46.

8. Holbrook Working, ‘A random difference series for use in the analysis of time series’, Journal of the American Statistical Association, 29(185) (March, 1934), 11–24.

Part 3: Modelling Stationary Time Series

9. G. Udny Yule, ‘On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers’, Philosophical Transactions of the Royal Society of London, Series A, 226 (April 1927), 267–98.

10. Maurice G. Kendall, ‘On autoregressive time series’, Biometrika, 33(2) (August, 1944), 105–22.

11. James Durbin, ‘The fitting of time series models’, Review of the International Institute of Statistics, 28(3) (1960), 233–44.

Part 4: Developments in Estimation and Inference

12. Maurice S. Bartlett, ‘On the theoretical specification and sampling properties of autocorrelated time series’, Journal of the Royal Statistical Society, Series B, Supplement, 8 (1946), 27–41.

13. Richard J. Anderson, ‘Distribution of the serial correlation coefficient’, Annals of Mathematical Statistics, 13(1) (March, 1942), 1–13.

14. George S. Watson and James Durbin, ‘Exact tests of serial correlation using noncircular statistics’, Annals of Mathematical Statistics, 22(3) (September, 1951), 446–51.

15. Henry B. Mann and Abraham Wald, ‘On the statistical treatment of linear stochastic difference equations’, Econometrica, 11(3/4) (July–October, 1943), 173–220.

16. James Durbin, ‘Efficient estimation of parameters in moving-average models’, Biometrika, 46(3/4) (December, 1959), 306–16.

17. A. M. Walker, ‘Large-sample estimation of parameters for autoregressive processes with moving-average residuals’, Biometrika, 49(1/2) (June, 1962), 117–31.

Volume II: A Maturing Discipline

Part 1: Modelling Relationships Between Time Series

18. Irving Fisher, ‘Our unstable dollar and the so-called business cycle’, Journal of the American Statistical Society, 20(150) (June, 1925), 179–202.

19. Bradford B. Smith, ‘Combining the advantages of first-difference and deviation-from-trend methods of correlating time series’, Journal of the American Statistical Association, 21(153) (March, 1926), 55–9.

20. Ragnar Frisch and Frederick V. Waugh, ‘Partial time regressions as compared with individual trends’, Econometrica, 1(4) (October, 1933), 387–401

21. Guy H. Orcutt and S. F. James, ‘Testing the significance of correlation between time series’, Biometrika, 35(3/4) (December, 1948), 397–413.

22. Donald Cochrane and Guy H. Orcutt, ‘Application of least squares regression to relationships containing autocorrelated error terms’, Journal of the American Statistical Association, 44(245) (March, 1949), 32–61.

23. James Durbin, ‘Estimation of parameters in time series regression models’, Journal of the Royal Statistical Society, Series B, 22(1)(1960), 139–53.

Part 2: Testing Time-Series Regression Models

24. James Durbin and George S. Watson, ‘Testing for serial correlation in least squares regression: I’, Biometrika, 37(3/4) (December, 1950), 409–28.

25. James Durbin and George S. Watson, ‘Testing for serial correlation in least squares regression: II’, Biometrika, 38(1/2) (June, 1951), 159–77.

26. James Durbin, ‘Testing for serial correlation in least-squares regression when some of the regressors are lagged dependent variables’, Econometrica, 38(3) (May, 1970), 410–21.

27. George E. P. Box and David A. Pierce, ‘Distribution of the residual autocorrelations in autoregressive-moving average time series models’, Journal of the American Statistical Association, 65(332) (December, 1970), 1509–26.

28. Walter Krāmer, Werner Ploberger, and Raimund Alt, ‘Testing for structural change in dynamic models’, Econometrica, 56(6) (November, 1988), 1355–69.

29. Trevor S. Breusch, ‘Testing for autocorrelation in dynamic linear models’, Australian Economic Papers, 17(31) (December, 1978), 334–55.

30. Leslie G. Godfrey, ‘Testing for higher order serial correlation in regression equations when the regressors include lagged dependent variables’, Econometrica, 46(6) (November, 1978), 1303–10.

31. Whitney K. Newey and Kenneth D. West, ‘A simple positive semidefinite, heteroskedsticity consistent covariance matrix’, Econometrica, 55(3) (May, 1987), 703–8.

Part 3: Causality

32. Clive W. J. Granger, ‘Investigating causal relations by econometric methods and cross-spectral methods’, Econometrica, 37(3) (August, 1969), 424–38.

33. Christopher A. Sims, ‘Money, income and causality’, American Economic Review, 62(4) (September, 1972), 540–52.

34. Richard Ashley, Clive W. J. Granger, and Richard W. Schmalensee, ‘Advertising and aggregate consumption: an analysis of causality’, Econometrica, 48(5) (June, 1980), 1149–67.

35. Helmut Lütkepohl, ‘Non-causality due to omitted variables’, Journal of Econometrics, 19(2–3) (August, 1982), 367–78.

36. John Geweke, ‘Measurement of linear dependence and feedback between time series’, Journal of the American Statistical Association, 77(378) (June, 1982), 304–10.

37. Charles R. Nelson and G. William Schwert, ‘Tests for predictive relationships between time series variables: a Monte Carlo investigation’, Journal of the American Statistical Association, 77(377) (March, 1982), 11–18.

38. Hiro Y. Toda and Peter C. B. Phillips, ‘Vector autoregressions and causality’, Econometrica, 61(6) (November, 1993), 1367–93.

Volume III: Single-Equation Modelling

Part 1: Dynamic Specification

39. David F. Hendry and Grayham E. Mizon, ‘Serial correlation as a convenient simplification, not a nuisance: a comment on a study of the demand for money by the Bank of England’, Economic Journal,88(351) (September, 1978), 549–63.

40. James E. H. Davidson, David F. Hendry, Frank Srba, and Stephen Yeo, ‘Econometric modeling of the aggregate time-series relationship between consumer expenditure and income in the United Kingdom’, Economic Journal, 88(352) (December, 1978), 661–92.

41. J. Denis Sargan, ‘Some tests of dynamic specification for a single equation’, Econometrica, 48(4) (May, 1980), 879–97.

42. David F. Hendry and Jean-François Richard, ‘On the formulation of empirical models in dynamic econometrics’, Journal of Econometrics, 20(1) (October, 1982), 3–33.

Part 2: Unit Roots, Time Trends, and Breaks

43. David A. Dickey and Wayne A. Fuller, ‘Distribution of the estimators for autoregressive time series with a unit root’, Journal of the American Statistical Association, 74(366) (June, 1979), 427–31.

44. Said E. Said and David A. Dickey, ‘Testing for unit roots in autoregressive-moving average models of unknown order’, Biometrika, 71(3) (December, 1984), 599–607.

45. Peter C. B. Phillips and Pierre Perron, ‘Testing for a unit root in time series regression’, Biometrika, 75(2) (June, 1988), 335–46.

46. Charles R. Nelson and Charles I. Plosser, ‘Trends and random walks in macroeconomic time series’, Journal of Monetary Economics, 10(2) (September, 1982), 139–62.

47. Charles R. Nelson and Heejoon Kang, ‘Pitfalls in the use of time as an explanatory variable in regression’, Journal of Business and Economic Statistics, 2(1) (January, 1984), 73–82.

48. Steven N. Durlauf and Peter C. B. Phillips, ‘Trends versus random walks in time series analysis’, Econometrica, 56(6) (November, 1988), 1333–54.

49. Pierre Perron, ‘The Great Crash, the oil price shock and the unit root hypothesis’, Econometrica, 57(6) (November, 1989), 1361–401.

50. Denis Kwiatkowski, Peter C. B. Phillips, Peter Schmidt, and Yongcheol Shin, ‘Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?’, Journal of Econometrics,54(1–3) (October–December, 1992), 159–78.

51. David DeJong, John Nankervis, N. Eugene Savin, and Charles Whiteman, ‘The power problems of unit root tests for time series with autoregressive errors’, Journal of Econometrics, 53(1–3) (July–September, 1992), 323–43.

52. Graham Elliott, Thomas J. Rothenberg, and James H. Stock, ‘Efficient tests for an autoregressive unit root’, Econometrica, 64(4) (July, 1996), 813–36.

53. Graham Elliott and James H. Stock, ‘Confidence intervals for autoregressive coefficients near one’, Journal of Econometrics, 103(1–2) (July, 2001), 155–81.

54. Serena Ng and Pierre Perron, ‘Lag length selection and the construction of unit root tests with good size and power’, Econometrica, 69(6) (November, 2001), 1519–54.

55. David Harris, David I. Harvey, Stephen J. Leybourne, and A. M. Robert Taylor, ‘Testing for a unit root in the presence of a possible break in trend’, Econometric Theory, 25(6) (December, 2009), 1545–88.

56. Dukpa Kim and Pierre Perron, ‘Unit root tests allowing for a break in the trend function at an unknown time under both the null and alternative hypotheses’, Journal of Econometrics, 148(1) (January, 2009), 1–13.

Volume IV: Multiple-Equation Modelling

Part 1: Simultaneous Equations, VARs, and Panels

57. John Geweke, ‘Testing the exogeneity specification in the complete dynamic simultaneous equations model’, Journal of Econometrics,7(2) (June, 1978), 163–85.

58. Christopher A. Sims, ‘Macroeconomics and reality’, Econometrica, 48(1) (January, 1980), 1–48.

59. Helmut Lütkepohl, ‘Comparison of criteria for estimating the order of a vector autoregressive process’, Journal of Time Series Analysis, 6(1) (1985), 35–52.

60. Olivier J. Blanchard and Danny Quah, ‘Dynamic effects of aggregate demand and aggregate supply disturbances’, American Economic Review, 79(4) (September, 1989), 655–73.

61. M. Hashem Pesaran and Yongcheol Shin, ‘Generalized impulse response analysis in linear multivariate models’, Economics Letters, 58(1) (January, 1998), 17–29.

62. Andrew Levin, Chien-Fu Lin, and Chia-Shang James Chu, ‘Unit root tests in panel data: asymptotic and finite sample properties’, Journal of Econometrics, 108(1) (May, 2002), 1–25.

63. Kyung So Im, M. Hashem Pesaran and Yongcheol Shin, ‘Testing for unit roots in heterogeneous panels’, Journal of Econometrics, 115(1) (August, 2003), 53–74.

Part 2: Spurious Regression, Cointegration, Common Trends, and VECMs

64. George E. P. Box and Paul Newbold, ‘Some comments on a paper of Coen, Gomme and Kendall’, Journal of the Royal Statistical Society, Series A, 134(2) (1971), 229–40.

65. Clive W. J. Granger and Paul Newbold, ‘Spurious regressions in econometrics’, Journal of Econometrics, 2(2) (July, 1974), 111–20.

66. J. Denis Sargan and Alok Bhargava, ‘Testing for residuals from least-squares regression being generated by a random walk’, Econometrica, 51(1) (January, 1983), 153–74.

67. Peter C. B. Phillips, ‘Understanding spurious regressions in econometrics’, Journal of Econometrics, 33(3) (December, 1986), 311–40.

68. Robert F. Engle and Clive W. J. Granger, ‘Co-integration and error correction: representation, estimation and testing’, Econometrica, 55(2) (March, 1987), 251–76.

69. James H. Stock, ‘Asymptotic properties of least squares estimators of cointegrating vectors’, Econometrica, 55(5) (September, 1987), 1035–56.

70. James H. Stock and Mark W. Watson, ‘Testing for common trends’, Journal of the American Statistical Association, 83(401) (March, 1988), 1097–107.

71. Soren Johansen, ‘Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models’, Econometrica, 59(6) (November, 1991), 1551–80.

72. Peter C.B. Phillips, ‘Optimal inference in co-integrated systems’, Econometrica, 59(2) (March, 1991), 282–306.

73. Farshid Vahid and Robert F. Engle, ‘Common trends and common cycles’, Journal of Applied Econometrics, 8(4) (October/December, 1993), 341–60.

74. Pentti Saikkonen and Helmut Lütkepohl, ‘Testing for cointegrating rank of a VAR process with structural shifts’, Journal of Business and Economic Statistics, 18(4) (October, 2000), 451–64.

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