Econometric Forecasting

  • P. Geoffrey Allen
  • Robert Fildes
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 30)


Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. Theory, however, gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little attention to dynamic structure. In a fairly simple way, the vector autoregression (VAR) approach that first appeared in the 1980s resolved the problem by shifting emphasis towards dynamics and away from collecting many causal variables. The VAR approach also resolves the question of how to make long-term forecasts where the causal variables themselves must be forecast. When the analyst does not need to forecast causal variables or can use other sources, he or she can use a single equation with the same dynamic structure. Ordinary least squares is a perfectly adequate estimation method. Evidence supports estimating the initial equation in levels, whether the variables are stationary or not. We recommend a general-to-specific model-building strategy: start with a large number of lags in the initial estimation, although simplifying by reducing the number of lags pays off. Evidence on the value of further simplification is mixed. If there is no cointegration among variables, then error-correction models (ECMs) will do worse than equations in levels. But ECMs are only sometimes an improvement even when variables are cointegrated. Evidence is even less clear on whether or not to difference variables that are nonstationary on the basis of unit root tests. While some authors recommend applying a battery of misspecification tests, few econometricians use (or at least report using) more than the familiar Durbin-Watson test. Consequently, there is practically no evidence on whether model selection based on these tests will improve forecast performance. Limited evidence on the superiority of varying parameter models hints that tests for parameter constancy are likely to be the most important. Finally, econometric models do appear to be gaining over extrapolative or judgmental methods, even for short-term forecasts, though much more slowly than their proponents had hoped.


Econometric forecasting error correction model forecast comparisons specification testing vector autoregression 


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  1. Abadir, K. M., K. Hadri E. Tzavalis (1999), “The influence of VAR dimensions on estimator biases,” Econometrica, 67, 163–181.CrossRefGoogle Scholar
  2. Akgiray, V. (1989), “Conditional heteroscedasticity in time series of stock returns: Evidence and forecasts,” Journal of Business, 62, 55–80.CrossRefGoogle Scholar
  3. Alexander, J. C. Jr. (1995), “Refining the degree of earnings surprise: A comparison of statistical and analysts’ forecasts,” Financial Review, 30, 469–506.CrossRefGoogle Scholar
  4. Allen, P. G. (1994), “Economic forecasting in agriculture,” International Journal of Forecasting, 10, 81–135.CrossRefGoogle Scholar
  5. Andrews, D. W. K. (1993), “Tests for parameter instability and structural change with unknown change point,” Econometrica, 61, 821–856.CrossRefGoogle Scholar
  6. Andrews, D. W. K. W. Ploberger (1994), “Optimal tests when a nuisance parameter is present only under the alternative,” Econometrica, 62, 1383–1414.CrossRefGoogle Scholar
  7. Armstrong, J. S. (1985), Long-Range Forecasting From Crystal Ball to Computer,2nd edition. New York: John Wiley Sons. Full text at
  8. Armstrong, J. S. M. C. Grohman (1972), “A comparative study of methods for long-range market forecasting,” Management Science, 19, 211–221. Full text at Google Scholar
  9. Artis, M. J. W. Zhang (1990), “BVAR forecasts for the G-7,” International Journal of Forecasting, 6, 349–362.CrossRefGoogle Scholar
  10. Ashley, R. (1983), “On the usefulness of macroeconomic forecasts as inputs to forecasting models,” Journal of Forecasting, 2, 211–223.CrossRefGoogle Scholar
  11. Ashley, R. (1988), “On the relative worth of recent macroeconomic forecasts,” International Journal of Forecasting, 4, 363–376.CrossRefGoogle Scholar
  12. Babula, R. A. (1988), “Contemporaneous correlation and modeling Canada’s imports of U.S. crops,” Journal of Agricultural Economics Research, 41, 33–38.Google Scholar
  13. Banerjee, A., J. J. Dolado, J. W. Galbraith, D. F. Hendry (1993), Co-integration, Error-correction, and the Econometric Analysis of Non-stationary Data. Oxford: Oxford University Press.Google Scholar
  14. Barrett, C. B. (1997), “Heteroscedastic price forecasting for food security management in developing countries,” Oxford Development Studies, 25, 225–236.Google Scholar
  15. Batchelor, R. P. Dua (1993), “Survey vs ARCH measures of inflation uncertainty,” Oxford Bulletin of Economics Statistics, 55, 341–353.CrossRefGoogle Scholar
  16. Belsley, D. A. (1988), “Modelling and forecast reliability,” International Journal of Forecasting, 4, 427–447.CrossRefGoogle Scholar
  17. Bera, A. M. L. Higgins (1997), “ARCH and bilinearity as competing models for nonlinear dependence,” Journal of Business Economic Statistics, 15, 43–50.Google Scholar
  18. Bessler, D. A. R. A. Babula, (1987), “Forecasting wheat exports: Do exchange rates matter?” Journal of Business and Economic Statistics, 5, 397–406.Google Scholar
  19. Bessler, D. A. T. Covey (1991), “Cointegration: Some results on U.S. cattle prices,” Journal of Futures Markets, 11, 461–474.CrossRefGoogle Scholar
  20. Bessler, D. A. S. W. Fuller (1993), “Cointegration between U.S. wheat markets,” Journal of Regional Science, 33, 481–501.CrossRefGoogle Scholar
  21. Bewley, R. M. Yang (1998), “On the size and power of system tests for cointegration,” Review of Economics and Statistics, 80, 675–679.CrossRefGoogle Scholar
  22. Bottomley, P. R. Fildes (1998), “The role of prices in models of innovation diffusion,” Journal of Forecasting, 17, 539–555.CrossRefGoogle Scholar
  23. Brailsford, T.J. R.W. Faff (1996), “An evaluation of volatility forecasting techniques,” Journal of Banking Finance, 20, 419–438.CrossRefGoogle Scholar
  24. Breusch, T. S. (1978), “Testing for autocorrelation in dynamic linear models,” Australian Economic Papers, 17, 334–355.CrossRefGoogle Scholar
  25. Breusch, T. S. A. R. Pagan (1979), “A simple test for heteroskedasticity and random coefficient variation,” Econometrica, 47, 1287–1294.CrossRefGoogle Scholar
  26. Burgess, D. F. (1975), “Duality theory and pitfalls in the specification of technologies,” Journal of Econometrics, 3, 105–121.CrossRefGoogle Scholar
  27. Byron, R. P. O. Ashenfelter (1995), “Predicting the quality of an unborn grange,” Economic Record, 71, 40–53.CrossRefGoogle Scholar
  28. Campa, J.M. P.H.K. Chang (1995), “The Forecasting Ability of Correlations Implied in Foreign Exchange Options,” Columbia University, PaineWebber Working Paper Series in Money, Economics, and Finance: PW/95/26, [19 pages]. Order from PaineWebber Series, 6N Uris Hall, Columbia University, New York, NY 10027 USA or Google Scholar
  29. Canova, F. B. E. Hansen (1995), “Are seasonal patterns constant over time? A test for seasonal stability,” Journal of Business and Economic Statistics, 13, 237–252.Google Scholar
  30. Challen, D. W. A. J. Hagger (1983), Macroeconomic Systems: Construction, Validation and Applications. New York: St. Martin’s Press.Google Scholar
  31. Chen, C. L. Liu (1993), “Joint estimation of model parameters and outlier effects in time series,” Journal of the American Statistical Association, 88, 284–297.Google Scholar
  32. Cheung, Y. M.D. Chinn (1997), “Further investigation of the uncertain unit root in GNP,” Journal of Business and Economic Statistics, 15, 68–73.Google Scholar
  33. Chow, G. C. (1960), “Tests of equality between sets of coefficients in two linear regressions,” Econometrica, 28, 591–605.CrossRefGoogle Scholar
  34. Christou, C., P. A. V. B. Swamy G. S. Tavlas (1996), “Modelling optimal strategies for the allocation of wealth in multicurrency investments,” International Journal of Forecasting, 12, 483–493.CrossRefGoogle Scholar
  35. Clements, M. P. D. F. Hendry (1995), “Forecasting in cointegrated systems,” Journal of Applied Econometrics, 10, 127–146.CrossRefGoogle Scholar
  36. Clements, M. P. D. F. Hendry (1996), “Intercept corrections and structural change,” Journal of Applied Econometrics, 11, 475–494.CrossRefGoogle Scholar
  37. Clements, M. P. D. F. Hendry (1997), “An empirical study of seasonal unit roots in forecasting,” International Journal of Forecasting, 13, 341–355.CrossRefGoogle Scholar
  38. Clements, M. P. D. F. Hendry (1999), Forecasting Non-stationary Economic Time Series: The Zeuthen Lectures on Economic Forecasting. Cambridge, MA: MIT Press.Google Scholar
  39. Collins, D. W. (1976), “Predicting earnings with sub-entity data: Some further evidence,” Journal of Accounting Research, 14, 163–177.CrossRefGoogle Scholar
  40. Conway, R. K., J. Hrubovcak M. LeBlanc, 1990, “A forecast evaluation of capital investment in agriculture,” International Journal of Forecasting, 6, 509–519.CrossRefGoogle Scholar
  41. Cooley, T. F. E. C. Prescott (1976), “Estimation in the presence of stochastic parameter variation,” Econometrica, 44, 167–184.CrossRefGoogle Scholar
  42. D’Agostino, R. B., A. Belanger R. B. D’Agostino Jr. (1990), “A suggestion for using powerful and informative tests of normality,” The American Statistician, 44, 316–321.Google Scholar
  43. D’Agostino, R. B. M. A. Stephens (1986), Goodness-of-Fit Techniques. New York: Marcel Dekker.Google Scholar
  44. Dangerfield, B. J. S. Morris (1988), “An empirical evaluation of top-down and bottom-up forecasting strategies,” Preceedings of the 1988 meeting of Western Decision Sciences Institute, 322–324.Google Scholar
  45. Dangerfield, B. J. J. S. Morris (1992), “Top-down or bottom-up: Aggregate versus disaggregate extrapolation,” International Journal of Forecasting, 8, 233–241.CrossRefGoogle Scholar
  46. Davidson, J. E. H., D. F. Hendry, F. Srba S. Yeo (1978), “Econometric modelling of the aggregate time-series relationship between consumers’ expenditure and income in the United Kingdom,” Economic Journal, 88, 661–692.CrossRefGoogle Scholar
  47. Davidson, R. J. G. MacKinnon (1993), Estimation and Inference in Econometrics. New York: Oxford University Press.Google Scholar
  48. Dhrymes, P. J. S. C. Peristiani (1988), “A comparison of the forecasting performance of WEFA and ARIMA time series methods,” International Journal of Forecasting, 4, 81–101.CrossRefGoogle Scholar
  49. Dickey, D. A. W. A. Fuller (1979), “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American Statistical Association, 74, 427–431Google Scholar
  50. Diebold, F.X. (1998), Elements of Forecasting. Cincinnati, Ohio: South-Western College Publishing.Google Scholar
  51. Dielman, T.E. R. Pfaffenberger (1982), “LA V (least absolute value) estimation in linear regression: A review,” TIMS Studies in the Management Sciences, 19, 31–52.Google Scholar
  52. Dielman, T. E. E. L. Rose (1994), “Forecasting in least absolute value regression with autocorrelated errors: A small-sample study,” International Journal of Forecasting, 10, 539–547.CrossRefGoogle Scholar
  53. Dixon, B. L. L. J. Martin (1982), “Forecasting U.S. pork production using a random coefficient model,” American Journal of Agricultural Economics, 64, 530–538.CrossRefGoogle Scholar
  54. Dolado, J. J., T. Jenkinson S. Sosvilla-Rivero (1990), “Cointegration and unit roots,” Journal of Economic Surveys, 4, 249–273.CrossRefGoogle Scholar
  55. Duo, P. D. J. Smyth (1995), “Forecasting U.S. home sales using BVAR models and survey data on households’ buying attitudes for homes,” Journal of Forecasting, 14, 217–227.CrossRefGoogle Scholar
  56. Dunn, D. W., W. H. Williams W. A. Spiney (1971), “Analysis and prediction of telephone demand in local geographic areas,” Bell Journal of Economics and Management Science, 2, 561–576.CrossRefGoogle Scholar
  57. Durbin, J. (1970), “Testing for serial correlation in least squares regression when some of the regressors are lagged dependent variables,” Econometrica, 38, 410–421.CrossRefGoogle Scholar
  58. Durbin, J. G. S. Watson (1950), “Testing for serial correlation in least squares regression I, ” Biometrika, 37, 409–428.Google Scholar
  59. Durbin, J. G. S. Watson (1951), “Testing for serial correlation in least squares regression II,” Biometrika, 38, 159–178.Google Scholar
  60. Efron, 13. (1990), “More efficient bootstrap computations,” American Statistician, 85, 79–89.Google Scholar
  61. Elliott, G., T. J. Rothenberg J.H. Stock (1996), “Efficient tests for an autoregressive unit root,” Econometrica, 64, 813–836.CrossRefGoogle Scholar
  62. Engle, R. F. (1982), “Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation,” Econometrica, 50, 987–1007.CrossRefGoogle Scholar
  63. Engle, R. F., S. J. Brown G. Stem (1988), “A comparison of adaptive structural forecasting methods for electricity sales,” Journal of Forecasting, 7, 149–172.CrossRefGoogle Scholar
  64. Engle, R. F. C. W. J. Granger (1987), “Co-integration and error correction: Representation, estimation, and testing,” Econometrica, 55, 251–276.CrossRefGoogle Scholar
  65. Engle, R. F., C. W. J. Granger J. J. Hallman (1989), “Merging short-and long-run forecasts,” Journal of Econometrics, 40, 45–62.CrossRefGoogle Scholar
  66. Engle, R. F., C. W. J. Granger, S. Hylleberg H. Lee (1993), “Seasonal cointegration: The Japanese consumption function,” Journal of Econometrics, 55, 275–298.CrossRefGoogle Scholar
  67. Engle, R. F., D. F. Hendry D. Trumble (1985), “Small-sample properties of ARCH estimators and tests,” Canadian Journal of Economics, 18, 66–93.CrossRefGoogle Scholar
  68. Engle, R. F. B. S. Yoo (1987), “Forecasting and testing in co-integrated systems,” Journal of Econometrics, 35, 143–159.CrossRefGoogle Scholar
  69. Fair, R. C. R. J. Shiller, (1990), “Comparing information in forecasts from econometric models,” American Economic Review, 80, 375–389.Google Scholar
  70. Fanchon, P. J. Wendell (1992), “Estimating VAR models under non-stationarity and cointegration: Alternative approaches to forecasting cattle prices,” Applied Economics, 24, 207–217.CrossRefGoogle Scholar
  71. Figlewski, S. (1994), Forecasting Volatility Using Historical Data. New York University Salomon Brothers Working Paper: S-94–13, 29. [29 pages] Order from Publications Department, New York University Salomon Center, 44 West 4th Street, Suite 9–160, New York, New York 10012–0021.Google Scholar
  72. Fildes, R. (1985), “Quantitative forecasting—the state of the art: Econometric models,” Journal of the Operational Research Society, 36, 549–580.Google Scholar
  73. Fildes, R. S. Makridakis (1995), “The impact of empirical accuracy studies on time series analysis and forecasting,” International Statistical Review, 63, 289–308.CrossRefGoogle Scholar
  74. Fildes, R. H. Stekler (2001), “The state of macroeconomic forecasting,” Journal of Macroeconomics (forthcoming) Google Scholar
  75. Foekens, E.W., P.S.H. Leeflang D.R. Wittink (1994), “A comparison and an exploration of the forecasting accuracy of a loglinear model at different levels of aggregation,” International Journal of Forecasting, 10, 245–261.CrossRefGoogle Scholar
  76. Frennberg, P. B. Hansson (1995), “An evaluation of alternative models for predicting stock volatility: Evidence from a small stock market,” Journal of International Financial Markets, Institutions Money, 5, 117–134.Google Scholar
  77. Funke, M. (1990), “Assessing the forecasting accuracy of monthly vector autoregressive models: The case of five OECD Countries,” International Journal of Forecasting, 6, 363–378.CrossRefGoogle Scholar
  78. Garcia-Ferrer, A., R. A. Highfield, F. Palm A. Zellner (1987), “Macroeconomic forecasting using pooled international data,” Journal of Business Economic Statistics, 5, 53–67.Google Scholar
  79. Gilbert, P. D. (1995), “Combining VAR estimation and state space model reduction for simple good predictions,” Journal of Forecasting, 14, 229–250.CrossRefGoogle Scholar
  80. Glaser, D. (1954), “A reconsideration of some parole prediction factors,” American Sociological Review, 19, 335–340.CrossRefGoogle Scholar
  81. Godfrey, L.G. (1978), “Testing against general autoregressive and moving average processes when the regressors include lagged dependent variables,” Econometrica, 46, 1293–1302.CrossRefGoogle Scholar
  82. Godfrey, L. G. (1988), Misspecification Tests in Econometrics. Cambridge: Cambridge University Press.Google Scholar
  83. Gordon, D. V. W. A. Kerr (1997), “Was the Babson prize deserved? An enquiry into an early forecasting model,” Economic Modelling, 14, 417–433.CrossRefGoogle Scholar
  84. Granger, C. W. J. P. Newbold (1974), “Spurious regressions in econometrics,” Journal of Econometrics, 2, 111–120.CrossRefGoogle Scholar
  85. Haavelmo, T. (1944), “The probability approach in econometrics,” Econometrica, 12, ( Supplement, July, 1944 ), 1–115.Google Scholar
  86. Hafer, R. W R. G. Sheehan (1989), “The sensitivity of VAR forecasts to alternative lag structures,” International Journal of Forecasting, 5, 399–408.CrossRefGoogle Scholar
  87. Hall, A. D., H. M. Anderson C. W. J. Granger (1992), “A cointegration analysis of Treasury bill yields,” Review of Economics and Statistics, 74, 116–126.CrossRefGoogle Scholar
  88. Hannan, E. J. R. D. Terrell (1966), “Testing for serial correlation after least squares regression,” Econometrica, 34, 646–660.CrossRefGoogle Scholar
  89. Harris, K. S. R. M. Leuthold (1985), “A comparison of alternative forecasting techniques for livestock prices: A case study,” North Central Journal of Agricultural Economics, 7, 40–50.CrossRefGoogle Scholar
  90. Hendry, D. F. (1979), “ Predictive failure and econometric modelling in macroeconomics: The transactions demand for money,” Ch. 9, pp. 217–242 in P. Ormerod, ed., Economic Modelling. London: Heinemann.Google Scholar
  91. Hendry, D. F. (1980), “Econometrics-alchemy or science,” Economica, 47, 387–406.CrossRefGoogle Scholar
  92. Hendry, D. F. (1997), “A theory of co-breaking,” Paper presented at the 17th International Symposium of Forecasting, Barbados, W. I.Google Scholar
  93. Hendry, D. F., A. R. Pagan, J. D. Sargan (1984), “Dynamic specification,” Ch. 18 in Z. Griliches M.D. Intriligator (eds.), Handbook of Econometrics, Vol. 2. Amsterdam: North Holland.Google Scholar
  94. Hendry, D. F. J. F. Richard (1982), “On the formulation of empirical models in dynamic econometrics,” Journal of Econometrics, 20, 3–33.CrossRefGoogle Scholar
  95. Hildreth, C. J. P. Houck (1968), “Some estimators for a linear model with random coefficients,” Journal of the American Statistical Association, 63, 584–595.CrossRefGoogle Scholar
  96. Hillmer, S. (1984), “Monitoring and adjusting forecasts in the presence of additive outliers,” Journal of Forecasting, 3, 205–215.CrossRefGoogle Scholar
  97. Hoffman, D.L. R. H. Rasche (1996), “Assessing forecast performance in a cointegrated system,” Journal of Applied Econometrics, 11, 495–517.CrossRefGoogle Scholar
  98. Holden, K. A. Broomhead (1990), “An examination of vector autoregressive forecasts for the U.K. economy,” International Journal of Forecasting, 6, 11–23.CrossRefGoogle Scholar
  99. Hopkins, J. A. Jr. (1927), “Forecasting cattle prices,” Journal of Farm Economics, 9, 433–446.CrossRefGoogle Scholar
  100. Hsiao, C. (1979), “Autoregressive modeling of Canadian money and income data,” Journal of the American Statistical Association, 74, 553–60.CrossRefGoogle Scholar
  101. Hylleberg, S., R. F. Engle, C. W. J. Granger B. S. Yoo (1990), “Seasonal integration and cointegration,” Journal of Econometrics, 44, 215–238.CrossRefGoogle Scholar
  102. Hylleberg, S., C. Jorgensen N. K. Sorensen (1993), “Seasonality in macroeconomic time series,” Empirical Economics, 18, 321–335.CrossRefGoogle Scholar
  103. Inder, B. A. (1984), “Finite-sample power of tests for autocorrelation in models containing lagged dependent variables,” Economics Letters, 14, 179–185CrossRefGoogle Scholar
  104. Jarque, C.M. A.K. Bera (1980), Efficient tests for normality, heteroskedasticity and serial independence of regression residuals,“ Economics Letters, 6, 255–259.CrossRefGoogle Scholar
  105. Johansen, S. (1988), “Statistical analysis of cointegrating vectors,” Journal of Economic Dynamics and Control, 12, 231–254.CrossRefGoogle Scholar
  106. Johansen, S. K. Juselius (1990), “Maximum likelihood estimation and inference on cointegration-with applications to the demand for money,” Oxford Bulletin of Economics and Statistics, 52, 169–210.CrossRefGoogle Scholar
  107. Joutz, F. L., G. S. Maddala, R.P. Trost (1995), “An integrated Bayesian vector autoregression and error correction model for forecasting electricity consumption and prices,” Journal of Forecasting, 14, 287–310.CrossRefGoogle Scholar
  108. Judge, G. G., R. C. Hill, W E. Griffiths, H. Lütkepohl T. C. Lee (1985), The Theory and Practice of Econometrics. (2nd edition) New York: John Wiley Sons.Google Scholar
  109. Just, R. E. (1993), “Discovering production and supply relationships: present status and future opportunities,” Review of Marketing and Agricultural Economics, 61, 11–40.Google Scholar
  110. Kaylen, M. S. (1988), “Vector autoregression forecasting models: Recent developments applied to the U.S. hog market,” American Journal of Agricultural Economics, 70, 701–712.CrossRefGoogle Scholar
  111. Kennedy, P. (1992), A Guide to Econometrics. Cambridge, MA: The MIT Press.Google Scholar
  112. Kenward, L. R. (1976), “Forecasting quarterly business expenditures on non-residential construction in Canada: An assessment of alternative models,” Canadian Journal of Economics, 9, 517–529.CrossRefGoogle Scholar
  113. King, M. L. (1987), “Testing for autocorrelation in linear regression models: A survey,” Ch. 3 in M. King D. Giles (eds.), Specification Analysis in the Linear Model. London: Routledge and Kegan Paul.Google Scholar
  114. Kinney, W. R. Jr. (1971), “Predicting earnings: Entity vs. sub-entity data,” Journal of Accounting Research, 9, 127–136.CrossRefGoogle Scholar
  115. Kiviet, J. F. (1986), “On the rigour of some misspecification tests for modeling dynamic relationships,” Review of Economic Studies, 53, 241–261.CrossRefGoogle Scholar
  116. Kling, J. L. D. A. Bessler (1985), “A comparison of multivariate forecasting procedures for economic time series,” International Journal of Forecasting, 1, 5–24.CrossRefGoogle Scholar
  117. Koenker, R. (1988), “Asymptotic theory and econometric practice,” Journal of Applied Econometrics, 3, 139–147.CrossRefGoogle Scholar
  118. Kramer, W., H. Sonnberger, J. Maurer P. Havlik (1985), “Diagnostic checking in practice,” Review of Economics and Statistics, 67, 118–123.CrossRefGoogle Scholar
  119. Learner, E.E. (1983), “Let’s take the con out of econometrics,” American Economic Review, 73, 31–43.Google Scholar
  120. Ledolter, J. (1989), “The effect of additive outliers on the forecasts from ARIMA models,” International Journal of Forecasting, 5, 231–240.CrossRefGoogle Scholar
  121. Lin, J. L. R S. Tsay (1996), “Co-integration constraint and forecasting: An empirical examination,” Journal of Applied Econometrics, 11, 519–538.CrossRefGoogle Scholar
  122. Liu, T. R., M. E. Gerlow S. H. Irwin (1994), The performance of alternative VAR models in forecasting exchange rates,“ International Journal of Forecasting, 10, 419–433.CrossRefGoogle Scholar
  123. Liu, L. M. Lin (1991), “Forecasting residential consumption of natural gas using monthly and quarterly time series,” International Journal of Forecasting, 7, 3–16.CrossRefGoogle Scholar
  124. Lucas, R. E., Jr. (1976), “Econometric policy evaluation: a critique,” in K. Brunner A.H. Meltzer, The Phillips Curve and Labor Markets. Carnegie-Rochester Conference Series on Public Policy, 1, Amsterdam, North-Holland, 19–46.Google Scholar
  125. Lütkepohl, H. (1987), Forecasting Aggregated Vector ARMA Processes. Lecture Notes in Economics and Mathematical Systems series, No. 284. Berlin: Springer.Google Scholar
  126. Lütkepohl, H. (1991), Introduction to Multiple Time Series Analysis. New York: Springer-Verlag.Google Scholar
  127. MacKinnon, J. G. (1991), “Critical values for cointegration tests,” Ch. 13 in R. F. Engle C. W. J. Granger, eds., Long-run Economic Relationships: Readings in Cointegration. Oxford: Oxford University Press.Google Scholar
  128. Maddala, G. S. (1988), Introduction to Econometrics. New York: Macmillan.Google Scholar
  129. Mayer, T. (1975), “Selecting economic hypotheses by goodness of fit,” Economic Journal, 85, 877–883.CrossRefGoogle Scholar
  130. Mayer, T. (1980), “Economics as a hard science: Realistic goal or wishful thinking?” Economic Inquiry, 18, 165–178.CrossRefGoogle Scholar
  131. McCloskey, D. N. S. T. Ziliak (1996), “The standard error of regressions,” Journal of Economic Literature, 34, 97–114.Google Scholar
  132. McCurdy, T.H. T. Stengos (1991), “A comparison of risk-premium forecasts implied by parametric versus nonparametric conditional mean estimators,” Queen’s Institute for Economic Research Discussion Paper: No. 843, 25 pages. Order from Economics Department, Queen’s University, Kingston, Ontario K7L 3N6 Canada, or through Google Scholar
  133. McDonald, J. (1981), “Modelling demographic relationships: An analysis of forecast functions for Australian births, with discussion,” Journal of the American Statistical Association, 76, 782–801.CrossRefGoogle Scholar
  134. McNees, S. K. (1990), “The role of judgment in macroeconomic forecasting accuracy,” International Journal of Forecasting, 6, 287–299.CrossRefGoogle Scholar
  135. Mizon, G. E. (1995), “A simple message for autocorrelation correctors: Don’t,” Journal of Econometrics, 69, 267–288.CrossRefGoogle Scholar
  136. Mizon, G. E. D. F. Hendry (1980), “An empirical and Monte Carlo analysis of tests of dynamic specification,” Review of Economic Studies, 47, 21–45.CrossRefGoogle Scholar
  137. Mizon, G. E. J. F. Richard (1986), “The encompassing principle and its application to testing non-nested hypotheses,” Econometrica, 54, 657–678.CrossRefGoogle Scholar
  138. Moyer, R. C. (1977), “Forecasting financial failure: A re-examination,” Financial Management, 6, 11–17.CrossRefGoogle Scholar
  139. Murray, M. P. (1994), “A drunk and her dog: An illustration of cointegration and error correction,” American Statistician, 48, 37–39.Google Scholar
  140. Myers, R. J. S. D. Hanson (1993), “Pricing commodity options when the underlying futures price exhibits time-varying volatility,” American Journal of Agricultural Economics, 75, 121–130.CrossRefGoogle Scholar
  141. Naik, G. B. L. Dixon (1986), “A Monte-Carlo comparison of alternative estimators of autocorrelated simultaneous systems using a U.S. pork sector model as the true structure,” Western Journal of Agricultural Economics, 11, 134–145.Google Scholar
  142. Nelson, C. R. (1972), “The prediction performance of the FRB-MIT-PENN model of the U.S. economy,” American Economic Review, 62, 902–917.Google Scholar
  143. Neter, J., M. H. Kutner, C. J. Nachtsheim W. Wasserman (1996), Applied Linear Statistical Models. 4th ed. Chicago: Irwin.Google Scholar
  144. Noh, J., R.F. Engle A. Kane (1993), “A test of efficiency for the SP 500 index option market using variance forecasts,” University of California, San Diego Department of Economics Working Paper No. 93–32, 25 pages. Postscript file available at Google Scholar
  145. Osterwald-Lenum, M. (1992), “A note with quantiles of the asymptotic distribution of the maximum likelihood cointegration rank test statistics,” Oxford Bulletin of Economics and Statistics, 54, 461–472.CrossRefGoogle Scholar
  146. Park, T. (1990), “Forecast evaluation for multivariate time-series models: The U.S. cattle market,” Western Journal of Agricultural Economics, 15, 133–143.Google Scholar
  147. Peach, J. T. J. L. Webb (1983), “Randomly specified macroeconomic models: Some implications for model selection, Journal of Economic Issues, 17, 697–720.Google Scholar
  148. Quandt, R.E. (1960), “Tests of the hypothesis that a linear regression system obeys two separate regimes,” Journal of the American Statistical Association, 55, 324–330.CrossRefGoogle Scholar
  149. Riddington, G.L. (1993), “Time varying coefficient models and their forecasting performance,” Omega, 21, 573–583.CrossRefGoogle Scholar
  150. Rippe, R. D. M. Wilkinson (1974), “Forecasting accuracy of the McGraw-Hill anticipations data,” Journal of the American Statistical Association, 69, 849–858.CrossRefGoogle Scholar
  151. Rossana, R. J. J. J. Seater (1995), “Temporal aggregation and economic time series,” Journal of Business and Economic Statistics, 13, 441–451.Google Scholar
  152. Roy, S.K. P. N. Johnson (1974), Econometric Models of Cash and Futures Prices of Shell Eggs. USDA-ERS Technical Bulletin Number 1502, 32 pp.Google Scholar
  153. Sarantis, N. C. Stewart (1995), “Structural, VAR and BVAR models of exchange rate determination: A comparison of their forecasting performance,” Journal of Forecasting, 14, 201–215.CrossRefGoogle Scholar
  154. Sarle, C. F. (1925), “The forecasting of the price of hogs,” American Economic Review, 15, Number 3, Supplement Number 2, 1–22.Google Scholar
  155. Sarmiento, C. (1996), Comparing Two Modeling Approaches: An Example of Fed Beef Supply. MS Thesis, University of Massachusetts.Google Scholar
  156. Schwert, G. W. (1989), “Tests for unit roots: A Monte Carlo investigation,” Journal of Business and Economic Statistics, 7, 147–159.Google Scholar
  157. Shapiro, S. S. M. B. Wilk (1965), “An analysis of variance test for normality (complete samples),” Biometrika, 52, 591–611.Google Scholar
  158. Shoesmith, G. L. (1995), “Multiple cointegrating vectors, error correction, and forecasting with Litterman’s model,” International Journal of Forecasting, 11, 557–567.CrossRefGoogle Scholar
  159. Sims, C. A., J. H. Stock M. W. Watson (1990), “Inference in linear time series models with some unit roots,” Econometrica, 58, 113–144.CrossRefGoogle Scholar
  160. Soliman, M.A. (1971), “Econometric model of the turkey industry in the United States,” Canadian Journal of Agricultural Economics 19, 47–60.CrossRefGoogle Scholar
  161. Sowey, E.R. (1973), “A classified bibliography of Monte Carlo studies in econometrics,” Journal of Econometrics, 1, 377–395.CrossRefGoogle Scholar
  162. Spanos, A. (1995), “On theory testing in econometrics: Modeling with nonexperimental data,” Journal of Econometrics, 67, 189–226.CrossRefGoogle Scholar
  163. Spencer, D. E. (1993), “Developing a Bayesian vector autoregression forecasting model,” International Journal of Forecasting, 9, 407–421.CrossRefGoogle Scholar
  164. Stock, J. H. (1994), “Unit roots, structural breaks and trends,” Handbook of Econometrics. Vol. 4, 2739–2841. Amsterdam: Elsevier Science.Google Scholar
  165. Stock, J. H. M. W. Watson (1996), “Evidence on structural instability in macroeconomic time series relations,” Journal of Business and Economic Statistics, 14, 11–30.Google Scholar
  166. Swamy, P.A.V.B., R.K. Conway M. R. LeBlanc (1989), “The stochastic coefficients approach to econometric modeling, part III: Estimation, stability testing and prediction,” Journal of Agricultural Economics Research, 41, 4–20.Google Scholar
  167. Swamy, P.A.V.B., A.B. Kennickell P. von zur Muehlen (1990), “Comparing forecasts from fixed and variable coefficient models: the case of money demand,” International Journal of Forecasting, 6, 469–477.CrossRefGoogle Scholar
  168. Swamy, P.A.V.B. P.A. Tinsley (1980), “Linear prediction and estimation methods for regression models with stationary stochastic coefficients,” Journal of Econometrics, 12, 103–142.CrossRefGoogle Scholar
  169. Thursby, J. G. (1992), “A comparison of several exact and approximate tests for structural shift under heteroscedasticity,” Journal of Econometrics, 53, 363–386.CrossRefGoogle Scholar
  170. Vere, D. J. G. R. Griffith (1995), “Forecasting in the Australian Lamb industry: The influence of alternative price determination processes,” Review of Marketing and Agricultural Economics, 63, 408–18.Google Scholar
  171. West, K. D. D. Cho (1995), “The predictive ability of several models of exchange rate volatility,” Journal of Econometrics, 69, 367–91.CrossRefGoogle Scholar
  172. Witt, S. F. C. A. Witt (1995), “Forecasting tourism demand: A review of empirical research,” International Journal of Forecasting, 11, 447–475.CrossRefGoogle Scholar
  173. Yokum, J. T. Jr. A. Wildt (1987), “Forecasting sales response for multiple time horizons and temporally aggregated data,” International Journal of Forecasting, 3, 479–488.CrossRefGoogle Scholar
  174. Zapata, H. O. P. Garcia (1990), “Price forecasting with time-series methods and nonstationary data: An application to monthly U.S. cattle prices,” Western Journal of Agricultural Economics, 15, 123–132.Google Scholar
  175. Zellner, A. (1992), “Statistics, science and public policy,” Journal of the American Statistical Association, 87, 1–6.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • P. Geoffrey Allen
    • 1
  • Robert Fildes
    • 2
  1. 1.Department of Resource EconomicsUniversity of MassachusettsUSA
  2. 2.Department of Management ScienceUniversity of LancasterUK

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