University of Oxford: MPhil Advanced Econometrics

General-to-Specific Time Series Modelling

Economies are high dimensional, interdependent, evolving from many sources of innovation, and non-constant from intermittent and often unanticipated shifts, making discovering their properties the key objective of empirical economic modelling. The course covers time-series general-to-specific modelling with a focus on embedding theory in general models as a starting point for selection, the validity of conditional modelling and loss of information through concepts of exogeneity and the theory of reduction, as well as the automatic detection of structural breaks and outliers through indicator saturation.

Lecture Slides

Lecture 1

Lecture 2

Lecture 3

Lecture 4

Readings and References

Main references marked with *

Lecture 1 Core References

• *Hendry, D. F., & Johansen, S. (2015). Model discovery and Trygve Haavelmo’s legacy. Econometric Theory, 31(01), 93-114.
• *Hendry, D. F., & Krolzig, H. M. (2005). The properties of automatic Gets modelling. The Economic Journal, 115(502), C32-C61.
  • Additional reading:
  • Hendry, D. F., & Doornik, J. A. (2014). Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics. MIT Press.

Lecture 2 Core References

• *Hendry, D. F., Johansen, S. and Santos, C. (2008). Automatic selection of indicators in a fully saturated regression. Computational Statistics, 23(2), 317-335.
• *Hendry, D.F., Johansen, S. and Santos, C (2008) Erratum.
• *Castle, J. L., Doornik, J. A., Hendry, D. F., & Pretis, F. (2015). Detecting location shifts during model selection by step-indicator saturation. Econometrics, 3(2), 240-264.
  • Additional reading:
  • Johansen, S., & Nielsen, B. (2009). An analysis of the indicator saturation estimator as a robust regression estimator. In The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry. Castle, J., & Shephard, N. (Eds.). OUP Oxford.
  • Johansen, S., & Nielsen, B. (2016). Asymptotic theory of outlier detection algorithms for linear time series regression models. Scandinavian Journal of Statistics, 43(2), 321-348.

Lecture 3 Core References

• *Engle, R. F., Hendry, D. F., & Richard, J. F. (1983). Exogeneity. Econometrica, 277-304.
• *Ericsson, N. R., Hendry, D. F., & Mizon, G. E. (1998). Exogeneity, cointegration, and economic policy analysis. Journal of Business & Economic Statistics, 16(4), 370-387.
• *Hendry, D.F, & Santos, C. (2010). An automatic test of super exogeneity. In M.W. Watson, T. Bollerslev, and J. Russell (Eds.), Volatility and Time Series Econometrics, pp.164-193. OUP Oxford.
  • Additional reading:
  • Engle, R.F., and Hendry, D.F. (1993) Testing superexogeneity and invariance in regression models. Journal of Econometrics 56(1), 119-139.
  • Hendry, D. F. (2017). Granger Causality. European Journal of Pure and Applied Mathematics, 10(1), 12-29.
  • Pretis, F. (2017). Exogeneity in Climate Econometrics. Oxford Economics Discussion Paper.