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3 results

  • 1 - Introduction

  • Russell A. Miller, Washington and Lee University, Virginia
  • Book:
    An Introduction to German Law and Legal Culture
    Print publication:
    17 October 2024, pp 1-36

  • Summary

    Chapter 1 introduces students to the various approaches used to pursue comparative legal studies. It especially presents the orthodox “legal families“ approach to macro-comparative law. The chapter then considers several critiques of that tradition. H. Patrick Glenn challenges the concept of “legal families“ and suggests a “legal traditions” framework to replace it. The chapter then presents the social-contextual approach to comparative law as promoted by Legrand. Finally, the chapter urges students to recognize the ethical implications of comparative law through Frankenberg’s concepts of “distancing” and “differencing.”

  • Some remarks on regression with autoregressive errors and their residual processes

  • R. J. Kulperger
  • Journal:
    Journal of Applied Probability / Volume 24 / Issue 3 / September 1987
    Published online by Cambridge University Press:
    14 July 2016, pp. 668-678
    Print publication:
    September 1987

  • We consider some linear regression models Y = Σα lfl(z) + X, where X is an autoregressive (AR) process. The residuals estimate the i.i.d. innovations sequence which drives the AR process. We then consider the partial sum process of the residuals and show they converge to Brownian bridges in certain cases. Some remarks are also made on similar processes when differencing is first applied to remove trends. When an AR process is differenced the residual partial sum can be asymptotically a random polynomial.

  • Time series modelling — an integrated approach

  • Don McNeil
  • Journal:
    Journal of Applied Probability / Volume 19 / Issue A / 1982
    Published online by Cambridge University Press:
    14 July 2016, pp. 413-425
    Print publication:
    1982

  • Some inadequacies of both the traditional (exponential smoothing) and Box-Jenkins approaches to time series forecasting of economic data are investigated. An approach is suggested which integrates these two methodologies. It is based on smoothing the data using straight line segments instead of differencing to obtain stationarity, and forecasting using an autoregressive-moving-average model for the residuals from the most recent linear segment. The efficiency of this approach is calculated theoretically using a series comprising integrated white noise.