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6 results

  • 5 - Infill Asymptotic Theory and Applications in Financial Econometrics

  • from Part II - Continuous-Time Models and High-Frequency Financial Econometrics
  • Edited by Shuping Shi, Macquarie University, Sydney, Xiaohu Wang, Fudan University, Shanghai, Tao Zeng, Zhejiang University, China
  • Book:
    Financial Econometrics
    Published online:
    20 February 2025
    Print publication:
    27 February 2025, pp 132-158

  • Summary

    Recent years have seen a surge of econometric development of infill asymptotic theory. Unlike the traditional large-sample theory which assumes that an increasing sample size is due to an increasing time span (denoted as the long-span asymptotic theory in this chapter), infill asymptotic theory assumes that the sample size increases because the sampling frequency shrinks toward zero. The limit of the infill asymptotics of the estimators are those based on a continuous record. Not surprisingly, a development of infill asymptotic theory is closely linked to the increased popularity of continuous time models in applied economics and finance. This chapter reviews the literature on the infill asymptotic theory and applications in financial econometrics, such as unit root testing, bootstrap, and structural break models. In many applications, nonstandard limiting distribution arises. In some cases, the initial condition shows up in the limiting distributions. Monte Carlo studies are carried out to check the performance of the infill asymptotic theory relative to the long-span asymptotic theory.

Financial Econometrics
  • Theory and Applications
  • Edited by Shuping Shi, Xiaohu Wang, Tao Zeng
  • Published online:
    20 February 2025
    Print publication:
    27 February 2025
  • Financial Econometrics is a contribution to modern financial econometrics, overviewing both theory and application. It covers, in detail, three important topics in the field that have recently drawn the attention of the academic community and practitioners, with low-frequency data (trend determination, bubble detection, and factor-augmented regressions) and examines various topics in high-frequency financial econometrics with continuous time models and discretized data. Also included are the estimation of stochastic volatility models, posterior-based hypothesis testing, and posterior-based model selection. Exploring topics at the forefront of research in the field of financial econometrics, this book offers an accessible introduction to the research and provides the groundwork for the development of new econometric techniques.

  • 6 - Multipurpose Environment

  • from Part II - Basic Methods
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book:
    Chemical Production Scheduling
    Published online:
    01 May 2021
    Print publication:
    06 May 2021, pp 147-156

  • Summary

    In this chapter, we discuss scheduling in multipurpose environments, which are the most general sequential environments. As in single- and multistage facilities, the general problem is posed in terms of facility (e.g., number and capacity of units) and product (e.g., processing times) data, as well as raw material and resource availability (e.g., batch release times), and product demand (e.g., due times). If the batching problem is solved independently, however, then the problem can be expressed in terms of batches instead of products. This is the problem that we study in the present chapter. Models for the simultaneous batching and scheduling can be formulated using the ideas presented in Chapter 4. General shared resources and storage policies can be modeled using the techniques presented in e Chapters 4 and 5, respectively.

  • 5 - Multistage Environment

  • from Part II - Basic Methods
  • Christos T. Maravelias, Princeton University, New Jersey
  • Book:
    Chemical Production Scheduling
    Published online:
    01 May 2021
    Print publication:
    06 May 2021, pp 128-146

  • Summary

    In this chapter, we discuss scheduling in multistage environments. The problem statement is presented in Section 5.1 and three types of models are presented in Section 5.2 (sequence-based), Section 5.3 (continuous grid-based), and Section 5.4 (discrete grid-based). In Section 5.5, we introduce an important new feature, namely, storage constraints. Again, we build upon the material covered in the previous chapters to model assignment and sequencing decisions, as well as other constraints such as release and due times.

  • STRUCTURAL NONLINEAR CONTINUOUS-TIME MODELS IN ECONOMETRICS

  • CLIFFORD R. WYMER
  • Journal:
    Macroeconomic Dynamics / Volume 1 / Issue 2 / June 1997
    Published online by Cambridge University Press:
    02 March 2005, pp. 518-548

  • Economic theory indicates the need for nonlinear structural models to study medium-term and long-run dynamic behavior of an economy. This paper argues that economic systems can be better specified and estimated using differential-equation rather than difference-equation systems and briefly reviews the estimators of continuous models. This approach of specifying structural models on the basis of economic theory and institutional structure explicitly and then testing the underlying hypothesis to verify the structural form is contrasted with a general-to-specific approach of successively more restricted VARMAX processes. Previous analyses of stability about the steady state or fixed point in phase space are extended to more general attractors to allow an investigation of complexity in economic systems. The critical dependence of some attractors, and particularly strange attractors, on parameter values emphasizes the need for consistent, efficient estimation. A structural approach provides a rigorous alternative to using single time series to determine whether economic systems exhibit aperiodic or chaotic dynamical behavior.

  • ESTIMATION OF CONTINUOUS-TIME MODELS FOR STOCK RETURNS AND INTEREST RATES

  • A. RONALD GALLANT, GEORGE TAUCHEN
  • Journal:
    Macroeconomic Dynamics / Volume 1 / Issue 1 / January 1997
    Published online by Cambridge University Press:
    02 March 2005, pp. 135-168

  • Efficient Method of Moments is used to estimate and test continuous-time diffusion models for stock returns and interest rates. For stock returns, a four-state, two-factor diffusion with one state observed can account for the dynamics of the daily return on the S&P Composite Index, 1927–1987. This contrasts with results indicating that discrete-time, stochastic volatility models cannot explain these dynamics. For interest rates, a trivariate Yield-Factor Model is estimated from weekly, 1962–1995, Treasury rates. The Yield-Factor Model is sharply rejected, although extensions permitting convexities in the local variance come closer to fitting the data.