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6054 results in Econometrics and mathematical methods

Page 30 of 303

  • 5 - Similarity Matching and Scaling the Experimental Data

  • Tobias F. Rötheli, Universität Erfurt, Germany
  • Book:
    The Behavioral Economics of Inflation Expectations
    Published online:
    24 July 2020
    Print publication:
    13 August 2020, pp 53-68

  • Summary

    This chapter details how the laboratory data on expectations are used to generate historical series of expectations.1 In essence, and proceeding on the assumption that economic agents function just like our subjects in the laboratory, we fit the responses of subjects to historical four-year sequences of the price level. In this process of fitting the laboratory expectations to historical data, two important aspects of behavior have to be addressed. The first aspect is the issue of similarity matching and the second concerns the scaling of the elicited data.

  • 8 - Inflation Dynamics

  • Tobias F. Rötheli, Universität Erfurt, Germany
  • Book:
    The Behavioral Economics of Inflation Expectations
    Published online:
    24 July 2020
    Print publication:
    13 August 2020, pp 85-93

  • Summary

    In this chapter, we use the measure of pattern-based expectations in an econometric investigation of inflation. It is well known that expected inflation itself is an important driver of inflation. Older accounts see the inflationary expectations of workers as the central variable that explains why expectations affect the course of the aggregate price level. In more recent contributions, decision-making of firms takes central stage. The expectation of producers regarding the future course of the price level is seen as an important element in firms’ price setting.1 The empirical study here follows this newer approach.

  • 4 - Features of the Laboratory Data

  • Tobias F. Rötheli, Universität Erfurt, Germany
  • Book:
    The Behavioral Economics of Inflation Expectations
    Published online:
    24 July 2020
    Print publication:
    13 August 2020, pp 47-52

  • Summary

    This chapter analyses the data elicited in the laboratory as described in Chapter 3. More specifically, before using the expectations data for the computation of time series of expectations, we want to assess how the elicited data compare across treatments and horizons. Further, it will be demonstrated that the expectations data from the laboratory survey cannot be explained by a simple linear model of extrapolation. This finding justifies the procedures detailed in the coming chapters.

The Behavioral Economics of Inflation Expectations
  • Macroeconomics Meets Psychology
  • Tobias F. Rötheli
  • Published online:
    24 July 2020
    Print publication:
    13 August 2020
  • As one of the first texts to take a behavioral approach to macroeconomic expectations, this book introduces a new way of doing economics. Rötheli uses cognitive psychology in a bottom-up method of modeling macroeconomic expectations. His research is based on laboratory experiments and historical data, which he extends to real-world situations. Pattern extrapolation is shown to be the key to understanding expectations of inflation and income. The quantitative model of expectations is used to analyze the course of inflation and nominal interest rates in a range of countries and historical periods. The model of expected income is applied to the analysis of business cycle phenomena such as the great recession in the United States. Data and spreadsheets are provided for readers to do their own computations of macroeconomic expectations. This book offers new perspectives in many areas of macro and financial economics.

Statistics
  • Karim M. Abadir, Risto D. H. Heijmans, Jan R. Magnus
  • Published online:
    11 June 2020
    Print publication:
    08 November 2018
  • Building on the success of Abadir and Magnus' Matrix Algebra in the Econometric Exercises Series, Statistics serves as a bridge between elementary and specialized statistics. Professors Abadir, Heijmans, and Magnus freely use matrix algebra to cover intermediate to advanced material. Each chapter contains a general introduction, followed by a series of connected exercises which build up knowledge systematically. The characteristic feature of the book (and indeed the series) is that all exercises are fully solved. The authors present many new proofs of established results, along with new results, often involving shortcuts that resort to statistical conditioning arguments.

Languages in Space and Time
  • Models and Methods from Complex Systems Theory
  • Marco Patriarca, Els Heinsalu, Jean Leó Leonard
  • Published online:
    04 June 2020
    Print publication:
    29 October 2020
  • This cross-disciplinary volume provides an overview of how complexity theory and the tools of statistical mechanics can be applied to linguistic problems to help reveal language groups, and to model the evolution and competition of languages in space and time. Illustrated with a series of case studies and worked examples, it presents an interdisciplinary framework to enable researchers from the mathematical, physical and social sciences to collaborate on linguistic problems. It demonstrates the complexity of linguistic databases and provides a mathematical toolkit for analyzing and extracting useful information from them - helping to conceptualize empirical facts better than a mere ethnographic view. Providing an important bridge to facilitate collaboration between linguists and mathematical modelers, this book will stimulate new ideas and avenues for research, and will form a valuable resource for advanced students and academics working across complex systems, sociolinguistics, and language dynamics.

The Rise and Fall of Business Firms
  • A Stochastic Framework on Innovation, Creative Destruction and Growth
  • S. V. Buldyrev, F. Pammolli, M. Riccaboni, H. E. Stanley
  • Published online:
    12 May 2020
    Print publication:
    13 August 2020
  • At the intersection between statistical physics and rigorous econometric analysis, this powerful new framework sheds light on how innovation and competition shape the growth and decline of companies and industries. Analyzing various sources of data including a unique micro level database which collects historic data on the sales of more than 3,000 firms and 50,000 products in 20 countries, the authors introduce and test a model of innovation and proportional growth, which relies on minimal assumptions and accounts for the empirically observed regularities. Through a combination of extensive stochastic simulations and statistical tests, the authors investigate to what extent their simple assumptions are falsified by empirically observable facts. Physicists looking for application of their mathematical and modelling skills to relevant economic problems as well as economists interested in the explorative analysis of extensive data sets and in a physics-orientated way of thinking will find this book a key reference.

Page 30 of 303