Paul Shaman

Paul Shaman
  • Professor Emeritus of Statistics and Data Science

Contact Information

  • office Address:

    319 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: time series analysis

Overview

Education

PhD, Columbia University, 1966; MA, Columbia University, 1964; AB, Dartmouth College, 1961

Recent Consulting

Expert witness, probability analysis, U.S. Postal Service and State of New Jersey, 1993-94; Expert witness, statistical analysis, Sprague and Sprague, Philadelphia, 1994; Expert witness, statistical analysis, Duane, Morris & Heckscher, Philadelphia, 1995-97

Academic Positions Held

Wharton: 1977-present (Chairperson, Statistics Department, 1990-2002). Previous appointments: Carnegie Mellon University, Stanford University, New York University

Other Positions

Program Director for Statistics and Probability, National Science Foundation, 1984-85

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Teaching

Past Courses

  • STAT399 - INDEPENDENT STUDY

    Written permission of instructor and the department course coordinator required to enroll in this course.

  • STAT435 - FORECASTING METHODS MGMT

    This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT520 - APPLIED ECONOMETRICS I

    This is a course in econometrics for graduate students. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. Students taking the course should be familiar with elementary statistical methodology and basic linear algebra, and should have some programming experience. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models.

  • STAT5200 - Applied Econometrics I

    This is a course in econometrics for graduate students. The goal is to prepare students for empirical research by studying econometric methodology and its theoretical foundations. Students taking the course should be familiar with elementary statistical methodology and basic linear algebra, and should have some programming experience. Topics include conditional expectation and linear projection, asymptotic statistical theory, ordinary least squares estimation, the bootstrap and jackknife, instrumental variables and two-stage least squares, specification tests, systems of equations, generalized least squares, and introduction to use of linear panel data models.

  • STAT521 - APPLIED ECONOMETRICS II

    Topics include system estimation with instrumental variables, fixed effects and random effects estimation, M-estimation, nonlinear regression, quantile regression, maximum likelihood estimation, generalized method of moments estimation, minimum distance estimation, and binary and multinomial response models. Both theory and applications will be stressed.

  • STAT535 - FORECASTING METHODS MGMT

    This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT5350 - Forecasting Methods Mgmt

    This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations.

  • STAT711 - FORECASTING METHODS MGMT

    This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT7110 - Forecasting Methods Mgmt

    This course provides an introduction to the wide range of techniques available for statistical modelling and forecasting of time series. Regression methods for decomposition models, trends and seasonality, spectral analysis, distributed lag models, autoregressive-moving average modeling, forecasting, exponential smoothing, and ARCH and GARCH models will be surveyed. The emphasis will be on applications, rather than technical foundations and derivations. The techniques will be studied critically, with examination of their usefulness and limitations. This course may be taken concurrently with the prerequisite with instructor permission.

  • STAT899 - INDEPENDENT STUDY

    Written permission of instructor, the department MBA advisor and course coordinator required to enroll.

  • STAT999 - INDEPENDENT STUDY

    Written permission of instructor and the department course coordinator required to enroll.

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