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Economics and Finance

D-Index
48
Citations
9765
World Ranking
1513
National Ranking
915

Overview

Norman R. Swanson is affiliated with Rutgers, The State University of New Jersey in the United States. Their research primarily focuses on economics, econometrics, and finance, with a particular emphasis on several interrelated subfields and specialized topics.

The main fields of study for their work include:

  • Economics and Econometrics
  • Finance
  • General Economics, Econometrics and Finance
  • Management Science and Operations Research
  • Statistics and Probability

Their research addresses topics such as:

  • Monetary Policy and Economic Impact
  • Market Dynamics and Volatility
  • Financial Risk and Volatility Modeling
  • Complex Systems and Time Series Analysis
  • Forecasting Techniques and Applications
  • Stock Market Forecasting Methods
  • Statistical Methods and Inference

Swanson's published work appears frequently in the following venues:

  • SSRN Electronic Journal
  • International Journal of Forecasting
  • Journal of Empirical Finance
  • Journal of Applied Econometrics
  • Annals of Financial Economics

Significant recent papers include:

  • The M6 forecasting competition: Bridging the gap between forecasting and investment decisions, 2024, International Journal of Forecasting
  • Forecasting volatility using double shrinkage methods, 2021, Journal of Empirical Finance
  • Predicting interest rates using shrinkage methods, real-time diffusion indexes, and model combinations, 2020, Journal of Applied Econometrics
  • New Evidence of the Marginal Predictive Content of Small and Large Jumps in the Cross-Section, 2020, Econometrics
  • The M6 forecasting competition: Bridging the gap between forecasting and investment decisions, 2023, arXiv (Cornell University)

Their frequent coauthors encompass a network of researchers with whom they have published multiple works, including:

  • John C. Chao
  • Xiye Yang
  • Spyros Makridakis
  • Evangelos Spiliotis
  • Fotios Petropoulos

Best Publications

  • Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions

    Norman R. Swanson;Clive W. J. Granger

  • A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

    Norman R. Swanson;Halbert White

  • Consistent Estimation with a Large Number of Weak Instruments

    John C. Chao;Norman R. Swanson

  • Forecasting economic and financial time-series with non-linear models

    Michael P. Clements;Philip Hans Franses;Norman R. Swanson

  • Money and Output Viewed Through a Rolling Window

    Norman R. Swanson

  • A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks

    Norman R. Swanson;Halbert White

  • Predictive Density Evaluation

    Valentina Corradi;Norman Swanson

  • Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

    Norman R. Swanson;Halbert White

  • An introduction to stochastic unit-root processes

    Clive W.J. Granger;Norman R. Swanson

  • Can we improve the perceived quality of economic forecasts

    Clive W. J. Granger

  • Further developments in the study of cointegrated variables

    C. W. J. Granger;Norman Swanson

  • An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series

    Geetesh Bhardwaj;Norman R. Swanson

  • Instrumental Variable Estimation with Heteroskedasticity and Many Instruments

    Jerry A. Hausman;Whitney K. Newey;Tiemen Woutersen;John C. Chao

  • Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence

    Hyun Hak Kim;Norman R. Swanson

  • A consistent test for nonlinear out of sample predictive accuracy

    Valentina Corradi;Norman R. Swanson

  • Semiparametric ARX neural-network models with an application to forecasting inflation

    Xiaohong Chen;J. Racine;N.R. Swanson

  • Predictive density and conditional confidence interval accuracy tests

    Valentina Corradi;Norman R. Swanson

  • Chapter 5 Predictive Density Evaluation

    Valentina Corradi;Norman R. Swanson

  • Are Statistical Reporting Agencies Getting It Right? Data Rationality and Business Cycle Asymmetry

    Norman R Swanson;Dick van Dijk

  • Out-of-sample tests for Granger causality

    John Chao;Valentina Corradi;Norman R. Swanson

  • A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks

    Norman Swanson;Halbert White

  • Impulse Response Functions Based on Causal Approach to Residual Orthogonalization in Vector Autoregressions

    Norman Swanson;Clive Granger

Frequent Co-Authors

Eric Ghysels
Eric Ghysels University of North Carolina at Chapel Hill
Mark W. Watson
Mark W. Watson Princeton University
Clive W. J. Granger
Clive W. J. Granger University of California, San Diego
Halbert White
Halbert White University of California, San Diego
Kala Krishna
Kala Krishna Pennsylvania State University
Michael P. Clements
Michael P. Clements University of Reading
Jörg Breitung
Jörg Breitung University of Cologne
Peter Christoffersen
Peter Christoffersen University of Toronto
Philip Hans Franses
Philip Hans Franses Erasmus University Rotterdam

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