About

Robert G. King is a Professor in the Department of Economics at Boston University. His academic background includes a PhD from Brown University. His research focuses on macroeconomics, monetary economics, and economic growth. He is associated with the Institute for Economic Development (IED) and is involved in various seminars, lectures, and research activities within the department. For more information or to contact him, his office is located in SSW 502, and he can be reached via email at rking@bu.edu or by phone at 617-353-5941. His professional profile and additional details are available through his website.

Research topics

• Political Science
• Keynesian economics
• Economics
• Physics
• Monetary economics
• Law

Selected publications

• EE323 Developing a Multi-Indication Wider Societal Value Model to Estimate the Economic Impact of Semaglutide in Canada

  Value in Health · 2025-12-01

  article
  PublisherDOI

• Author response for "A novel framework to assess the prevention value of a health intervention"

  2025-10-13

  peer-review
  PublisherDOI

• EE628 Quantifying the Fiscal Value of Prevention Programs for Depression

  Value in Health · 2025-12-01

  article
  PublisherDOI

• Evolving Reputation for Commitment: The Rise, Fall and Stabilization of Us Inflation

  SSRN Electronic Journal · 2022-01-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Credibility and Explicit Inflation Targeting

  SSRN Electronic Journal · 2022-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• GLDEX: Fitting Single and Mixture of Generalised Lambda Distributions

  2022-05-12 · 5 citations

  datasetOpen access

  The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution. References include the following: Karvanen and Nuutinen (2008) "Characterizing the generalized lambda distribution by L-moments" &lt;<a href="https://doi.org/10.1016%2Fj.csda.2007.06.021" target="_top">doi:10.1016/j.csda.2007.06.021</a>&gt;, King and MacGillivray (1999) "A starship method for fitting the generalised lambda distributions" &lt;<a href="https://doi.org/10.1111%2F1467-842X.00089" target="_top">doi:10.1111/1467-842X.00089</a>&gt;, Su (2005) "A Discretized Approach to Flexibly Fit Generalized Lambda Distributions to Data" &lt;<a href="https://doi.org/10.22237%2Fjmasm%2F1130803560" target="_top">doi:10.22237/jmasm/1130803560</a>&gt;, Su (2007) "Nmerical Maximum Log Likelihood Estimation for Generalized Lambda Distributions" &lt;<a href="https://doi.org/10.1016%2Fj.csda.2006.06.008" target="_top">doi:10.1016/j.csda.2006.06.008</a>&gt;, Su (2007) "Fitting Single and Mixture of Generalized Lambda Distributions to Data via Discretized and Maximum Likelihood Methods: GLDEX in R" &lt;<a href="https://doi.org/10.18637%2Fjss.v021.i09" target="_top">doi:10.18637/jss.v021.i09</a>&gt;, Su (2009) "Confidence Intervals for Quantiles Using Generalized Lambda Distributions" &lt;<a href="https://doi.org/10.1016%2Fj.csda.2009.02.014" target="_top">doi:10.1016/j.csda.2009.02.014</a>&gt;, Su (2010) "Chapter 14: Fitting GLDs and Mixture of GLDs to Data using Quantile Matching Method" &lt;<a href="https://doi.org/10.1201%2Fb10159" target="_top">doi:10.1201/b10159</a>&gt;, Su (2010) "Chapter 15: Fitting GLD to data using GLDEX 1.0.4 in R" &lt;<a href="https://doi.org/10.1201%2Fb10159" target="_top">doi:10.1201/b10159</a>&gt;, Su (2015) "Flexible Parametric Quantile Regression Model" &lt;<a href="https://doi.org/10.1007%2Fs11222-014-9457-1" target="_top">doi:10.1007/s11222-014-9457-1</a>&gt;, Su (2021) "Flexible parametric accelerated failure time model"&lt;<a href="https://doi.org/10.1080%2F10543406.2021.1934854" target="_top">doi:10.1080/10543406.2021.1934854</a>&gt;.

  PublisherOA PDFDOI

• The Rise, Fall and Stabilization of U.S. Inflation: Shifting Regimes and Evolving Reputation

  National Bureau of Economic Research · 2021-12-01 · 4 citations

  reportOpen access1st authorCorresponding

  The rise, fall, and stabilization of US inflation between 1969 and 2005 is consistent with a model of shifting policy regimes that features a forward-looking New Keynesian Phillips curve, policymakers that can or cannot commit, and private sector learning about policymaker type. Using model-implied inflation forecasting rules to extract state variables from the inflation forecasts in the Survey of Professional Forecasters, we provide evidence that policy regimes without commitment prevailed before 1980 and regimes with commitment prevailed afterward. With theory and quantification, we find that evolution of reputational capital is central to understanding the behavior of inflation.

  PublisherDOI

• The Rise, Fall and Stabilization of U.S. Inflation: Shifting Regimes and Evolving Reputation

  SSRN Electronic Journal · 2021 · 7 citations

  1st authorCorresponding
  • Political Science
  • Economics
  • Keynesian economics
  PublisherDOI

• Managing Expectations in the New Keynesian Model

  Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2018-01-01 · 1 citations

  articleSenior author
  Publisher

• Business Cycles

  The New Palgrave Dictionary of Economics · 2018-01-01

  book-chapterSenior author

  Development of rational expectations models of the business cycle has been the central issue on the macroeconomic research agenda since the influential analyses of Robert Lucas (1972a, b). In this essay, we review these developments, focusing on the extent to which the rational expectations perspective has generated a new understanding of economic fluctuations.

  PublisherDOI

Frequent coauthors

• Alfred L. Copley

  77 shared

• Marvin Goodfriend

  Carnegie Mellon University

  57 shared

• Alexander L. Wolman

  45 shared

• Mark W. Watson

  34 shared

• Charles I. Plosser

  33 shared

• Michael Dotsey

  32 shared

• Yang Lu

  University of Houston

  29 shared

• Julia K. Thomas

  27 shared

Education

• Ph.D.

  Brown University