Research

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Selected KPMG Research


  • Dreaming of Infallibility: Periodic Adjustments Under Reg. Section 1.482-4

    With Thomas D. Bettge, Hans Gerling, Mark R. Martin, and Jack O'Meara

    Published May 2025 in Tax Notes Federal, Vol. 187.
    Link

    This technical note addresses a significant shift in how the IRS plans to handle tax adjustments for international companies that transfer valuable assets like patents and trademarks between their own entities as of 2025. For decades, a rule allowing the IRS to revisit these transfers and adjust taxes based on later profits—known as periodic adjustments—has rarely been used. However, a new IRS memo from January 2025 signals a more aggressive stance, suggesting these adjustments will now be considered almost automatically correct. The note explains why this new interpretation is problematic and could lead to increased tax liabilities and disputes for businesses. It also outlines key strategies companies can use to prepare, such as securing an Advance Pricing Agreement (APA) with the IRS or implementing contractual "true-up" mechanisms to manage potential future tax demands.

Journal Publications


  • Credit Market Expectations and the Business Cycle: Evidence from a Textual Analysis Approach
    Published Sep. 2024 in Economics Bulletin, Vol. 44 No. 3 pp. 1242-1253.
    Link

    This paper examines the relationship between errors in credit spread expectations and key macroeconomic indicators over the period 1948 to 2022. By employing textual analysis on Wall Street Journal title pages, I construct a historical proxy for credit market sentiment, extending the data on credit spread expectations back to 1919. The Survey of Professional Forecasters provides the training data for this model. The analysis reveals that increases in credit spread expectation errors, interpreted as signals of heightened market optimism, are robust predictors of subsequent declines in economic activity. Most saliently, a one-standard deviation increase in forecast errors is associated with a 1.47 percentage point decline in GDP growth, highlighting the significant role of credit market sentiment in driving macroeconomic cycles.

Other Working Papers


  • Monetary Policy Announcements and Household Expectations of the Future
    Link

    This paper studies how U.S. households adjust their expectations after Federal Reserve announcements from 2013 to 2021. Using microdata from the Survey of Consumer Expectations, we find short-term revisions in inflation, interest rate, and home price forecasts. Inaction by the Fed signals dovishness and lowers expectations for future rate hikes. Tightening, by contrast, dampens home price growth. Other domains, like income and spending, show little movement. Information frictions play a central role: effects weaken at longer horizons and adjust slowly over time. The results highlight the limits of monetary policy communication when attention and interpretation vary.

    • Household Sentiment Analysis through a Hierarchical Bayesian Latent Class Model
      Link

      This paper employs Latent Dirichlet Analysis for Survey Data (LDA-S) to identify and classify households into distinct belief types based on their responses in the Survey of Consumer Expectations (SCE). I uncover three belief types-inconsistent/uncertain, pessimistic, and optimistic-characterized by unique patterns of expectations about macroeconomic and personal financial conditions. By incorporating these belief types into a model predicting inflation expectations, I demonstrate a significant improvement in the model's explanatory power. The findings of this study have important implications for central bank communication strategies. As different belief types are shown to have a statistically significant impact on respondents' 12-month inflation expectations, it becomes crucial for central banks to consider the type of information households are consuming and tailor their communication accordingly. Moreover, this research highlights the potential of using latent class analysis techniques to extract valuable information from survey data, which can be applied in various economic contexts.