ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Economic and Policy Uncertainties and Firm Value in the U.S. Consumer Nondurable Goods Industry

Bahram Adrangi, Arjun Chatrath, Madhuparna Kolay and Kambiz Raffiee

Correspondence: Bahram Adrangi, adrangi@up.edu

University of Portland. Portland, Oregon.

pdf (594.45 Kb) | doi: https://doi.org/10.47260/bae/1226

Abstract

This paper examines the impact of macroeconomic and policy-related uncertainties on firm value, measured by Tobin’s Q, for a panel of nine major U.S. consumer nondurable goods firms from 1980 through the first quarter of 2022. Utilizing panel quantile regression and panel MIDAS-VAR models, the analysis incorporates firm-level financial variables such as the current and quick ratios, debt-to-asset ratio, and operating income after depreciation. The findings reveal that economic policy uncertainty, consumer confidence, and inflationary expectations are positively associated with Tobin’s Q and Granger cause firm value across the distribution. In contrast, recessionary expectations do not significantly influence Tobin’s Q, reflecting the essential nature of the sector’s products and its relative insulation from business cycle downturns. These results suggest that firms in this sector may benefit from strategic focus on liquidity management, operational efficiency, and responsiveness to shifts in consumer sentiment and policy conditions.

Keywords:

  Tobin’s Q, Uncertainty, Economic Policy Uncertainty, Inflation expectations, Consumer confidence index, Panel quantile regression, Panel MIDS Vector autoregressive.


References

Adrangi, B., & Raffiee, K. (1999). On total price uncertainty and the behavior of a competitive firm. The American Economist, 43(2), 59-65.

Adrangi, B., Chatrath, A., Kolay, M., & Raffiee, K (2024a) Enterprise Value, Economic and Policy Uncertainties:  The Case of US Air Carriers. Journal of Theoretical Accounting Research, 20(1), 123-175.

Adrangi, B., Chatrath, A., & Raffiee, K. (2023a). S&P 500 volatility, volatility regimes, and economic uncertainty. Bulletin of Economic Research, 75(4), 1362-1387.

Adrangi, B., Chatrath, A., Maitra, D., & Sengupta, A. (2024b). Is the Influence of Oil Shocks on Economic Policy Uncertainty Fading? American Business Review, 27(2), 5.

Adrangi, B., Chatrath, A., & Raffiee, K. (2024c). U.S. Diesel Fuel Price Volatility and Economic Policy Uncertainty.  Oil, Gas & Energy Quarterly, 72 (3), 533-566.

Adrangi, B., Chatrath, A., & Raffiee, K. (2025b).  Latin American Equities, Volatility Regimes, and the US Economic Policy Uncertainty. Bulletin of Applied Economics, Forthcoming.

Adrangi, B., Chatrath, A., Hatamerad, S. ,& Raffiee, K. (2025a).  Equity Markets Volatility, Regime Dependence and Economic Uncertainty: The Case of Pacific Basin. Bulletin of Applied Economics, Bulletin of Applied Economics, 2025, 12(1), 75-105. https://doi.org/10.47260/bae/1215

Adrangi, B., Hatamerad, S., Kolay, M., & Raffiee, K . (2026). Economic and Policy Uncertainties and Firm Value: The Case of Consumer Durable Goods. Journal of Theoretical Accounting Research, Forthcoming.  22(2), 111-158.

Adrangi, B., & Hamilton, E. (2023b). Market concentration and performance: The case of the US airline industry. The Journal of Theoretical Accounting Research, 18(3), 54-78.

Angrist, J., Chernozhukov, V., & Fernández‐Val, I. (2006). Quantile regression under misspecification, with an application to the US wage structure. Econometrica, 74(2), 539-563.

Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229-1279. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Vol. 131 November 2016 Issue 4. The Quarterly Journal of Economics, 1593, 1636.

Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636.

Baker, Scott, Bloom, Nicholas and Davis, Steven J. (2012), “Measuring Economic Policy Uncertainty,” University of Chicago and Stanford University. Available at www.policyuncertainty.com

Baker, S. R., Bloom, N., & Davis, S. J. (2014). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 129(4), 1631-1686.

Barrodale, I., & Roberts, F. D. (1973). An improved algorithm for discrete l_1 linear approximation. SIAM Journal on Numerical Analysis, 10(5), 839-848.

Butt, M. N., Baig, A. S., & Seyyed, F. J. (2023). Tobin’s Q approximation as a metric of firm performance: an empirical evaluation. Journal of Strategic Marketing, 31(3), 532-548.

Canay, I. A. (2011). A simple approach to quantile regression for panel data. The econometrics journal, 14(3), 368-386.

Canova, F., & Ciccarelli, M. (2013). Panel Vector Autoregressive Models: A Survey☆ The views expressed in this article are those of the authors and do not necessarily reflect those of the ECB or the Eurosystem. In VAR models in macroeconomics–new developments and applications: Essays in honor of Christopher A. Sims (pp. 205-246). Emerald Group Publishing Limited.

Chauvet, M. (1998). An Economic Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching. International Economic Review, 39, 969-996.

Chauvet, M. and J. Piger. (2008).  A Comparison of the Real-Time Performance of Business Cycle Dating Methods.  Journal of Business and Economic Statistics, 26, 42-49.

Chauvet, Marcelle and Piger, Jeremy Max, Smoothed U.S. Recession Probabilities [RECPROUSM156N], retrieved from FRED, Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/RECPROUSM156N, July 1, 2024.

Danielsen, T. M. (2017). Effect of Exposure to Renewable Energy on Valuation of Oil and Gas Companies (Master's thesis, University of Stavanger, Norway).

Davis, S. J., Bloom, N., & Baker, S. R. (2013). Measuring economic policy uncertainty. NBER Working Paper, 21633.

Demir, E., & Ersan, O. (2017). Economic policy uncertainty and cash holdings: Evidence from BRIC countries. Emerging Markets Review, 33, 189-200.

Fadul, L. (2005). Ethical Behavior and CSR in the Oil and Gas Sector: Implications for Firm Value. Journal of Business Ethics, 145-161.

Flacco, P. R., & Kroetch, B. G. (1986). Adjustment to Production Uncertainty and the Theory of the Firm. Economic Inquiry, 24(3), 485-495.

Fooladi, I. (1986). The effect of proportional profit tax on the level of output, under uncertainty. Atlantic Economic Journal, 14(4), 90-94.

Fooladi, I., & Kayhani, N. (1991). Random Cost Functions and Production Decisions. Eastern Economic Journal, 17(2), 199-202.

Galbraith, John Kenneth. The Age of Uncertainty. Ed. Boston: Houghton Mifflin Company, 1977.

Galvao Jr, A. F. (2011). Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics, 164(1), 142-157.

Galvao, A. F., & Poirier, A. (2019). Quantile regression random effects. Annals of Economics and Statistics, (134), 109-148.

Galvao, A. F., Gu, J., & Volgushev, S. (2020). On the unbiased asymptotic normality of quantile regression with fixed effects. Journal of Econometrics, 218(1), 178-215.

Geraci, M. (2019). Additive quantile regression for clustered data with an application to children's physical activity. Journal of the Royal Statistical Society Series C: Applied Statistics, 68(4), 1071-1089.

García-Gómez, C. D., Demir, E., Chen, M. H., & Díez-Esteban, J. M. (2022). Understanding the effects of economic policy uncertainty on US tourism firms’ performance. Tourism Economics, 28(5), 1174-1192.

Golchin, B., and B. Rekabdar. (2024).  Anomaly Detection in Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning.   Conference on AI, Science, Engineering, and Technology (AIxSET), Laguna Hills, CA, USA. 1-8, doi: 10.1109/AIxSET62544.2024.00007. https://ieeexplore.ieee.org/abstract/document/10770963

Golchin, B. and N. Riahi. (2021). Emotion detection in twitter messages using combination of long-short-term memory and convolutional deep neural networks. International Conference on Computer and Knowledge Engineering (ICCKE), World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:15, No:9.

Ghysels, E. (2004). The MIDAS touch: Mixed data sampling regression models. University of North Carolina.

Ghysels, E. (2016). Macroeconomics and the reality of mixed frequency data. Journal of Econometrics, 193(2), 294-314.

Hall, P., & Sheather, S. J. (1988). On the distribution of a studentized quantile. Journal of the Royal Statistical Society: Series B (Methodological), 50(3), 381-391.

He, X., & Zhu, L. X. (2003). A lack-of-fit test for quantile regression. Journal of the American Statistical Association, 98(464), 1013-1022.

Hendricks, W., & Koenker, R. (1992). Hierarchical spline models for conditional quantiles and the demand for electricity. Journal of the American statistical Association, 87(417), 58-68.

Jin, Y., & Jorion, P. (2006). Firm value and hedging: Evidence from US oil and gas producers. The journal of Finance, 61(2), 893-919.

Kato, K., Galvao Jr, A. F., & Montes-Rojas, G. V. (2012). Asymptotics for panel quantile regression models with individual effects. Journal of Econometrics, 170(1), 76-91.

Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.

Koenker, R. W., & d'Orey, V. (1987). Algorithm AS 229: Computing regression quantiles. Applied statistics, 383-393.

Koenker, R., & Machado, J. A. (1999). Goodness of fit and related inference processes for quantile regression. Journal of the american statistical association, 94(448), 1296-1310.

Koenker, R. (2004). Quantile regression for longitudinal data. Journal of multivariate analysis, 91(1), 74-89.

Koenker, R. (2005). Quantile regression [M]. Econometric Society Monographs, Cambridge University Press, Cambridge.

Koenker, R., & Ng, P. (2005). Inequality constrained quantile regression. Sankhyā: The Indian Journal of Statistics, 418-440.

Lamarche, C. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157(2), 396-408.

Litterman, R. B. (1986). Forecasting with Bayesian vector autoregressions: Five years of experience. In V. (. Zarnowitz, Business Cycles, Indicators, and Forecasting (pp. 87–156). Chicago: University of Chicago Press.

Moon, H. R., & Weidner, M. (2015). Linear regression for panel with unknown number of factors as interactive fixed effects. Econometrica, 83(4), 1543-1579.

Moon, H. R., & Weidner, M. (2017). Dynamic linear panel regression models with interactive fixed effects. Econometric Theory, 33(1), 158-195.

Pesaran, M. H. (2006). Estimation and Inference in Large Heterogeneous Panels with Multifactor Error Structure. Econometrica, 967–1012.

Phan, D., Nguyen, H., & Faff, R. (2014). Uncovering the asymmetric linkage between financial derivatives and firm value—The case of oil and gas exploration and production companies. Energy economics, 45, 340-352.

Portnoy, S. (1991). Asymptotic behavior of regression quantiles in non-stationary, dependent cases. Journal of Multivariate analysis, 38(1), 100-113.

Powell, J. L. (1986). Censored regression quantiles. Journal of econometrics, 32(1), 143-155.

Sandmo, A. (1971). On the theory of the competitive firm under price uncertainty. The American Economic Review, 65-73.

Syaifulhaq, M. D. H., & Herwany, A. (2020). Capital Structure and Firm’s Growth in Relations to Firm Value at Oil and Gas Companies Listed in Indonesia Stock Exchange. Journal of Accounting Auditing and Business-Vol, 3(1).

Yildiz Savas, E., & Kapusuzoglu, A. (2020). Risk management activities for oil and gas producers and the impact on firm value. Environmental Science and Pollution Research, 27(7), 7087-7095.

Yu, K. L. (2003). Quantile regression: Applications and current research areas. ournal of the Royal Statistical Society.