Forecasting GDP Growth Rates Using Accounting Earnings: A Large Panel Microdata Approach

References

• Abdalla AM, Carabias JM, Patatoukas PN (2021) The real-time macro content of corporate financial reports: A dynamic factor model approach. J. Monetary Econom. 118:260–280.Crossref, Google Scholar
• Adam K, Nagel S (2023) Expectations data in asset pricing. Handbook of Economic Expectations (Academic Press, Cambridge, MA), 477–586. Google Scholar
• Anesti N, Galvão AB, Miranda-Agrippino S (2022) Uncertain kingdom: Nowcasting gross domestic product and its revisions. J. Appl. Econometrics 37(1):42–62.Crossref, Google Scholar
• Ang A, Piazzesi M, Wei M (2006) What does the yield curve tell us about GDP growth? J. Econom. 131(1–2):359–403.Crossref, Google Scholar
• Anilowski C, Feng M, Skinner DJ (2007) Does earnings guidance affect market returns? The nature and information content of aggregate earnings guidance. J. Accounting Econom. 44(1–2):36–63. Google Scholar
• Bai J, Ng S (2008) Forecasting economic time series using targeted predictors. J. Econom. 146(2):304–317.Crossref, Google Scholar
• Bai J, Ng S (2009) Boosting diffusion indices. J. Appl. Econometrics 24(4):607–629.Crossref, Google Scholar
• Baqaee DR, Farhi E (2019) The macroeconomic impact of microeconomic shocks: Beyond Hulten’s theorem. Econometrica 87(4):1155–1203.Crossref, Google Scholar
• Barro RJ (1990) The stock market and investment. Rev. Financial Stud. 3(1):115–131.Crossref, Google Scholar
• Bernard VL, Thomas JK (1990) Evidence that stock prices do not fully reflect the implications of current earnings for future earnings. J. Accounting Econom. 13(4):305–340.Crossref, Google Scholar
• Bond P, Edmans A, Goldstein I (2012) The real effects of financial markets. Annual Rev. Financial Econom. 4:339–360.Crossref, Google Scholar
• Bryzgalova S, Pelger M, Zhu J (2025) Forest through the trees: Building cross-sections of stock returns. J. Finance 80(5):2447–2506.Crossref, Google Scholar
• Campbell JY (1999) Asset prices, consumption, and the business cycle. Handbook of Macroeconomics, vol. 1, part C (Elsevier, Amsterdam), 1231–1303.Google Scholar
• Carriero A, Clark TE, Marcellino M (2016) Common drifting volatility in large Bayesian VARs. J. Bus. Econom. Statist. 34(3):375–390.Crossref, Google Scholar
• Chodorow-Reich G, Nenov PT, Simsek A (2021) Stock market wealth and the real economy: A local labor market approach. Amer. Econom. Rev. 111(5):1613–1657. Google Scholar
• Chudik A, Grossman V, Pesaran MH (2016) A multi-country approach to forecasting output growth using PMIs. J. Econom. 192(2):349–365. Google Scholar
• Clark TE, West KD (2007) Approximately normal tests for equal predictive accuracy in nested models. J. Econom. 138(1):291–311.Crossref, Google Scholar
• Cong LW, Feng G, He J, He X (2025) Growing the efficient frontier on panel trees. J. Financial Econom. 167:104024.Crossref, Google Scholar
• Cooley TF, Quadrini V (2001) Financial markets and firm dynamics. Amer. Econom. Rev. 91(5):1286–1310.Crossref, Google Scholar
• Cooper RW, Haltiwanger JC (2006) On the nature of capital adjustment costs. Rev. Econ. Stud. 73(3):611–633. Crossref, Google Scholar
• Crouzet N, Mehrotra NR (2020) Small and large firms over the business cycle. Amer. Econom. Rev. 110(11):3549–3601.Crossref, Google Scholar
• Edmans A, Jayaraman S, Schneemeier J (2017) The source of information in prices and investment-price sensitivity. J. Financial Econom. 126(1):74–96.Crossref, Google Scholar
• Feige EL, Watts HW (1972) An investigation of the consequences of partial aggregation of micro-economic data. Econometrica 40(2):343–360.Crossref, Google Scholar
• Feng G, Giglio S, Xiu D (2020) Taming the factor zoo: A test of new factors. J. Finance 75(3):1327–1370.Crossref, Google Scholar
• Fort TC, Haltiwanger J, Jarmin RS, Miranda J (2013) How firms respond to business cycles: The role of firm age and firm size. NBER Working Paper No. 19134, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Foucault T, Fresard L (2014) Learning from peers’ stock prices and corporate investment. J. Financial Econom. 111(3):554–577.Crossref, Google Scholar
• Gabaix X (2011) The granular origins of aggregate fluctuations. Econometrica 79(3):733–772.Crossref, Google Scholar
• Gaertner FB, Kausar A, Steele LB (2020) Negative accounting earnings and gross domestic product. Rev. Account. Stud. 25(4):1382–1409. Crossref, Google Scholar
• Gertler M, Gilchrist S (1994) Monetary policy, business cycles, and the behavior of small manufacturing firms. Quart. J. Econom. 109(2):309–340.Crossref, Google Scholar
• Gertler M, Gilchrist S (2018) What happened: Financial factors in the great recession. J. Econom. Perspect. 32(3):3–30.Crossref, Google Scholar
• Giannone D, Lenza M, Primiceri GE (2021) Economic predictions with big data: The illusion of sparsity. Econometrica 89(5):2409–2437.Crossref, Google Scholar
• Gilchrist S, Zakrajšek E (2012) Credit spreads and business cycle fluctuations. Amer. Econom. Rev. 102(4):1692–1720.Crossref, Google Scholar
• Glas A, Heinisch K (2023) Conditional macroeconomic survey forecasts: Revisions and errors. J. Internat. Money Finance 138:102927.Google Scholar
• Granger CWJ (1987) Implications of aggregation with common factors. Econom. Theory 3(2):208–222.Crossref, Google Scholar
• Gu S, Kelly B, Xiu D (2020) Empirical asset pricing via machine learning. Rev. Financial Stud. 33(5):2223–2273.Crossref, Google Scholar
• Hamilton JD (1996) This is what happened to the oil price-macroeconomy relationship. J. Monetary Econom. 38(2):215–220.Crossref, Google Scholar
• Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd. (Springer, New York).Google Scholar
• Huang D, Jiang F, Tu J, Zhou G (2015) Investor sentiment aligned: A powerful predictor of stock returns. Rev. Financial Stud. 28(3):791–837.Crossref, Google Scholar
• Hulten CR (1978) Growth accounting with intermediate inputs. Rev. Econom. Stud. 45(3):511–518.Crossref, Google Scholar
• Jermann UJ, Quadrini V (2007) Stock market boom and the productivity gains of the 1990s. J. Monetary Econom. 54(2):413–432.Crossref, Google Scholar
• Kalay A, Nallareddy S, Sadka G (2018) Uncertainty and sectoral shifts: The interaction between firm-level and aggregate-level shocks, and macroeconomic activity. Management Sci. 64(1):198–214.Link, Google Scholar
• Kaplan G, Violante GL (2018) Microeconomic heterogeneity and macroeconomic shocks. J. Econom. Perspect. 32(3):167–194.Crossref, Google Scholar
• Kelly BT, Pruitt S, Su Y (2019) Characteristics are covariances: A unified model of risk and return. J. Financial Econom. 134(3):501–524.Crossref, Google Scholar
• Kilian L, Vigfusson RJ (2013) Do oil prices help forecast U.S. real GDP? The role of nonlinearities and asymmetries. J. Bus. Econom. Statist. 31(1):78–93.Crossref, Google Scholar
• Konchitchki Y, Patatoukas PN (2014a) Accounting earnings and gross domestic product. J. Accounting Econom. 57(1):76–88.Crossref, Google Scholar
• Konchitchki Y, Patatoukas PN (2014b) Taking the pulse of the real economy using financial statement analysis: Implications for macro forecasting and stock valuation. Accounting Rev. 89(2):669–694.Crossref, Google Scholar
• Konchitchki Y, Luo Y, Ma MLZ, Wu F (2016) Accounting-based downside risk, cost of capital, and the macroeconomy. Rev. Accounting Stud. 21:1–36.Crossref, Google Scholar
• Koop GM (2013) Forecasting with medium and large Bayesian VARs. J. Appl. Econometrics 28(2):177–203.Crossref, Google Scholar
• Lev B, Sougiannis T (1996) The capitalization, amortization, and value-relevance of R&D. J. Accounting Econom. 21(1):107–138. Google Scholar
• Levine R, Zervos S (1998) Stock markets, banks, and economic growth. Amer. Econom. Rev. 88(3):537–558.Google Scholar
• Litterman RB (1986) Forecasting with Bayesian vector autoregressions: Five years of experience. J. Bus. Econom. Statist. 4(1):25–38.Google Scholar
• Longo L, Riccaboni M, Rungi A (2022) A neural network ensemble approach for GDP forecasting. J. Econom. Dynam. Control 134:104278.Crossref, Google Scholar
• Marcellino M, Porqueddu M, Venditti F (2016) Short-term GDP forecasting with a mixed-frequency dynamic factor model with stochastic volatility. J. Bus. Econom. Statist. 34(1):118–127.Crossref, Google Scholar
• Marcellino M, Stock JH, Watson MW (2003) Macroeconomic forecasting in the Euro area: Country specific versus area-wide information. Eur. Econom. Rev. 47(1):1–18.Crossref, Google Scholar
• Massy WF (1965) Principal components regression in exploratory statistical research. J. Amer. Statist. Assoc. 60(309):234–256.Crossref, Google Scholar
• McCracken MW, Ng S (2016) FRED-MD: A monthly database for macroeconomic research. J. Bus. Econom. Statist. 34(4):574–589.Crossref, Google Scholar
• McCracken MW, Ng S (2020) FRED-QD: A quarterly database for macroeconomic research. NBER Working Paper No. 26872, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Miao J, Wang P (2012) Bubbles and total factor productivity. Amer. Econom. Rev. 102(3):82–87.Crossref, Google Scholar
• Mogliani M, Simoni A (2021) Bayesian MIDAS penalized regressions: Estimation, selection, and prediction. J. Econom. 222(1, part C):833–860.Crossref, Google Scholar
• Nallareddy S, Ogneva M (2017) Predicting restatements in macroeconomic indicators using accounting information. Accounting Rev. 92(2):151–182.Crossref, Google Scholar
• Orcutt GH, Watts HW, Edwards JB (1968) Data aggregation and information loss. Amer. Econom. Rev. 58(4):773–787.Google Scholar
• Patatoukas PN (2014) Detecting news in aggregate accounting earnings: Implications for stock market valuation. Rev. Accounting Stud. 19:134–160.Crossref, Google Scholar
• Pesaran MH, Schuermann T, Smith LV (2009) Forecasting economic and financial variables with global VARs. Internat. J. Forecasting 25(4):642–675. Crossref, Google Scholar
• Poterba JM (2000) Stock market wealth and consumption. J. Econom. Perspect. 14(2):99–118.Crossref, Google Scholar
• Quadrini V, Sun Q (2018) Credit and firm-level volatility of employment. J. Eur. Econom. Assoc. 16(5):1433–1475. Crossref, Google Scholar
• Samadi SY, Herath HMWB (2024) Reduced-rank envelope vector autoregressive model. J. Bus. Econom. Statist. 42(3):918–932. Google Scholar
• Shen Z, Xiu D (2025) Can machines learn weak signals? NBER Working Paper No. 33421, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Shivakumar L (2007) Aggregate earnings, stock market returns and macroeconomic activity: A discussion of ‘does earnings guidance affect market returns? The nature and information content of aggregate earnings guidance.’ J. Accounting Econom. 44(1–2):64–73.Crossref, Google Scholar
• Shivakumar L, Urcan O (2017) Why does aggregate earnings growth reflect information about future inflation? Accounting Rev. 92(6):247–276.Crossref, Google Scholar
• Sims CA (1980) Macroeconomics and reality. Econometrica 48(1):1–48. Crossref, Google Scholar
• Stark T (1010) Realistic evaluation of real-time forecasts in the Survey of Professional Forecasters. Special report, Federal Reserve Bank of Philadelphia, Philadelphia, PA.Google Scholar
• Stock JH, Watson MW (2016) Dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconomics. Handbook of Macroeconomics, vol. 2 (Elsevier, Amsterdam), 415–525. Google Scholar
• Subrahmanyam A, Titman S (2013) Financial market shocks and the macroeconomy. Rev. Financial Stud. 26(11):2687–2717.Crossref, Google Scholar
• Sun Q, Xiaolan MZ (2019) Financing intangible capital. J. Financial Econom. 133(3):564–588.Crossref, Google Scholar
• Swanson NR, White H (1997) A model selection approach to real-time macroeconomic forecasting using linear models and artificial neural networks. Rev. Econom. Statist. 79(4):540–550.Crossref, Google Scholar
• Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J. Roy. Statist. Soc. Ser. B Statist. Methodology 67(2):301–320.Crossref, Google Scholar