Corrigendum for Dividend Dynamics, Learning, and Expected Stock Index Returns(The Journal of Finance, (2019), 74, 1, (401-448), 10.1111/jofi.12731)

Ravi Jagannathan, Binying Liu, Jiaqi Zhang

Research output: Contribution to journalComment/debate

Abstract

We discovered inconsistencies in our coding for Jagannathan and Liu (2019) that, after addressed, has led to changes in the tables and figures that we reported. These changes do not in anyway affect any of the paper's statements, findings, or conclusions. We report updated tables and figures in this erratum and highlight any statistics where the change is nontrivial by underlining it. The inconsistencies are as follows: In the paper, we mention that our Kalman filter estimation is based on nonoverlapping annual dividend data. However, in the codes we used a mixture of nonoverlapping and overlapping regressions to estimate parameters and state variables in the AR[1] processes of earnings-to-dividend ratios and inflation rates. Here, we correct this inconsistency, and use nonoverlapping data throughout all parts of the paper. In Tables and in Section I of the paper, we had accidentally lagged the right side variable by six months while estimating the model. This error has now been corrected. In Figure of the paper, we forgot to specify, when reporting summary plots for earnings-to-dividend ratios and inflation rates, that these plots were for the variables de-meaned. Here, we report the figure without de-meaning. In estimating expected returns from the long-run risks model, we accidentally mistook inflation lagged by one year as current inflation in some parts of our coding. This has been corrected. III Dividend Growth Rates and Expected Growth Rates (Table presented.) V Dividend Growth Rates and Expected Growth Rates (Real Rates) (Table presented.) VII Stock Index Returns and Stock Yields (Table presented.) VIII Stock Index Returns and Shocks to Dividend Expectations (Table presented.) The nontrivial changes are as follows. In Table (and Table), the out-of-sample R2 for our dividend model drops from 0.413 (0.395) to 0.320 (0.331), but remains statistically higher than the corresponding R2s of competing models. Return predictability R2 for the full learning model in Table increases from 0.271 to 0.291 for the full data sample, in Table it increases during expansions from 0.191 to 0.252, and decreases during recessions from 0.641 to 0.474. These changes do not change the main conclusions in the paper. The Internet Appendix to the paper gives the data, and the Matlab and Stata codes used in generating the tables and figures. IX Stock Index Returns and Epstein and Zin () Expected Returns (Table presented.) XI Return Predictability during Expansions versus Recessions (Table presented.) AII Dividend Growth Rates and Expected Growth Rates (Quarterly, Semiannual, and Biannual Rates) (Table presented.) AIII Stock Index Returns and Stock Yields (Quarterly, Semiannual, and Biannual Rates) (Table presented.) AIV Stock Index Returns and Epstein and Zin () Expected Returns (Quarterly, Semiannual, and Biannual Rates) (Table presented.) 5 (Figure presented.) Evolution of long-run risks model parameter and coefficient estimates over time. This figure plots estimates of the parameters in our long-run risks model, aside from those in the dividend process, and coefficients A that relate price-to-dividend ratios, the latent variable (Formula presented.), the retention ratio, and the inflation rate to expected returns, assuming that these parameters are estimated based on data up to time τ for τ between 1975 and 2016. The shaded regions are recessions. A year is in recession if any of its months correspond to NBER recession dates. Although all figures change somewhat, the discussions on these patterns do not change. 6 (Figure presented.) Evolution of long-run risks model state variables, expected excess stock index returns, and risk-free rate over time. This figure plots estimates of the state variables for our long-run risks model, as well as expected excess returns and the risk-free rate from our model, assuming full information, learning about dividends, or learning about all parameters in our long-run risks model (i.e. full learning), between 1975 and 2016. The shaded regions are recessions. A year is in recession if any of its months correspond to NBER recession dates. Although all figures change somewhat, the discussions on these patterns do not change. 7 (Figure presented.) Cumulative sum of squared errors difference. Panel A plots the cumulative sum of squared errors difference (SSED) of our long-run risks model, assuming learning about dividends, in predicting stock index returns. Panel B plots the SSED of our long-run risks model, assuming learning about all parameters in our long-run risks model (i.e., full learning). Dividends are estimated based on nonoverlapping annual data since 1946. Statistics are based on nonoverlapping annual data between 1975 and 2016. The shaded regions are recessions. A year is in recession if any of its months correspond to NBER recession dates. Although all figures change somewhat, the discussions on these patterns do not change. 8 (Figure presented.) Incremental gain in cumulative sum of squared errors difference from learning. Panel A plots the incremental gain in the cumulative sum of squared errors difference (SSED) of our long-run risks model, assuming learning about dividends versus full information. Panel B plots the incremental gain in SSED of our long-run risks model, assuming learning about all parameters in our long-run risks model (i.e. full learning), versus full information. Dividends are estimated based on nonoverlapping annual data since 1946. Statistics are based on nonoverlapping annual data between 1976 and 2015. The shaded regions are recessions. A year is in recession if any of its months correspond to NBER recession dates. Although all figures change somewhat, the discussions on these patterns do not change.

Original languageEnglish (US)
Pages (from-to)2107-2116
Number of pages10
JournalJournal of Finance
Volume74
Issue number4
DOIs
StatePublished - Aug 1 2019

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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