[A crucial issue in asset pricing is to understand the relative importance of discount rate (DR) news and cash flow (CF) news in driving the time-series and cross-sectional variations of stock returns. Many studies directly estimate the DR news but back out the CF news as the residual. We argue that this approach has a serious limitation because the DR news cannot be accurately measured due to the small predictive power, and the CF news, as the residual, inherits the large misspecification error of the DR news. We apply this residualbased decomposition approach to Treasury bonds and equities and find results that are either counterintuitive or unrobust. Potential solutions, including modeling both DR news and CF news directly, the Bayesian model averaging approach, and the principal component analysis, are explored.]

In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through discrete variation under a non-periodicity condition on the hazard function of the error distribution. Furthermore, we show that the presence of continuous regressors may lead to stronger identification results. Our non-parametric estimator is shown to be consistent and asymptotically normal, and outperforms that of Lewbel and Linton (2002) in a simulation study. For the panel data setting, we provide the first systematic treatment of non-parametric identification and estimation of the truncated panel data model with fixed effects by extending our treatment of the cross-sectional case. We also consider various other extensions.

We examine the effects of regulations designed to address the potential conflict of interest that arises when sell-side analyst research is not independent of investment banking. We focus on two types of regulation: (1) internal barriers between equity research and investment banking that restrict communication; and (2) disclosure requirements relating to analyst compensation. We find that information barriers can increase research effort and improve report quality by limiting an investment bank's ability to distort its analyst's incentives. However, this type of regulation can also reduce information production and lower the quality of reports if an investment bank benefits directly from research activity. Disclosure requirements, on the other hand, unambiguously lead to more informative prices and a higher report quality relative to either information barriers or no regulation.

A disconcerting, albeit generally accepted, finding is that aggregate stock returns are predictable by dividend yield but dividend growth is unpredictable. I show that part of this lack of dividend growth predictability stems from how dividend growth is constructed. I then show a dramatic reversal of predictability in the 134 years during 1872–2005: stock returns are largely unpredictable in the first seven decades, but become predictable in the postwar period; dividend growth is strongly predictable in the prewar years but this predictability disappears in the postwar years. New evidence on the predictability of long-run returns and dividend growth is also shown.

The Impact of Unexpected Maternal Death on Education: First Evidence from Three National Administrative Data Links by Stacey H. Chen, Yen-Chien Chen and Jin-Tan Liu. Published in volume 99, issue 2, pages 149-53 of American Economic Review, May 2009

This study provides evidence of changes in how analysts generate stock recommendations after the SEC's approval of NASD Rule 2711 in May 2002, which introduced regulatory reforms to enhance the independence of analysts' research. We investigate the relations of analysts' stock recommendations with intrinsic value estimates (based on analysts' earnings forecasts relative to the stock prices, V/P) and with investment-banking-related conflicts of interest during the 1994–2005 period. We find a stronger relation between analysts' stock recommendations and V/P and a weaker relation between analysts' stock recommendations and conflicts of interest in the post-Rule period than prior to the implementation of the Rule. Moreover, the increases in the relation between stock recommendations and V/P after the implementation of the Rule are greater for the stocks recommended by analysts with greater potential conflicts of interest. Our findings suggest that the implementation of Rule 2711 has enhanced analysts' independence.

This paper investigates whether macroeconomic variables can predict recessions in the stock market, i.e., bear markets. Series such as interest rate spreads, inflation rates, money stocks, aggregate output, unemployment rates, federal funds rates, federal government debt, and nominal exchange rates are evaluated. After using parametric and nonparametric approaches to identify recession periods in the stock market, we consider both in-sample and out-of-sample tests of the variables’ predictive ability. Empirical evidence from monthly data on the Standard & Poor’s S&P 500 price index suggests that among the macroeconomic variables we have evaluated, yield curve spreads and inflation rates are the most useful predictors of recessions in the US stock market, according to both in-sample and out-of-sample forecasting performance. Moreover, comparing the bear market prediction to the stock return predictability has shown that it is easier to predict bear markets using macroeconomic variables.

February 01 2009 Estimating the Variance of Wages in the Presence of Selection and Unobserved Heterogeneity Stacey H Chen Stacey H Chen Search for other works by this author on: This Site Google Scholar Author and Article Information Stacey H Chen Online ISSN: 1530-9142 Print ISSN: 0034-6535 Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology2009 The Review of Economics and Statistics (2009) 91 (1): 227. https://doi.org/10.1162/rest.91.1.227 Connected Content This is a correction to: Estimating the Variance of Wages in the Presence of Selection and Unobserved Heterogeneity Cite Icon Cite Permissions Share Icon Share Facebook Twitter LinkedIn Email Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation Stacey H Chen; Estimating the Variance of Wages in the Presence of Selection and Unobserved Heterogeneity. The Review of Economics and Statistics 2009; 91 (1): 227. doi: https://doi.org/10.1162/rest.91.1.227 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Review of Economics and Statistics Search Advanced Search View Original Article Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology2009 Article PDF first page preview Close Modal You do not currently have access to this content.

[Structural models of default calibrated to historical default rates, recovery rates, and Sharpe ratios typically generate Baa-Aaa credit spreads that are significantly below historical values. However, this "credit spread puzzle" can be resolved if one accounts for the fact that default rates and Sharpe ratios strongly covary; both are high during recessions and low during booms. As a specific example, we investigate credit spread implications of the Campbell and Cochrane (1999) pricing kernel calibrated to equity returns and aggregate consumption data. Identifying the historical surplus consumption ratio from aggregate consumption data, we find that the implied level and time variation of spreads match historical levels well.]