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The prevalence and stability of marriage has declined in the United States as the economic lives of men and women have converged. Family change has not been uniform, however, and the widening gaps in marital status, relationship stability, and childbearing between socioeconomic groups raise concerns about child well-being in poor families and future inequality. This paper uses data from a recent cohort of young adults – Wave IV of the National Longitudinal Study of Adolescent Health – to investigate whether disparities in cognitive ability and non-cognitive skills contribute to this gap. Blinder–Oaxaca decompositions of differences in key family outcomes across education groups show that, though individual non-cognitive traits are significantly associated with union status, relationship instability, and single motherhood, they collectively make no significant contribution to the explanation of educational gaps for almost all of these outcomes. Measured skills can explain as much as 25 percent of differences in these outcomes by family background (measured by mother’s education), but this effect disappears when own education is added to the model. Both cognitive and non-cognitive skills are strongly predictive of educational attainment but, conditional on education, explain very little of the socioeconomic gaps in family outcomes for young adults.

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.

This paper investigates whether stock‐price indexes of emerging markets can be characterized as random walk (unit root) or mean reversion processes. We implement a panelbased test that exploits cross‐sectional information from seventeen emerging equity markets during the period January 1985 to April 2002. The gain in power allows us to reject the null hypothesis of random walk in favor of mean reversion at the 5 percent significance level. We find a positive speed of reversion with a half‐life of about 30 months. These results are similar to those documented for developed markets. Our findings provide an interesting comparison to existing studies on more matured markets and reduce the likelihood of earlier mean reversion findings as attributable to data mining.

Evidence of mean reversion in U.S. stock prices during the post‐World War II era is mixed. I find that using the standard portfolio formation method to construct size‐sorted portfolios is inadequate for detecting mean reversion. Using alternative portfolio formation methods and additional cross‐sectional power gained from size‐sorted portfolios during the period 1963 to 1998, I find strong evidence of mean reversion in portfolio prices. My findings imply a significantly positive speed of reversion with a half‐life of approximately three and a half years. Parametric contrarian investment strategies that exploit mean reversion outperform buy‐and‐hold and standard contrarian strategies.

This study uses parametric hazard models to investigate duration dependence in US stock market cycles over the January 1929 through December 1992 period. Market cycles are determined using the Beveridge‐Nelson (1981) approach to the decomposition of economic time series. The results show that both real and nominal cycles exhibit positive duration dependence. The implication of this finding is that actual prices revert to their permanent or trend level in a non‐random manner as the cyclical component dissipates over time. This process is consistent with mean reversion in price and suggests that predictable periodicity in market cycles may exist. Only limited evidence is obtained that discrete shifts or trends in mean cycle duration exist. The length of market cycles appears not to have changed over the 1929–92 period.

Okun's law has been proven to be one of the most accepted theories in the macroeconomics field. It describes the relationship between gross domestic product (GDP) and unemployment. Arthur Okun's (1962) study was developed to help apply appropriate macroeconomic policy changes. Though the coefficient has been re‐estimated, Okun's original work states that a one‐percentage point reduction in the unemployment rate would produce approximately 3% more output. This correlation has continuously been scrutinized, its accuracy studied, and the degree of dependency these variables have on one another has been evaluated.

The economics literature on gender has expanded considerably in recent years, fueled in part by new sources of data, including from experimental studies of gender differences in preferences and other traits. At the same time, economists have been developing more realistic models of psychological and social influences on individual choices and the evolution of culture and social norms. Despite these innovations, much of the economics of gender has been left behind, and still employs a reductive framing in which gender gaps in economic outcomes are either due to discrimination or to “choice.” I suggest here that the persistence of this approach is due to several distinctive economic habits of mind – strong priors driven by market bias and gender essentialism, a perspective that views the default economic agent as male, and an oft-noted tendency to avoid complex problems in favor of those that can be modeled simply. I also suggest some paths forward.

The different types of estimators of rational expectations models are surveyed. A key feature is that the model′s solution has to be taken into account when it is estimated. The two ways of doing this, the substitution and errors‐in‐variables methods, give rise to different estimators. In the former case, a generalised least‐squares or maximum‐likelihood type estimator generally gives consistent and efficient estimates. In the latter case, a generalised instrumental variable (GIV) type estimator is needed. Because the substitution method involves more complicated restrictions and because it resolves the solution indeterminacy in a more arbitary fashion, when there are forward‐looking expectations, the errors‐in‐variables solution with the GIV estimator is the recommended combination.