Local to unity limit theory is used in applications to construct confidence intervals (CIs) for autoregressive roots through inversion of a unit root test (Stock (1991)). Such CIs are asymptotically valid when the true model has an autoregressive root that is local to unity (ρ = 1 + c/n), but are shown here to be invalid at the limits of the domain of definition of the localizing coefficient c because of a failure in tightness and the escape of probability mass. Failure at the boundary implies that these CIs have zero asymptotic coverage probability in the stationary case and vicinities of unity that are wider than O(n−1/3). The inversion methods of Hansen (1999) and Mikusheva (2007) are asymptotically valid in such cases. Implications of these results for predictive regression tests are explored. When the predictive regressor is stationary, the popular Campbell and Yogo (2006) CIs for the regression coefficient have zero coverage probability asymptotically, and their predictive test statistic Q erroneously indicates predictability with probability approaching unity when the null of no predictability holds. These results have obvious cautionary implications for the use of the procedures in empirical practice.

This paper conducts an investigation of volatility transmission between stock markets in Hong Kong, Europe and the United States covering the time period from 2000 up to 2011. Using intra-daily data we compute realized volatility time series for the three markets and employ a Heterogeneous Autoregressive Distributed Lag Model as our baseline econometric specification. Motivated by the presence of various crisis events contained in our sample, we detect time-variation and structural breaks in volatility spillovers. Particularly during the financial crisis of 2007, we find effects consistent with the notion of contagion, suggesting strong and sudden increases in the cross-market synchronization of chronologically succeeding volatilities. Investigating the role of mean breaks and conditional heteroskedasticity in the realized volatilities, however, we find the latter to be the main driver of breaks in volatility spillovers. Taking the volatility of realized volatilities into account, we find no evidence of contagion anymore.

Some important puzzles in macro finance can be resolved in a model featuring systematically varying volatility of unpriced shocks to firms׳ earnings. In the data, the correlation between corporate debt and stock market valuations is low. The model accounts for this via the opposing effect of unpriced earnings risk on levered debt and equity prices. The model also explains the low (or nonexistent) risk-reward relation for the market portfolio of levered equity via the opposing effects of unpriced and priced uncertainty (both components of stock volatility) on the levered equity risk premium. Versions of the model calibrated to empirical measures of both types of fundamental risk can quantitatively substantiate these explanations. Variation in residual earning dispersion accounts for a significant fraction of observed disagreement between debt and equity valuations and of realized stock volatility. The implication that the two components of risk should forecast the levered equity risk premium with opposite signs is also supported in the data. The results are a notable advance for risk-based asset pricing.

We structurally estimate a model in which agents’ information processing biases can cause predictability in firms’ asset returns and investment inefficiencies. We generalize the neoclassical investment model by allowing for two biases—overconfidence and overextrapolation of trends—that distort agents’ expectations of firm productivity. Our model's predictions closely match empirical data on asset pricing and firm behavior. The estimated bias parameters are well identified and exhibit plausible magnitudes. Alternative models without either bias or with efficient investment fail to match observed return predictability and firm behavior. These results suggest that biases affect firm behavior, which in turn affects return anomalies.

Several studies document a robust negative association between net external financing and average stock returns, which is referred to as the external financing effect. Using total asset growth as a comprehensive measure of overall corporate investment and total profitability gross of R&D expenditures as a measure of true economic profitability, we provide new evidence in support of the q-theory explanation for the external financing effect. We also test the market timing explanation for the external financing effect but fail to document supportive evidence.

During the downturn of 2008-2009, output and hours fell significantly, but labor productivity rose. These facts have led many to conclude that there is a significant deviation between observations and current macrotheories that assume business cycles are driven, at least in part, by fluctuations in total factor productivities of firms. We show that once investment in intangible capital is included in the analysis, there is no inconsistency. Measured labor productivity rises if the fall in output is underestimated; this occurs when there are large unmeasured intangible investments. Microevidence suggests that these investments are large and cyclically important.

We investigate the impact of excess perk consumption on crash risk in state-owned enterprises in China. To enjoy excess perks, executives in state-owned enterprises have an incentive to withhold bad news for extended periods, leading to higher future stock price crash risk. Consistent with this assertion, we find a positive correlation between excess perks and crash risk. The findings are robust to the inclusion of other determinants of crash risk identified in the literature, such as earnings management, conditional conservatism, and firm-level corporate governance mechanisms. The results still hold after accounting for possible endogeneity issues using a two-stage least squares estimation. Earnings management (conditional conservatism) helps amplify (lessen) this impact. Moreover, better external monitoring mitigates the impact of excess perks on firm crash risk. We further find that the impact of excess perks on crash risk is more pronounced in firms whose executives are approaching retirement and persists for at least two years.

We propose robust methods for inference about the effect of a treatment variable on a scalar outcome in the presence of very many regressors in a model with possibly non-Gaussian and heteroscedastic disturbances. We allow for the number of regressors to be larger than the sample size. To make informative inference feasible, we require the model to be approximately sparse; that is, we require that the effect of confounding factors can be controlled for up to a small approximation error by including a relatively small number of variables whose identities are unknown. The latter condition makes it possible to estimate the treatment effect by selecting approximately the right set of regressors. We develop a novel estimation and uniformly valid inference method for the treatment effect in this setting, called the “post-double-selection†method. The main attractive feature of our method is that it allows for imperfect selection of the controls and provides confidence intervals that are valid uniformly across a large class of models. In contrast, standard post-model selection estimators fail to provide uniform inference even in simple cases with a small, fixed number of controls. Thus, our method resolves the problem of uniform inference after model selection for a large, interesting class of models. We also present a generalization of our method to a fully heterogeneous model with a binary treatment variable. We illustrate the use of the developed methods with numerical simulations and an application that considers the effect of abortion on crime rates.

The unit values of internationally traded goods are heavily influenced by quality. We model this in an extended monopolistic competition framework where, in addition to choosing price, firms simultaneously choose quality subject to nonhomothetic demand. We estimate quality and quality-adjusted price indexes for 185 countries over 1984–2011. Our estimates are less sensitive to assumptions about the extensive margin of firms than are purely “demand-side” estimates. We find that quality-adjusted prices vary much less across countries than do unit values and, surprisingly, the quality-adjusted terms of trade are negatively related to countries’ level of income.

Cities are neither completely specialized nor completely diverse. However, prior research has focused almost entirely on the polar cases of complete specialization and complete diversity. This paper develops a model that can also generate the intermediate case of cities that feature the coagglomeration of some but not all industries, thus giving theoretical foundations to the analysis of business clusters. The analysis sharply challenges the conventional wisdom that the size and composition of cities are necessarily driven primarily by agglomerative efficiencies.