This article develops the uniform asymptotic theory for local projection (LP) regression when the true lag order of the model is unknown and potentially infinite. The theory allows for varying degrees of persistence in the data, growing response horizons, and general conditionally heteroskedastic martingale-difference shocks. Based on the theory, we make two main contributions. First, we show that LPs can achieve semiparametric efficiency at a given horizon under classical assumptions on the data, provided that the controlled lag order diverges. Thus, the commonly perceived efficiency loss of LPs can become asymptotically negligible with many controls. Second, we propose LP-based inference procedures for (level and cumulated) impulse responses that possess robustness properties not shared by existing methods. Inference methods using two distinct standard errors are considered. The uniform validity for the first method depends on a zero fourth-order cumulant condition on shocks, while that of the second holds more generally for conditionally heteroskedastic martingale-difference shocks. We propose a bootstrap procedure that improves finite-sample performance and extend the standard error construction to structural responses.

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.

Using rapid expansion of the Beijing subway from 2000 to 2018, we analyze its impact on collaborative matches in innovations. We find that an hour reduction in travel time between a pair of locations in Beijing brought 14.85% to 37.69% increase in collaborated patents. Far-apart location pairs were more affected, and the local average causal response is approximately 34.92% to 82.29%. The effect is mainly driven by increased matches among highly productive innovators. The entry of new innovators, relocation of existing innovators, and collaborations among low-productive innovators also contribute to the increase in collaborative matches, especially in the long run.

We demonstrate that a financial intermediary-based asset pricing model offers a compelling explanation for a new set of conditional moments of equity term structure and convenience yields. The model’s key mechanism is that the time-varying tightness of intermediaries’ leverage constraints drives significant mean reversion in the price of risk. This model guides us in devising a novel empirical methodology to estimate the tightness of these constraints (i.e., the Relative Tightness Index) from cross-sectional returns of various asset classes. Our findings affirm that this measure significantly drives the dynamics of equity yield slope and convenience yields, both empirically and quantitatively.

Empirical support for the hypothesis that closed-end fund discounts are related to overhanging tax liabilities has been mixed. We introduce a new approach to testing this hypothesis by examining changes in discount levels following distributions of dividends and capital gains. Since distributions reduce future shareholder tax liabilities, the tax liability hypothesis implies that closed-end fund discounts should decline following distributions. Focusing on changes in discounts isolates this tax effect by eliminating the impact of other fund-specific factors on discount levels. Our results support the tax liability hypothesis, showing that short-run fluctuations in discounts are directly affected by taxable distributions.

This paper proposes “implied stochastic volatility models” designed to fit option-implied volatility data and implements a new estimation method for such models. The method is based on explicitly linking observed shape characteristics of the implied volatility surface to the coefficient functions that define the stochastic volatility model. The method can be applied to estimate a fully flexible nonparametric model, or to estimate by the generalized method of moments any arbitrary parametric stochastic volatility model, affine or not. Empirical evidence based on S&P 500 index options data show that the method is stable and performs well out of sample.

This paper extends Brock and Durlauf's (2001a, 2001b) binary choice complete network (or group interaction) model with homogeneous rational expectations to a general network model with heterogeneous rational expectations. In our model, individuals will form expectations regarding peers' behaviors taking into account their characteristics. Endogenous, contextual, and correlated effects are all identifiable. Conditions for unique equilibrium are established. For a complete network with heterogeneous rational expectations, multiple equilibria can be characterized by an aggregate scalar index. The empirical results on adolescents' smoking behaviors show significant endogenous and contextual effects, even after controlling for school-grade random effects and school fixed effects.

This paper examines the benefits of having an executive or director elected as a fellow of the Chinese Academies of Sciences and Engineering (i.e., a star academician). Evidence indicates that markets react positively to news of company executives/directors being elected. We further document that having a fellow spurs innovation, brings additional government subsidy, increases Tobin's q, lowers costs of capital, attracts analyst attention, and suppresses audit fees in the years following successful elections. These outcomes appear to be largely driven by fellows who (i) are executives rather than non-executive directors, and (ii) have held no government office.

Previous authors have raised the concern that there could be serious survival bias in the observed U.S. equity premium. Contrary to conventional wisdom, we argue that the survival bias in the U.S. data is unlikely to be significant. To reach this conclusion, we introduce a general framework for modeling survival and derive a mathematical relationship between the ex ante survival probability and the average survival bias. This relationship reveals the fundamental difficulty facing the survival argument: High survival bias requires an ex ante probability of market failure, which seems unrealistically high given the history of world financial markets.

We examine price negotiation in the payment card industry by exploiting a unique merchant-, industry-, and city-level data set. Motivated by the substantial variation in acquirer fees and heterogeneous merchant card transactions, we use Nash bargaining to model the negotiation over the acquirer fee between an acquirer and a merchant. We find that the merchants secure a larger incremental surplus than the acquirer on average. Moreover, merchants might face upward pressure on acquirer fees as the card penetration rate rises over time, and policies that weaken the acquirer's bargaining power could relieve the upward fee pressure.