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This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just-identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n-variable VAR is confined to the set of values that orthogonalize the population variance-covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse-response functions that are implicit in the traditional sign-restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just-identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.

The objective was to identify the firms’ short-run and long-run strategies that contributed to firms’ development, job creation and economic growth of local economies, and employees’ and customers’ satisfaction across all Butler’s stages of development. The results indicate that the firms have been successful supporting environmental sustainability, conservation of natural resources, and protection of cultural elements of local communities. During the Covid-19 shutdown, the female entrepreneurs assessed past strategies, invested in development and production of new and better-quality products and services, advanced employees’ and entrepreneurial skills, and transformation of digital and production infrastructure. The study identified the importance of government policies critical for entrepreneurship success, particularly during global crises. The paper illustrates several lessons focused specifically on fostering a supportive work environment that enables firms to endure through and successfully recover from market shocks or global crises. The study concludes that all female entrepreneurs were experienced, motivated, visionary, goal-oriented, and innovative regarding their entrepreneurial undertakings while focusing on understanding the needs and maximizing employee and customer satisfaction. The resiliency they developed enabled them to stay focused on their goals and maintain successful operations while facing insufficient financial and non-financial support, market challenges, and global crises.

The paper is concerned with inference based on the mean function of a functional time series. We develop a normal approximation for the functional sample mean and then focus on the estimation of the asymptotic variance kernel. Using these results, we develop and asymptotically justify testing procedures for the equality of means in two functional samples exhibiting temporal dependence. Evaluated by means of a simulation study and application to a real data set, these two-sample procedures enjoy good size and power in finite samples.

This paper studies the role of private sector companies in the development of local amenities. We use evidence from one of the largest multinationals of the 20th century: the United Fruit Company (UFCo). The firm was given a large land concession in Costa Rica—one of the so-called “Banana Republics”—from 1899 to 1984. Using administrative census data with census-block geo-references from 1973 to 2011, we implement a geographic regression discontinuity design that exploits a land assignment that is orthogonal to our outcomes of interest. We find that the firm had a positive and persistent effect on living standards. Company documents explain that a key concern at the time was to attract and maintain a sizable workforce, which induced the firm to invest heavily in local amenities—like the development of education and health infrastructure—that can account for our result. Consistent with this mechanism, we show, empirically and through a proposed model, that the firm’s investment efforts increase with worker mobility.

Heteroskedasticity- and autocorrelation-robust (HAR) inference in time series regression typically involves kernel estimation of the long-run variance. Conventional wisdom holds that, for a given kernel, the choice of truncation parameter trades off a test’s null rejection rate and power, and that this tradeoff differs across kernels. We formalize this intuition: using higher-order expansions, we provide a unified size-power frontier for both kernel and weighted orthonormal series tests using nonstandard “fixed-b” critical values. We also provide a frontier for the subset of these tests for which the fixed-b distribution is t or F. These frontiers are respectively achieved by the QS kernel and equal-weighted periodogram. The frontiers have simple closed-form expressions, which show that the price paid for restricting attention to tests with t and F critical values is small. The frontiers are derived for the Gaussian multivariate location model, but simulations suggest the qualitative findings extend to stochastic regressors.

This paper shows how to use realized kernels to carry out efficient feasible inference on the ex post variation of underlying equity prices in the presence of simple models of market frictions. The weights can be chosen to achieve the best possible rate of convergence and to have an asymptotic variance which equals that of the maximum likelihood estimator in the parametric version of this problem. Realized kernels can also be selected to (i) be analyzed using endogenously spaced data such as that in data bases on transactions, (ii) allow for market frictions which are endogenous, and (iii) allow for temporally dependent noise. The finite sample performance of our estimators is studied using simulation, while empirical work illustrates their use in practice.

The long-run abnormal returns following both stock repurchases and seasoned equity offerings disappear for the events in 2003–2012. The disappearance is associated with the changing market environment: increased institutional investment, decreased trading costs, improved liquidity, and enhanced regulations on corporate governance and information disclosure. In response to the more efficient pricing of stocks, firms become less opportunistic in stock repurchases and offerings. Recent events of stock repurchases and offerings are motivated more by business-operating reasons than to exploit mispricing. Both external market factors and internal firm factors contribute to the disappearance of the postevent abnormal returns. Our findings on the recent events contrast with those of earlier studies and shed light on how the changing market environment affects both asset pricing and corporate behavior. This paper was accepted by Wei Jiang, finance.

This paper shows that robust inference under weak identification is important to the evaluation of many influential macro asset pricing models, including (time-varying) rare-disaster risk models and long-run risk models. Building on recent developments in the conditional inference literature, we provide a novel conditional specification test by simulating the critical value conditional on a sufficient statistic. This sufficient statistic can be intuitively interpreted as a measure capturing the macroeconomic information decoupled from the underlying content of asset pricing theories. Macro-finance decoupling is an effective way to improve the power of the specification test when asset pricing theories are difficult to refute because of a severe imbalance in the information content about the key model parameters between macroeconomic moment restrictions and asset pricing cross-equation restrictions. We apply the proposed conditional specification test to the evaluation of a time-varying rare-disaster risk model and the construction of robust model uncertainty sets.

This paper investigates the long‐term stock price effects and equity risk effects of supply chain disruptions based on a sample of 827 disruption announcements made during 1989–2000. Stock price effects are examined starting one year before through two years after the disruption announcement date. Over this time period the average abnormal stock returns of firms that experienced disruptions is nearly –40%. Much of this underperformance is observed in the year before the announcement, the day of the announcement, and the year after the announcement. Furthermore, the evidence indicates that firms do not quickly recover from the negative effects of disruptions. The equity risk of the firm also increases significantly around the announcement date. The equity risk in the year after the announcement is 13.50% higher when compared to the equity risk in the year before the announcement.

This study examines the persistent slowdown of the U.S. economy since 2007 and argues that it represents a secular stagnation: a structural form of slowdown growth, rather than a cyclical weakness. Despite unprecedented monetary expansion and near-zero interest rates from 2008 to 2022, economic growth failed to return to norms, revealing the limits of conventional policy tools. Using annual growth data from 1949 to 2025, the study employs ANOVA-type models and binary-variable segmentation to identify structural breaks in growth behavior. We distinguish six homogeneous business-cycle intervals and three long-run growth phases, with long-term growth averages of 4%, 3%, and 2%, respectively. One important conclusion is that the decline of growth potential has been a long-run process undeterred by the excessive use of monetary and fiscal tools. This resonates with the Tinbergen Rule that discourages the use of short-run policies to treat long-run economic problems. We refined the concept of secular stagnation and defined it as a long-run structural phenomenon driven by recent socio-economic conditions including slower productivity growth, chronic investment shortfalls, diminishing returns from digital technology, etc. The ongoing stagnation is unlikely to reverse without significant policy reorientation; it needs renewed scholarly and policy attention and requires long-term structural solutions.