This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable parameters is asymptotically unaffected by the partial instability. In the empirical analysis of presumably stable parameters—such as structural parameters in Euler conditions—one can thus ignore moderate instabilities in other parts of the model and still obtain approximately correct inference.

We consider a two-sided, finite-horizon search and matching model with heterogeneous types and complementarity between types. The quality of the pool of potential partners deteriorates as agents who have found mutually agreeable matches exit the market. When search is costless and all agents participate in each matching round, the market performs a sorting function in that high types of agents have multiple chances to match with their peers. However, this sorting function is lost if agents incur an arbitrarily small cost in order to participate in each round. With a sufficiently rich type space, the market unravels as almost all agents rush to participate in the first round and match and exit with anyone they meet.

An agent samples projects over time, observing quality for each, while a principal can select at most one. The principal values quality, whereas the agent only wants a project chosen. Transfers are unavailable, but the principal can verify quality by paying a cost. We fully characterize the dynamics of verification by determining optimal mechanisms for this problem. With a low verification cost and a long horizon, the optimal mechanism involves a deterministic selection rule that initially discriminates on quality but chooses a project irrespective of its quality at a deadline. Verification occurs with an intermediate probability before the deadline, declining over time. We show how these conclusions change if the verification cost is high or the horizon is short, and under certain forms of imperfect commitment. Our analysis provides guidelines on how dynamics interact with the benefits of verification.

A seller has a fixed number of goods to sell by a deadline. At each time, he posts a regular price and decides whether to hold a flash sale. Over time, buyers privately enter the market and strategically time their purchases. If a buyer does not purchase when she arrives, she can pay an attention cost to recheck the regular price afterwards, or she can wait for future flash sales where she may obtain a good at a discounted price. In the unique Markov perfect equilibrium, the seller sporadically holds flash sales to lower the stock of goods. A flash sale increases the willingness to pay of future buyers, but decreases the willingness to pay of buyers who arrive early in the game. When it is very likely that a buyer will obtain a good in a flash sale, the seller holds a “big” initial flash sale for all but one unit of the good.

The majority of innovations are developed by multi-sector firms. The knowledge needed to invent new products is more easily adapted from some sectors than from others. We study this network of knowledge linkages between sectors and its impact on firm innovation and aggregate growth. We first document a set of sectoral-level and firm-level observations on knowledge applicability and firms’ multi-sector patenting behaviour. We then develop a general equilibrium model of firm innovation in which inter-sectoral knowledge linkages determine the set of sectors a firm chooses to innovate in and how much R&D to invest in each sector. It captures how firms evolve in the technology space, accounts for cross-sector differences in R&D intensity, and describes an aggregate model of technological change. The model matches new observations as demonstrated by simulation. It also yields new insights regarding the mechanism through which sectoral fixed costs of R&D affect growth.

Motivated by several interesting features of the highway mowing auction data from the Texas Department of Transportation (TDoT), we study three competing procurement auction models with endogenous entry. Our entry and bidding models provide several interesting implications. For the first time, we show that even within an independent private value paradigm, as the number of potential bidders increases, bidders' equilibrium bidding behaviour can become less aggressive, and the expected procurement cost may rise because the “entry effect” is always positive and may dominate the negative “competition effect”. We then develop structural models of entry and bidding corresponding to the three models under consideration, controlling for unobserved auction heterogeneity, and use the recently developed semi-parametric Bayesian estimation method to analyse the data. We select the model that best fits the data, and use the corresponding structural estimates to quantify the “entry effect” and the “competition effect” with regard to the individual bids and the procurement cost.

This article proposes a test for the conditional superior predictive ability (CSPA) of a family of forecasting methods with respect to a benchmark. The test is functional in nature: under the null hypothesis, the benchmark’s conditional expected loss is no more than those of the competitors, uniformly across all conditioning states. By inverting the CSPA tests for a set of benchmarks, we obtain confidence sets for the uniformly most superior method. The econometric inference pertains to testing conditional moment inequalities for time series data with general serial dependence, and we justify its asymptotic validity using a uniform non-parametric inference method based on a new strong approximation theory for mixingales. The usefulness of the method is demonstrated in empirical applications on volatility and inflation forecasting.

An agent may privately learn which aspects of his job are more important by shirking on some of them, and use that information to shirk more effectively in the future. In a model of long-term employment relationship, we characterize the optimal relational contract in the presence of such learning-by-shirking and highlight how the performance measurement system can be managed to sharpen incentives. Two related policies are studied: intermittent replacement of existing measures, and adoption of new ones. In spite of the learning-by-shirking effect, the optimal contract is stationary, and may involve stochastic replacement/adoption policies that dilute the agent’s information rents from learning how to game the system.

We provide new empirical evidence for the way in which financial markets process information. Our results rely critically on high-frequency intraday price and volume data for the S&P 500 equity portfolio and U.S. Treasury bonds, along with new econometric techniques, for making inference on the relationship between trading intensity and spot volatility around public news announcements. Consistent with the predictions derived from a theoretical model in which investors agree to disagree, our estimates for the intraday volume-volatility elasticity around important news announcements are systematically below unity. Our elasticity estimates also decrease significantly with measures of disagreements in beliefs, economic uncertainty, and textual-based sentiment, further highlighting the key role played by differences-of-opinion.

Observed choices between risky lotteries are difficult to reconcile with expected utility maximization, both because subjects appear to be too risk averse with regard to small gambles for this to be explained by diminishing marginal utility of wealth, as stressed by Rabin (2000), and because subjects’ responses involve a random element. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representations of the decision situation. In this model, risk aversion results from a sort of perceptual bias—but one that represents an optimal decision rule, given the limitations of the mental representation of the situation. We propose a quantitative model of the noisy mental representation of simple lotteries, based on other evidence regarding numerical cognition, and test its ability to explain the choice frequencies that we observe in a laboratory experiment.