This paper proposes a new mixed vector autoregression (MVAR) model to examine the relationship between aggregate time series and functional variables in a multivariate setting. The model facilitates a reexamination of the oil-stock price nexus by estimating the effects of demand and supply shocks from the global market for crude oil on the entire distribution of U.S. stock returns since the late 1980s. We show that the MVAR effectively extracts information from the returns distribution that is more relevant for understanding the oil-stock price nexus beyond simply looking at the first few moments. Using novel functional impulse response functions (FIRFs), we find that oil market demand shocks tend to increase returns, while both demand and supply shocks reduce volatility, and have an asymmetric effect on the returns distribution as a whole. In a value-at-risk (VaR) analysis, we also find that the oil market contains important information that reduces expected loss, and that the response of VaR to the oil market demand and supply shocks has changed over time.
Traditional dynamic discrete choice model-Conditional Choice Probability estimator (DDCM-CCP) literature considered identification using the likelihood of longitudinal choice only. However, such an approach has a limitation in identifying flexible dynamics for latent state due to the discrete and typically small-dimensional nature of choice. This paper extends this literature to utilize imperfect measurements, called proxies, for a latent discrete state. I first show that proxies improve identification and discuss how survey design on proxies affects identification conditions. I then extend the estimator from Arcidiacono and Miller (2011) to pool information from an unbalanced panel of noisy proxies, enabling estimation of more flexible latent state dynamics than Markov chains. As an application, I estimate a dynamic model of labor supply and mental health, where dynamic mental health only imperfectly observed-plays a central role. The results reveal more complex dynamics than a standard Markov chain.
This paper studies approximation error in the quasilinear utility model. Error arises because individuals do not perfectly optimize, and instead satisfice. We investigate the consequences of individual satisficing for modeling aggregate demand, providing an approximate aggregation theorem. We present a simple method for statistical inference on the minimal level of satisficing needed to explain aggregate data. In an illustrative application to scanner panel data, we find that individual-level data require a nontrivial degree of satisficing, but aggregate data admit a representative agent that maximizes a quasilinear utility function.
Linear factor models are generally not identified. We provide sufficient conditions for identification: Under a natural sparsity assumption (the presence of local factors that affect only subsets of observables), the true loading matrix is the sparsest rotation and can be recovered by minimizing the & ell; 1-norm of the loading matrix. This enables economically meaningful interpretation of the individual factors. More generally, our & ell; 1-rotation criterion offers a novel approach to simplify the loading matrix and performs well relative to existing methods (e.g., Varimax, Kaiser (1958)) in our simulations. We illustrate our method in two economic applications. The R package l1rotation implements the method and facilitates adoption.
I build a model of many-to-one matching with nontransferable utility involving many agents on both sides of the market, for example, workers and firms. Under parsimonious assumptions on preferences and assuming that matches are stable, I provide a tractable asymptotic characterization of the joint distribution of match characteristics. I find that one can identify the joint surplus of a match, but cannot separately identify workers' and firms' preferences from data on realized matches. Within-firm variation in matched worker characteristics, only available in many-to-one matching data, can help identifying and estimating unobserved firm heterogeneity in the surplus function.
