Essays on Information Frictions, Prices and Unemployment

I investigate the effects of information frictions in price setting decisions. I show that firms' output prices and wages are less sensitive to aggregate economic conditions when firms and workers cannot perfectly understand (or know) the aggregate state of the economy. Prices and wages respond with a lag to aggregate innovations because agents learn slowly about those changes, and this delayed adjustment in prices makes output and unemployment more sensitive to aggregate shocks. In the first chapter of this dissertation, I show that workers' noisy information about the state of the economy help us to explain why real wages are sluggish. In the context of a search and matching model, wages do not immediately respond to a positive aggregate shock because workers do not (yet) have enough information to demand higher wages. This increases firms' incentives to post more vacancies, and it makes unemployment volatile and sensitive to aggregate shocks. This mechanism is robust to two major criticisms of existing theories of sluggish wages and volatile unemployment: the flexibility of wages for new hires and the cyclicality of the opportunity cost of employment. Calibrated to U.S. data, the model explains 60% of the overall unemployment volatility. Consistent with empirical evidence, the response of unemployment to TFP shocks predicted by my model is large, hump-shaped, and peaks one year after the TFP shock, while the response of the aggregate wage is weak and delayed, peaking after two years. In the second chapter of this dissertation, I study the role of information frictions and inventories in firms' price setting decisions in the context of a monetary model. In this model, intermediate goods firms accumulate output inventories, observe aggregate variables with one period lag, and observe their nominal input prices and demand at all times. Firms face idiosyncratic shocks and cannot perfectly infer the state of nature. After a contractionary nominal shock, nominal input prices go down, and firms accumulate inventories because they perceive some positive probability that the nominal price decline is due to a good productivity shock. This prevents firms' prices from decreasing and makes current profits, households' income, and aggregate demand go down. According to my model simulations, a 1% decrease in the money growth rate causes output to decline 0.17% in the first quarter and 0.38% in the second followed by a slow recovery to the steady state. Contractionary nominal shocks also have significant effects on total investment, which remains 1% below the steady state for the first 6 quarters.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

Cortisol Reactivity and Observed Parenting among Mothers of Children with and without ADHD

Parenting is a robust predictor of developmental outcomes among children with ADHD. Early parenting predicts the persistence and course of ADHD and comorbid problems above and beyond risk associated with shared genetic effects. Yet, on average, mothers of children with ADHD are less positive and more negative in their parent-child interactions compared to mothers of non-disordered children. Little is known about psychobiological markers which may be associated with individual variations in maternal parenting in families of children with ADHD. Neurobiological models of parenting suggest that maternal cortisol levels following a stressor may be positively associated with hostile and intrusive parenting; however, to date no studies have examined maternal cortisol reactivity and parenting in school-age, or clinical samples of, children. Mothers’ regulation of physiological stress responses may be particularly important for families of children with ADHD, as parenting a child with chronically challenging behaviors represents a persistent environmental stressor. The current study sought to extend the existing literature by providing an empirical examination of the relationship between maternal cortisol reactivity following two laboratory stressors and parenting among mothers of children with and without ADHD. It was hypothesized that child ADHD group would moderate the relationship between cortisol reactivity and self-reported and observed parenting. Greater total cortisol output and greater increase in cortisol during the TSST were associated with decreased positive parenting and increased negative and directive parenting, with the exception of parental involvement, which was associated with increased cortisol output during the TSST. Conversely, cortisol output during the PCI was associated with increased positive parenting, increased parental involvement, and decreased negative parenting. In contrast to the TSST, a greater decrease in cortisol during the PCI indicated more positive parenting and parental involvement. These associations were specific to mothers of children with ADHD, with the exception of maternal directiveness, which was specific to comparison mothers. Findings add to our understanding of physiological processes associated with maternal parenting and contribute to an integrative biological, psychological, and cognitive process model of parenting in families of children with ADHD.