Essays on Incomplete Information, Model Uncertainty, and Macroeconomic Policy PDF Download

Author: Giacomo Rondina
Publisher:
ISBN:
Category :
Languages : en
Pages : 204

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Author: Jean-Jacques Laffont
Publisher: Harvard University Press
ISBN: 9780674265554
Category : Business & Economics
Languages : en
Pages : 160

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These three elegant essays develop principles central to the understanding of the diverse ways in which imperfect information affects the distribution of resources, incentives, and the evaluation of economic policy. The first concerns the special role that information plays in the allocation process when it is possible to improve accuracy through private investment. The common practice of hiring "experts" whose information is presumably much better than their clients' is analyzed. Issues of cooperative behavior when potential group members possess diverse pieces of information are addressed. Emphasis is placed on the adaptation of the "core" concept from game theory to the resource allocation model with differential information. The second essay deals with the extent to which agents can influence the random events they face. This is known as moral hazard, and in its presence there is a potential inefficiency in the economic system. Two special models are studied: the role of moral hazard in a monetary economy, and the role of an outside adjudicatory agency that has the power to enforce fines and compensation. The final essay discusses the problem of certainty equivalence in economic policy. Conditions under which a full stochastic optimization can be calculated by solving a related, much simpler "certainty equivalence" problem are developed. The reduction in the complexity of calculation involved is very great compared with the potential loss of efficiency.

Author: Mingjun Zhao
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 76

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Abstract: My dissertation grapples with the issues of model uncertainty in macroeconomics, and analyzes its consequences for monetary policy. It consists of three essays. In the first essay (Chapter 1), "Monetary Policy under Misspecified Expectations", I examine policy choices for the central bank that faces uncertainty about the process of expectation formation by economic agents. The economy contains both "rule-of-thumb" agents who base their expectations on recent observations and agents who have rational expectations. The central bank is uncertain about the fraction of the rule-of-thumb agents. This uncertainty concern enables me to partially rationalize the over cautious policy stance of the Fed: empirically observed policy in the past two decades involves much weaker responses than optimal policies derived from various micro-founded models. It is well understood that when the economy is more forward-looking, the central bank displays more aggressive responses to inflation and output. But the uncertainty-averse central bank evaluates policies by the performance in the worst case. In my economy this has a high fraction of agents that are backward-looking. The best policy the central bank chooses thus involves moderate responses. That is to say, this minimax policy moves closer toward actual less responsive policy. In the second essay (Chapter 2), "Phillips Curve Uncertainty and Monetary Policy", I investigate the effect of model uncertainty on policy choices employing a more general approach, which nests the minimax and Bayesian approaches as limiting cases. The central bank is uncertain about whether the economy has a sticky price Phillips curve or a sticky information Phillips curve. I argue that how the central bank chooses a policy depends both on its perception of uncertainty environment and on its attitude towards uncertainty. I find that as the central bank either becomes more uncertainty-averse or considers sticky information more plausible, the response to inflation decreases and to output increases. The third essay (Chapter 3) is entitled "Optimal Simple Rules in RE Models with Risk Sensitive Preferences". This paper provides a useful method to solve optimal simple rules under risk sensitive preference in macro models with forward looking behavior. An application to a new Keynesian model with lagged dynamics is offered and risk sensitive preference is found to amplify policy responses.

Author: Sylverie Herbert
Publisher:
ISBN:
Category :
Languages : en
Pages : 112

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This dissertation contains three essays addressing issues pertaining to macroeconomic policies in presence of imperfect and heterogeneous information. Chapter 1 studies how central banks should design communication as a function of the economic fundamentals and the private sector's heterogeneous beliefs about these fundamentals. Chapter 2 examines how the Federal Open Market Committee's state-dependent topics coverage may affect expectations about future monetary policy. Chapter 3 measures the impact of uncertainty about fiscal policy on financial markets. Macroeconomic decisions involve expectations about the state of the economy and the private sector relies on information provided by central banks to form these expectations. Central banks therefore have a central role in shaping these expectations. Chapter 1 presents a model in which a central bank has incentives to use communication strategically to shape expectations so that the private sector takes a specific action regardless of the fundamentals. In this chapter, I formalize these strategic motives to communicate differently across states in a Bayesian persuasion game with heterogeneous receivers. A Sender communicates about a binary fundamental to Receivers, who holds heterogeneous beliefs about the state. The Sender wants them to take a specific action regardless of the fundamental but Receivers want to align their action with the fundamental. I derive the Sender's optimal disclosure strategy about the fundamentals as a function of both the fundamentals and the Receivers' disagreement. Then, I apply this framework to a central bank communication problem and test empirically the predictions in the model using one example of communication, the Fed's forecasts. I show that a central bank would want to send moderating signals (reporting the fundamental in either state with positive error probabilities), but the reporting accuracy increases with private sector disagreement. The second chapter analyzes the extent of state-dependent coverage of topics by the FOMC. A topic's prevalence could affect expectations in two ways: first, it provides information about the fundamental but the prevalence can also provide information about how extreme the realization is. I first document, applying computational linguistics methods to FOMC minutes, that a topics' newsworthiness varies over time and depends on both variation and level of its related macroeconomic variables: negative outcomes such as high inflation, low output, high unemployment make their associated topics more newsworthy. This suggests that the minutes are potentially an informative source about what the central bank is concerned about, and thus likely to react to. I then develop a model in which this state dependent composition (unusual number of signals about a fundamental) impacts agents' expectations about both the state of the economy and the interest rate, therefore generating a signaling effect about an interest rate change. Taking into account this signaling effect of the mix of topics, I aim to derive the optimal state contingent communication policy. The third chapter, co-authored with Yu She, turns to uncertainty and how disagreement or uncertain communication from policy makers can impact financial markets. We investigate the impact of uncertainty about fiscal policy on nominal yields, such as the fiscal cliff episode of 2012 and government shutdown of 2013. Both episodes were marked by an intense debate on Twitter between politicians. We gather tweets from politicians and government agencies during the period January 2012 to December 2015 which are related to a potential shutdown. We use sentiment analysis such as dictionary methods to measure uncertainty and negative sentiment to create a proxy for government policy uncertainty. Regressing this proxy and dummies for FOMC meetings on nominal yields at daily frequency, we find that an increase in disagreement or uncertainty portrayed through the tweets has a negative impact on nominal yields (3-month to 1-year maturity).

Author: Andras Komaromi
Publisher:
ISBN:
Category :
Languages : en
Pages : 154

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This dissertation is a compilation of three essays on the role of information frictions in macroeconomics. The first essay contributes to the literature on the impact of uncertainty on the business cycle. The cross-sectional dispersion of firm-level outcomes, such as sales growth or stock returns, is markedly countercyclical. Recent papers have framed this fact as evidence that exogenous "uncertainty shocks" are important drivers of business cycles. This paper provides empirical evidence that the co-movement of various dispersion measures with the business cycle is better understood as the economy's endogenous response to traditional first moment shocks - dispersion is the effect, not the cause. It then develops a theoretical model that links the cross-sectional dispersion of micro-level outcomes to the aggregate state of the economy. The mechanism is based on time-varying rational inattention. In bad times, firms pay more attention to idiosyncratic shocks hitting their business environment. More precise micro- level information about the underlying heterogeneity leads to higher dispersion in realized outcomes. In line with the empirical findings, the model generates countercyclical dispersion without relying on exogenous second moment (uncertainty) shocks. The second essay uses survey expectations to assess the microfoundations of an important class of macroeconomic models. Many theoretical macro models try to explain the pervasive nominal and real stickiness in the data by assuming rational decision-making under imperfect information. The behavior of consensus (average) forecasts is consistent with the predictions of these models, which can be seen as supportive empirical evidence for the models' microfoundations (Coibion and Gorodnichenko, 2012). This paper demonstrates, however, that the individual-level data underlying the consensus forecasts are at odds with this interpretation. In particular, I document that individual expectations in the Survey of Professional Forecasters do not pass a very weak test of rational expectations: current forecast revisions are strong predictors of subsequent forecast errors. Information frictions alone cannot explain this pattern. I go on to propose a simple modification of the noisy information framework that allows for a particular form of non-rational expectations: agents may incorrectly weight new information against their prior. I show that this parsimonious model can match the survey data along several dimensions. Using the structure of the model, I estimate the direction and size of inefficiencies in the expectations formation process. I find that in most cases agents put too much weight on their private information, which can be interpreted as overconfidence in the precision of private information. I also show that there is substantial heterogeneity across agents in the deviation from rational expectations, and I relate these differences to observable characteristics. Finally, I discuss potential interpretations of my empirical results and their implications for macroeconomic theory. The third essay explores the potential trade-off between competition and systemic stability in financial intermediation. Why do banks feel compelled to operate with such high leverage despite the risks this poses? Using a simple model, I argue that the degree of competition goes a long way in explaining capital structure decisions. On the one hand, information frictions (adverse selection) render debt a cheaper form of financing than equity. On the other hand, more reliance on debt increases the probability of bankruptcy, which results in the loss of the bank's charter value. The degree of competition affects charter values, and hence changes the way banks balance between these two forces. A panel analysis of European banks' capital structure around the introduction of the euro reveals statistically and economically significant effects consistent with this hypothesis. Banks, in particular smaller banks, decreased their equity ratios after entering the currency area. Complementary evidence suggests that this effect can be attributed to increased competitive pressures boosted by the euro.

Author: Matti Pohjola
Publisher:
ISBN: 9789514411809
Category : Business cycles
Languages : en
Pages : 374

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Author: Jennifer La'O
Publisher:
ISBN:
Category :
Languages : en
Pages : 220

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This dissertation consists of four chapters analyzing the effects of heterogeneous and asymmetric information in macroeconomic and financial settings, with an emphasis on short-run fluctuations. Within these chapters, I study the implications these informational frictions may have for the behavior of firms and financial institutions over the business cycle and during crises episodes. The first chapter examines how collateral constraints on firm-level investment introduce a powerful two-way feedback between the financial market and the real economy. On one hand, real economic activity forms the basis for asset dividends. On the other hand, asset prices affect collateral value, which in turn determines the ability of firms to invest. In this chapter I show how this two-way feedback can generate significant expectations-driven fluctuations in asset prices and macroeconomic outcomes when information is dispersed. In particular, I study the implications of this two-way feedback within a micro-founded business-cycle economy in which agents are imperfectly, and heterogeneously, informed about the underlying economic fundamentals. I then show how tighter collateral constraints mitigate the impact of productivity shocks on equilibrium output and asset prices, but amplify the impact of "noise", by which I mean common errors in expectations. Noise can thus be an important source of asset-price volatility and business-cycle fluctuations when collateral constraints are tight. The second chapter is based on joint work with George-Marios Angeletos. In this chapter we investigate a real-business-cycle economy that features dispersed information about underlying aggregate productivity shocks, taste shocks, and-potentially-shocks to monopoly power. We show how the dispersion of information can (i) contribute to significant inertia in the response of macroeconomic outcomes to such shocks; (ii) induce a negative short-run response of employment to productivity shocks; (iii) imply that productivity shocks explain only a small fraction of high-frequency fluctuations; (iv) contribute to significant noise in the business cycle; (v) formalize a certain type of demand shocks within an RBC economy; and (vi) generate cyclical variation in observed Solow residuals and labor wedges. Importantly, none of these properties requires significant uncertainty about the underlying fundamentals: they rest on the heterogeneity of information and the strength of trade linkages in the economy, not the level of uncertainty. Finally, none of these properties are symptoms of inefficiency: apart from undoing monopoly distortions or providing the agents with more information, no policy intervention can improve upon the equilibrium allocations. The third chapter is also based on joint work with George-Marios Angeletos. This chapter investigates how incomplete information affects the response of prices to nominal shocks. Our baseline model is a variant of the Calvo model in which firms observe the underlying nominal shocks with noise. In this model, the response of prices is pinned down by three parameters: the precision of available information about the nominal shock; the frequency of price adjustment; and the degree of strategic complementarity in pricing decisions. This result synthesizes the broader lessons of the pertinent literature. However, this synthesis provides only a partial view of the role of incomplete information: once one allows for more general information structures than those used in previous work, one cannot quantify the degree of price inertia without additional information about the dynamics of higher-order beliefs, or of the agents' forecasts of inflation. We highlight this with three extensions of our baseline model, all of which break the tight connection between the precision of information and higher-order beliefs featured in previous work. Finally, the fourth chapter studies how predatory trading affects the ability of banks and large trading institutions to raise capital in times of temporary financial distress in an environment in which traders are asymmetrically informed about each others' balance sheets. Predatory trading is a strategy in which a trader can profit by trading against another trader's position, driving an otherwise solvent but distressed trader into insolvency. The predator, however, must be sufficiently informed of the distressed trader's balance sheet in order to exploit this position. I find that when a distressed trader is more informed than other traders about his own balances, searching for extra capital from lenders can become a signal of financial need, thereby opening the door for predatory trading and possible insolvency. Thus, a trader who would otherwise seek to recapitalize is reluctant to search for extra capital in the presence of potential predators. Predatory trading may therefore make it exceedingly difficult for banks and financial institutions to raise credit in times of temporary financial distress.

Author: Ponpoje Porapakkarm
Publisher:
ISBN:
Category :
Languages : en
Pages : 356

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Author: Yizhou Kuang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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This dissertation consists of three essays that delve into the intersection of econometrics and macroeconomics. The essays employ econometric tools to investigate various topics related to macroeconomic forecasting and policy-making. The first essay aims to help policy-makers conduct robust inference on parameters that may suffer identification issues from DSGE models, and perform reliable counterfactual analysis based on available macroeconomic indicators. The second essay from a non-structural perspective, explores how to optimally forecast these variables in real-time utilizing available macroeconomic variables under model uncertainty. The last essay looks at Survey of Professional Forecasters and studies how agents update their beliefs based on common and private signals during business cycles.The first chapter introduces a new algorithm to conduct robust Bayesian estimation and inference in dynamic stochastic general equilibrium models. The algorithm combines standard Bayesian methods with an equivalence characterization of model solutions. This algorithm allows researchers to perform the following analysis: First, find the complete range of posterior means of both the deep parameters and any parameters of interest robust to the choice of priors in a sense I make precise. Second, derive the robust Bayesian credible region for these parameters. I prove the validity of this algorithm and apply this method to the models in Cochrane (2011) and An and Schorfheide (2007) to achieve robust estimations for structural parameters and impulse responses. In addition, I conduct a sensitivity analysis of optimal monetary policy rules with respect to the choice of priors and provide bounds to the optimal Taylor rule parameters.In the second chapter, my coauthors Yongmiao Hong, Yuying Sun and I focus on real-time monitoring of economic activities, also known as nowcasting. Nowcasting can be particularly challenging in the era of Big Data because it requires the management of a substantial amount of time series data that exhibit different frequencies and release dates. In this paper, we propose a novel now-casting strategy that utilizes dynamic factor models, which we call leave-b-out forward validation model averaging with penalization (LboFVMA). We demonstrate that the selected weight converges asymptotically to an optimal and consistent estimator, even in cases where all candidate models are misspecified. Further-more, the proposed estimator is consistent and follows an asymptotic Gaussian distribution if the true model is included among the candidate models. Our simulation results demonstrate that the LboFVMA approach performs well, as it generates low mean square forecast errors. This highlights its effectiveness and accuracy in the field of nowcasting.In the third chapter, my coauthors Nathan Mislang, Kristoffer Nimark and I propose a method to empirically decompose a cross-section of observed belief revisions into components driven by private and common signals under weak assumptions. We define a common signal as the single signal that if observed by all agents can explain the maximum amount of belief revisions across agents. Private signals are defined to explain the residual belief revisions unaccounted for by the common signal. When applied to probability forecasts from the Survey of Professional Forecasters we find that private signals account for more of the observed belief revisions than common signals. There is a large cross-sectional heterogeneity in signal precision across forecasters, with about 1/2 of them observing private signals that are less precise than the common signal. Unconditionally, the precision of private and common signals are positively correlated, suggesting that private and common information are complements. Inflation volatility, perceived stock market volatility and a high risk of recession are all factors associated with increased informativeness and precision of both private and common signals. Disagreement between the private and common signals can partly explain increases in uncertainty about macro variables. We discuss the implications of our findings for theoretical models of information acquisition.

Author: Jeffrey A. Levy
Publisher:
ISBN:
Category : Macroeconomics
Languages : en
Pages : 90

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In this dissertation I examine economic uncertainty, particularly from the perspective of disaggregation below the national level. In part one I outline the building of news-based uncertainty measures for all 50 US states plus Washington DC. I analyze different search specifications, finding that the simplest set of terms involving one group for "economy" and one group for "uncertainty" has the highest resolution, while yielding similar results to more focused news searches, including one similar to the ubiquitous policy search from Baker et al. (2016), and an alternative specification designed to eliminate false positives. I also explore the differences between two other national uncertainty measures - the VIX stock market volatility index from the Chicago Board Options Exchange, and the unforecastable macroeconomic factors from Jurado et al. (2015). In part two, I build upon the analysis of the state uncertainty measures begun in part one. I show that analyzing uncertainty at the national level obscures significant state-level variation, with state-to-national uncertainty correlations ranging from 0.124 to 0.913, while some inter-state uncertainty correlations even turn negative. I then show that VAR analysis using state-level unemployment figures yields impulse response functions that are remarkably similar to existing national-level uncertainty research, with 92% of states exhibiting a rise in unemployment that peaks near 12 months after an uncertainty shock, then overshoots the starting point for a time. Finally, in part three I attempt to get at the causal effects of an uncertainty shock, as VAR analysis is unable to do, by applying a difference-in-difference framework to the natural experiment of military base closures. I look at the 1991, 1993, 1995, and 2005 rounds of the Base Realignment and Closure (BRAC) process, whereby the government moves military jobs in and out of bases through a process that is, at least initially, heavily insulated from confounding economic indicators and political influence. This creates asymmetric and exogenous uncertainty shocks in places with different military employment, which I use to show that a one-percentage point higher military share of employment causes a tenth of a percentage point higher unemployment rate in a given Metropolitan Statistical Area (MSA) during a BRAC round.