This dissertation studies the role of bounded rationality in New Keynesian models. In particular, I focus on ``cognitive discounting", the idea that agents discount variables far into the future at higher rates than typically implied in the benchmark model.

Chapter 1 estimates a Behavioral New Keynesian model to revisit the evidence that passive US monetary policy in the pre-1979 sample led to indeterminate equilibria and sunspot-driven fluctuations, while active policy after 1982 was instrumental in restoring macroeconomic stability. The model assumes cognitive discounting and we estimate the model allowing for both determinacy and indeterminacy. The empirical results show that determinacy is preferred both before and after 1979. Even if monetary policy is found to react only mildly to inflation in the pre-Volcker era, the estimated substantial degrees of bounded rationality prevent the economy from falling into indeterminacy.

In Chapter 2, I show how to introduce cognitive discounting into alternative models of larger sizes and with different expectational assumptions. I use this finding to build and estimate a Behavioral Smets and Wouters model, which is a medium-scale DSGE model. The empirical estimation shows that the data still prefer substantial degrees of bounded rationality even in a model with as many frictions as the Smets and Wouters model. Chapter 3 develops a New Keynesian model with trend inflation and cognitive discounting. In a model with rational expectations, higher trend inflation generates macroeconomic instability by making the economy more susceptible to equilibrium indeterminacy. The rational expectations hypothesis, however, implies a very large weight given to expectations far into the future, with only minimal discounting. Since determinacy or indeterminacy of the equilibrium is a property of the whole model, a failure to recognize and explicitly model deviations from benchmark models of expectations formation may skew the results. Indeed, the analysis shows that this instability hinges on the conventional assumption of rational expectations and that introducing cognitive discounting in the model counteracts the instability introduced by trend inflation and increases the scope for determinacy. Therefore, trend inflation is not as destabilizing as previously thought. Moreover, the data favor the specification including bounded rationality and a determinate equilibrium during the Great Moderation sample.

Several years ago, Robert Lucas’s critique of non-structural models led to the rational expectations revolution in macroeconomics, pioneering several so-called DSGE models of the economic business cycle. While these serve as an appropriate lens through which we could view the economy, the picture these models capture is nonetheless blurry. In these essays, I attempt to correct for a fundamental oversight in some of these models by allowing agents to deviate from perfect rationality so as to better capture human behavior. The first two essays model investors as imperfectly rational, exhibiting cognitive biases such as anchoring and confidence swings that occur both endogenously and exogenously. The results show that business cycles are more volatile when financial agents are behavioral and that such biases can explain a host of historic phenomenon such as the 2008 financial crisis. In the third essay, we explore the effects of cognitive discounting on fiscal multipliers. We show that Ricardian equivalence is further violated when agents are myopic but that such effects are non-linear. Myopia also interacts with monetary inflation targeting and the share of hand-to-mouth agents to have severe effects on the determinacy conditions of the model. All these intricacies are lost when looking purely through a rational expectations lens.

The three chapters in this dissertation analyze the unconventional monetary policy tools that were utilized in response to the global financial crisis of 2007-2009. Chapter 1 examines the degree of misspecification in a mainstream DSGE model with unconventional monetary policy using the DSGE-VAR approach. The findings indicate that this type of model exhibits a high level of misspecification. For instance, estimation results point to the data favoring an unrestricted vector autoregression model over a DSGE model with unconventional monetary policy. Thus, policymakers should exercise caution when using new macroeconomic models that incorporate unconventional monetary policy.

Chapter 2 examines the link between expectations formation and the effectiveness of central bank forward guidance. In a standard New Keynesian model, agents form expectations about future macroeconomic variables via either the standard rational expectations hypothesis or a more plausible theory of expectations formation called adaptive learning. The results show that the efficacy of forward guidance depends on the manner in which agents form their expectations. During an economic crisis (e.g. a recession), for example, the assumption of rational expectations overstates the effects of forward guidance relative to adaptive learning. Specifically, the output gap is higher under rational expectations than adaptive learning. Thus, if monetary policy is based on a model with rational expectations, which is the standard assumption in the macroeconomic literature, the results of forward guidance could be potentially misleading.

Chapter 3 investigates the effectiveness of forward guidance while relaxing two standard macroeconomic assumptions: rational expectations and frictionless financial markets. A standard DSGE model is extended to include the financial accelerator mechanism. The results show that the addition of financial frictions amplifies the differences between rational expectations and adaptive learning to forward guidance. During a period of economic crisis (e.g. a recession), output under rational expectations displays more favorable responses to forward guidance than under adaptive learning. These differences are exacerbated when compared to a similar analysis without financial frictions. Thus, monetary policymakers should consider the way in which expectations and credit market frictions are modeled when examining the effects of forward guidance.

Chapter 1 studies the effects of cyclical changes in U.S. income and wealth inequality on macroeconomic gyrations, shifts in aggregate demand, debt swings, and on the effectiveness of monetary policy. It does so though the lens of a structural model with heterogeneous agents that is brought to the data to jointly explain a set of macroeconomic series and a time-varying dimension of inequality, namely the evolution of the U.S. income and wealth inequality since the 1950s to 2009. The paper shows that changes in income and wealth inequality explain a small fraction of output cycles, operate through persistent changes in Aggregate Demand, and cause large swings in household indebtedness. Despite that influence, changes in income and wealth inequality since the mid-80s to 2009 cannot explain the cyclical debt pileup of that period which is accounted for by credit relaxation. Despite the imbalances of the model in terms of the time-varying and unequal distribution of income and wealth, monetary policy has a small effect on income and wealth inequality, and effectively stabilizes the economy by responding aggressively to inflation even if the observed economy wide fluctuations emanate from changes in income and wealth inequality.

Although the recent dramatic decline of the labor share has attracted a lot of attention, the origins and implications of the historical swings of that share during 1964–2016 still remain unexplored. Chapter 2 fills that void. More specifically, Chapter 2 investigates the driving forces and the implications of the fluctuations in the U.S. labor share. To that end, it considers a structural model featuring various potential drivers of the labor share, such as the relative price of investment, labor unionization, measurement errors etc. To that set of potential drivers, the present work adds production automation aiming to encapsulate routine-biased technological shifts, and shows how to bring the model to the data in order to explain the labor share – something that is not straightforward. The analysis shows that changes in production automation explain about 10% of output fluctuations, a third of the labor share across time, about the entire decline in the labor share since the 1990s, and trigger a countercyclical response in labor hours that cannot be matched by any other conventional aggregate shock in a structural framework.

Chapter 3 sheds lights on the unexplored determinants of the notoriously persistent Euro- Area unemployment from a macroeconomic perspective. More precisely, it quantifies the relative importance of a plethora of drivers of unemployment swings by jointly examining unemployment and wage fluctuations through the lens of a structural model estimated on several wage series to strengthen the identification of shifts in labor market competitiveness. I find that these shifts determine long-run unemployment cycles, but their short-run effect depends on the unemployment–wage co-movement: the stronger the co-movement – as it is in Portugal and Spain compared to Greece and Italy – the lower their effect. Furthermore, the low degree of labor market competitiveness is catalytic for the unemployment spike of the Great Recession. Nevertheless, reforms boosting competition during the recovery period generate subdued wage growth. It is worth mentioning that this paper contributes in the Bayesian estimation of DSGE models as well by applying an ecient treatment of the state space dimensionality. In particular, it borrows an approach from the econometrics literature considering reduced-form models, and operationalizes it within the context of a DSGE model.

Finally, Chapter 4 tackles two unresolved issues in the literature on subdued wage growth. First, not all contributions use the same wage measure, sample period, or economies. Second, all contributions employ reduced-form models impeding a general equilibrium approach that would quantify the relative importance of various driving factors of wage growth. I address those issues by studying the big four Euro-Area economies in a structural framework involving a plethora of disturbances extracted from the data through Bayesian estimation on five wage indicators since the 1990s. More importantly, I pin down the influence of those factors during a particular period of time, namely during output recoveries from troughs. I find a cyclical real wage recovery in Germany after the sovereign debt crisis that is statisti- cally di↵erent from the past and is driven by a weakening in firms’ pricing power despite a productivity slowdown. In contrast, a cyclical (real) wage-less output recovery is observed in France and Italy. In France, the productivity slowdown dominates the weakening in firms’ market power. In Italy, the latter effect along with a demand pick up boosting wages do not suffice to exceed the weakening in workers’ market power. In Spain, cyclical wage growth is positive – the sizable weakening in firms’ pricing power and rising demand exceed the weakening in workers’ market power – only in real terms and not in nominal terms, highlighting the importance of jointly examining price and wage inflation.

This dissertation is a study of how government actions can impact the macroeconomy under different circumstances. In Chapter 1, I examine the impacts from political conflict and violence in several empirical frameworks. Using generalized least squares, I find that higher levels of magnitude of violence lead to lower growth, investment, and standards of living. This result is further confirmed after running two-stage least squares regressions to address the concern of reverse correlation. I also apply a panel vector-autoregression (VAR) model to study the cross-country economic effects of a shock to the magnitude of political violence in three groups of countries that have had turbulent conflicts in the late 20th century. Findings from the panel VARs show that cross-country effects from a political violence shock are most significant when the countries share characteristics directly related to the cause of the conflict.

In Chapter 2 and 3, I pivot towards studying how the behavioral feature, "myopia", affects government policy in the U.S. economy. Chapter 2 investigates the impact of fiscal policy on economic stimulation within the context of agents who exhibit partial myopia, implying that households and firms are less forward-looking and attentive towards future events. This deviation from perfect rationality impacts the marginal propensity to consume, ultimately challenging the theory of Ricardian Equivalence. To address this, I emphasize the importance of introducing partially myopic agents into a new Keynesian model with hand-to-mouth consumers. I estimate the model using Bayesian MCMC methods to fit U.S. time series data between 1984-2019 under both determinacy and indeterminacacy. Under determinacy, partially myopic agents may result in higher fiscal multipliers but significantly crowd out private investment. Furthermore, the estimated myopia parameter is 0.86, which is in alignment with Gabaix (2020). However, the data suggets a preference for an indeterminate solution characterized by low degrees of myopia and a passive monetary policy.

Chapter 3 is an examination of how including myopia affects the conditions for a unique and stable equilibrium. I find that the inclusion has substantial effects on the determinacy of the model, where empirically founded values of hand-to-mouth consumers, reasonable degrees of myopia, and active monetary policy cannot all coexist.

This dissertation uses Bayesian methods to understand how expectations are formed and their role in macroeconomic fluctuations. All three essays study expectations formation through different perspectives and econometric tools.

The first chapter analyzes the impact of central bank transparency on the evolution of agents’ expectations for the Mexican case via a New Keynesian model with adaptive learning and survey forecasts. Among multiple scenarios, the data prefer the observed transparency degree followed by the Mexican central bank, where the central bank credibly communicatesthe inflation target and discloses relevant information about its policy rule. The results show that agents exhibit a faster learning speed than the U.S. and a declining perceived inflation persistence. Plus, the model-implied learning mechanism can match the empirical inflation expectations from the Survey on Expectations of Private Sector Specialists. Moreover, there is evidence suggesting that higher degrees of transparency increase the effectiveness of monetary policy in stabilizing the economy.

Chapter 2 assesses the role of economic conditions in inflation expectation formation using a Bayesian latent class ordinal model and qualitative survey data from the Michigan Survey of Consumers. The results show evidence that inflation expectations have been formed distinctly depending on the economic conditions faced by individuals. Furthermore, the effect of demographic indicators, such as age, gender, education, and income, on inflation expectations varies with the level of distress of the economy.

Lastly, the final chapter develops and estimates a model with informational frictions. Agents are inattentive and form subjective expectations using an economic model. The proposed expectation formation mechanism is estimated using Bayesian methods and tested against rational expectations. The paper yields three novel results. First, the model embedding inattention à la Mankiw and Reis (2002) and subjective expectations under adaptive learning provides the best fit of the data. Secondly, the degree of inattention is susceptible to how the expectation formation process is modeled. In particular, the level of inattention is considerably reduced when I depart from the rational expectation assumption. Finally, this result remains unchanged when tested using real-time macroeconomic series and expectations data from the U.S. Survey of Professional Forecasters.

This dissertation contains three chapters on empirical macroeconomics and monetary policy. In Chapter 1, I test the forecast performance of a small-scale Dynamic Stochastic General Equilibrium (DSGE) model with sentiment shocks. I relax the benchmark assumption of rational expectations and assume instead that economic agents behave in a near-rational fashion: every period they learn and update their beliefs using a constant gain learning algorithm. Sentiment shocks are captured by exploiting observed data on expectations and are defined as the deviations from the model implied expectations due to exogenous waves of pessimism or optimism. The forecast evaluation is accomplished by comparing the root mean squared prediction error of the canonical 3-equation New Keynesian model at different horizons and under different expectation assumptions: rational expectations, learning, and learning with sentiment. The results show that the model with learning and sentiment shocks is not only able to compete with the other two alternatives, but it is generally better to forecast the output gap and the inflation rate. In Chapter 2, I use a small open economy DSGE model to investigate how Mexico's central bank has conducted its monetary policy in the period 1995-2019. The main objective of the paper is to document the systematic changes in the Bank of Mexico's reaction function by analyzing possible shifts in the parameters of the policy rule. The central bank's policy is modeled using a Taylor rule that relates the nominal interest rate to output, inflation, and the exchange rate. I employ Bayesian computational techniques and conduct rolling-window estimations to explicitly show the transition of the policy coefficients over the sample period. Furthermore, the paper examines the macroeconomic implications of these changes through rolling-window impulse-response functions. The results suggest that the Bank of Mexico's response to inflation has been steady since 1995, while the response to output and the exchange rate has decreased and stabilized after 2002. In Chapter 3, I reconsider whether monetary policy in small open economies responds to exchange rates by studying possible parameter instabilities in a DSGE model. The main focus of the paper is to revisit preceding evidence on the response to exchange rate movements by the Bank of England and determine if its reaction function has remained constant throughout the sample. To this end, I estimate a small open economy general equilibrium model using Bayesian econometric techniques over rolling windows. I find overwhelming evidence of shifts in several parameters, including those related to the policy rule. Furthermore, posterior odds tests reveal a time-varying response to exchange-rate fluctuations by the monetary authorities. The results favor the model with the nominal exchange rate embedded in the policy rule for the initial subsamples. However, the evidence steadily evolves across windows and ultimately changes to prefer the model specification with no exchange rate. The paper also documents evident variations in the model dynamics derived by the instability of parameters via rolling-window impulse response functions and variance decomposition analysis.