195 research outputs found
Identifying business cycle turning points in real time
No full textThis paper evaluates the ability of a statistical regime-switching model to identify turning points in U.S. economic activity in real time. The authors work with a Markov-switching model fit to real gross domestic product and employment data that, when estimated on the entire postwar sample, provides a chronology of business cycle peak and trough dates close to that produced by the National Bureau of Economic Research (NBER). Next, they investigate how accurately and quickly the model would have identified NBER-dated turning points had it been used in real time for the past 40 years. In general, the model identifies turning point dates in real time that are close to the NBER dates. For both business cycle peaks and troughs, the model provides systematic improvement over the NBER in the speed at which turning points are identified. Importantly, the model achieves this with few instances of “false positives.” Overall, the evidence suggests that the regime-switching model could be a useful supplement to the NBER Business Cycle Dating Committee for establishing turning point dates. It appears to capture the features of the NBER chronology accurately and swiftly; furthermore, the method is transparent and consistent.Forecasting ; Economic conditions ; Business cycles
The 2001 recession and the states of the Eighth Federal Reserve District
No full textRecessions ; Federal Reserve District, 8th
Reproducing Business Cycle Features: How Important Is Nonlinearity Versus Multivariate Information?
No full textIn this paper, we consider the ability of time-series models to generate simulated data that display the same business cycle features found in U.S. real GDP. Our analysis of a range of popular time-series models allows us to investigate the extent to which multivariate information can account for the apparent univariate evidence of nonlinear dynamics in GDP. We find that certain nonlinear specifications yield an improvement over linear models in reproducing business cycle features, even when multivariate information inherent in the unemployment rate, inflation, interest rates, and the components of GDP is taken into account.
Employment and the business cycle
No full textThe Great Recession of 2007-2009 has not only caused a large wealth loss, it was also followed by a sluggish subsequent recovery. Two years after officially emerging from the recession, the economy was still growing at a low pace and payroll employment was far from reaching its previous peak. However, assessment of the employment situation was markedly different across different series. The two most important employment series, payroll employment (ENAP) and civilian employment (TCE), have recently been displaying divergent patterns. This has been a source of great uncertainty regarding labor market conditions. This paper investigates the differences in the cyclical dynamics of these series and the implications for monitoring business cycle on a current basis. Univariate and multivariate Markov switching models are applied to revised and real time unrevised data. We find that the main differences across these series occur around recessions. The employment measures have diverged considerably around the last three recessions in 1990-1991, in 2001, and in 2007-2009, but especially during their subsequent recoveries. In particular, while the probabilities of recession for models that include ENAP depict jobless recoveries, the probabilities of recessions from models with TCE fall right around the trough of the last three recessions, as determined by the NBER. This significantly impacts the identification of turning points in multivariate models in sample and in recursive real time analysis, with models that use TCE being more accurate compared to the NBER dating, and delivering faster call of troughs in real time. Models that include ENAP series, on the other hand, yield delays in signaling business cycle troughs, especially the most recent ones.Employment, Business Cycle, Turning Point, Real Time, Markov-Switching, Dynamic Factor Model, Jobless Recovery
Identifying business cycle turning points in real time
No full textThis paper evaluates the ability of a statistical regime-switching model to identify turning points in U.S. economic activity in real time. The authors work with Markov-switching models of real GDP and employment that, when estimated on the entire post-war sample, provide a chronology of business cycle peak and trough dates very close to that produced by the National Bureau of Economic Research (NBER). Next, they investigate how accurately and quickly the models would have identified turning points had they been used in real-time for the past forty years. In general, the models identify turning point dates in real-time that are close to the NBER dates. For both business cycle peaks and troughs, the models provide systematic improvement over the NBER in the speed at which turning points are identified. Importantly, the models achieve this with few instances of "false positives." Overall, the evidence suggests that the regime-switching model could be a useful supplement to the NBER Business Cycle Dating Committee for establishing turning point dates. The model appears to capture the features of the NBER chronology in an accurate, timely way, and does so in a transparent and consistent fashion.Forecasting ; Economic conditions ; Business cycles
Essays on the Asymmetric Effects of Monetary Policy
No full textThe asymmetric effects of monetary policy is the idea that monetary policy actions have asymmetric effects on output and inflation across different states of the world or across different characteristics of the monetary policy action. In the existing literature, there are three types of asymmetry discussed. Monetary policy actions can have different effects depending on the direction of the action, the size of the action, and the phase of the business cycle that the action took place in. This is a topic that is of interest to policy makers around the world as they try to assess the impacts that their proposed policies will have on output and inflation. The asymmetric effects of monetary policy across the three dimensions listed above is the dominant theme of my dissertation. In Chapter 2, I study the asymmetric effects of monetary policy on output over the business cycle using a local projections model. In Chapter 3, I expand the model to include all three types of asymmetry. In Chapter 4, I use a simulation-based study to determine whether the differences specification or the levels specification with a time trend is the correct specification to run in the local projections models from Chapter 2 and Chapter 3
Factors Affecting Tipping Behavior: A Regression Analysis of State-Level Determinants
No full text57 pagesTipping originated hundreds of years ago before it was imported to the United States in the mid 1800s. Today, restaurants across the country have switched to integrated point-of-sale technology systems such as Toast and Square. This study investigates the determinants of average tip percentages left on the Toast platform across all 50 states and six quarter periods spanning 2022 and 2023 using a fixed effects regression model. The goal of this study is to test whether independent variables measuring tip credit, cost-of-living, unemployment rate, state and local sales tax, and personal income per capita affect average tip percentages at the U.S. state level. The significant results propose higher levels of cost-of-living are associated with decreases in average tip percentage, suggesting that higher living costs may reduce consumers' willingness to tip generously. In contrast, increases in unemployment rate and reductions in personal income per capita, on average per state, are associated with increased average tip percentages. These findings align with theories of altruism and generosity that suggest during times of economic recession, consumers tip in larger percentages as motivated by a desire to help servers
ESSAYS ON MONETARY TRANSMISSION AND BANKING
No full textThe commercial banking sector in the United States comprises numerous small, local (community) banks primarily focused on lending to small borrowers in their respective local economies, alongside a smaller group of large, geographically-diversified (non-community) banks that cater to larger borrowers. On average, the lending practices and business models of these two types of banks different substantially. In this dissertation, I analyze the macroeconomic implications of the lending practices of community banks, along with the geographical factors driving their performance dynamics, using a novel method of impulse response function decomposition and existing high-dimensional time-series econometric methodologies, respectively. In brief, I find that the extent of national comovement in community bank performance has increased in recent decades, and that community bank lending plays a significant role in the transmission of monetary policy despite the decline in the presence of community banks relative to that of their noncommunity counterparts.
The second chapter makes a methodological contribution, which informs the analysis of the role of community bank lending in monetary policy transmission in the third chapter. In this chapter, I formulate the concept of a pass-throughimpulse response function (PT-IRF), which captures the contribution of any given subsystem of a greater dynamical system to the net effect of the propagation of a structural shock. I also describe methods of empirically estimating and performing inference on PT-IRFs using vector autoregressions and local projections. Finally, I demonstrate the applicability of PT-IRFs by estimating and empirically testing the effect of a monetary policy shock on unemployment through changes in bank lending in a small autoregressive model.
The third chapter examines how heterogeneity in lending practices acrosscommunity and noncommunity banks influences the transmission of monetary policy to the real economy. Using PT-IRFs, I quantify the contributions of community versus noncommunity bank lending to the dynamic effect of a monetary policy shock on output. My findings show that noncommunity bank lending amplifies the contractionary effects of a monetary tightening in the short run, whereas community bank lending has a stronger amplificatory contribution in the medium run. These results suggest that a continued decline in the relative presence of community banks may lead to a subsequent decline in the persistence of monetary transmission. Furthermore, the adverse impact of a monetary tightening on spending must concentrate more persistently among small businesses and agricultural producers in remote rural areas, since these borrower segments tend to heavily rely on community bank lending as a source of funds.
The fourth chapter studies the comovement in community bank profitability dynamics at three different geographical levels. I use a hierarchical dynamic factor model to extract posterior distributions of national, regional, and state-level latent drivers of quarterly fluctuations in state-average community bank return-on-equity series for all 50 US states. The results show a decrease in the intensity of idiosyncratic performance dynamics since the global financial crisis, along with a near-uniform increase in national comovement. This finding implies an increase in the exposure of the community banking sector to systemic risk, suggesting a potential increase in fragility during future financial crises
Sticky Information and Economic Dynamics
No full textIn this dissertation, I investigate economic dynamics under the sticky information model as- sumption. First, I propose a novel method for evaluating the likelihood of a nonlinear model with time-varying parameters and endogenous variables. Using this method, I estimate model param- eters and unobserved time-varying parameters of the sticky information Phillips curve. Finally, I adapt a bounded rationality assumption to an endogenous sticky information model, further enriching our understanding of economic behavior under these conditions.In Chapter 1, I propose a method to evaluate the likelihood of a nonlinear model with time- varying parameters and endogenous variables. Existing techniques to estimate time-varying param- eter models with endogenous variables are restricted to conditionally linear models. The proposed approach modifies a Sequential Monte Carlo filter to evaluate the likelihood of a nonlinear process with an endogenous variable. The modified filter augments the typical measurement and state equations with an equation incorporating instrumental variables. I evaluate the performance of a Bayesian estimator based on the likelihood calculation using simulations and find that the approach generates accurate estimates of both parameters and the unobserved time-varying parameter.
In Chapter 2, I analyze the empirical evidence of variation in a structural parameter of the sticky information Phillips curve. This involves scrutinizing both the statistical significance of the variation and its economic implications. Upon examination, I discover a systematic trend in firms’ attention to relevant macroeconomic conditions, indicating a decline in attention over time.
In Chapter 3, I study the stability of equilibrium in a general equilibrium model with information frictions. The equilibrium attentiveness rate is stable under a decreasing gain adaptive learning scheme. This stability motivates a review of the transition between equilibrium rates; a drop in the cost of gathering and processing information is used to shift the equilibrium. The attentiveness rate immediately jumps and increases asymptotically, approaching the new equilibrium
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