Market structure and business cycles: Do nominal rigidities influence the importance of real shocks?

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively unimportant in neo-Keynesian environments. A model with endogenous capital utilization captures both results depending upon the degree of nominal rigidity. In the model, MEI shocks create a wedge between the nominal returns on bonds and capital. Nominal rigidities activate this wedge and place the relative importance on MEI shocks, while TFP shocks dominate when prices are perfectly flexible.Business Cycle Fluctuations; Nominal Rigidities; Exogenous Shocks

Precautionary Learning and Inflationary Biases

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a statistical decision making context, this loss function can be understood as a second order approximation to a von-Neumann Morgenstern utility function. This paper considers instead the implications for adaptive learning of a third order approximation. The resulting asymmetry leads the estimator to put more weight on avoiding mistakes in one direction as opposed to the other. As a precaution against making a more costly mistake, a statistician biases his estimates in the less costly direction by an amount proportional to the variance of the estimate. We investigate how this precautionary bias will affect learning dynamics in a model of inflationary biases. In particular we find that it is possible to maintain a lower long run inflation rate than could be obtained in a time consistent rational expectations equilibrium.Least squares learning, time inconsistency, statistical decision making

Structural Macroeconometrics

Methodologies for analyzing the forces that move and shape national economies have advanced markedly in the last thirty years, enabling economists as never before to unite theoretical and empirical research and align measurement with theory. In Structural Macroeconometrics , David DeJong and Chetan Dave provide the unified overview and in-depth treatment analysts need to apply these latest theoretical models and empirical techniques. The authors' emphasis throughout is on time series econometrics. DeJong and Dave detail methods available for solving dynamic structural models and casting solutions in the form of statistical models with empirical implications that may be analyzed either analytically or numerically. They present the full range of methodologies for characterizing and evaluating these empirical implications, including calibration exercises, method-of-moment procedures, and likelihood-based procedures, both classical and Bayesian. The book is complete with a rich array of implementation algorithms, sample empirical applications, and supporting computer code. Structural Macroeconometrics is tailored specifically to equip readers with a set of practical tools that can be used to expedite their entry into the field. DeJong and Dave's uniquely accessible, how-to approach makes this the ideal textbook for graduate students seeking an introduction to macroeconomics and econometrics and for advanced students pursuing applied research in macroeconomics. The book's historical perspective, along with its broad presentation of alternative methodologies, makes it an indispensable resource for academics and professionals.methodologies, research, measurement, theory, analysis, models, empirical, technique, statistical, computer, numerical

The Bank Lending Channel: a FAVAR Analysis

We examine the role of commercial banks in monetary transmission in a factor-augmented vector autoregression (FAVAR). A FAVAR exploits a large number of macroeconomic indicators to identify monetary policy shocks, and we add commonly used lending aggregates and lending data at the bank level. While our results suggest that the bank lending channel (BLC) is stronger than previously thought, this feature is not robust. In addition, our results indicate a diffuse response to monetary innovations when individual banks are grouped according to asset sizes and loan components. This suggests that other bank characteristics could improve the identification of the BLC.Bank Lending Channel, FAVAR, Monetary Policy

Expectations Formation and Capacity Utilization: Empirical Analyses

This dissertation consists of three empirical chapters. The first chapter examines the extent to which real-world agents are rational in making quantitative expectations, an issue over which there is much debate. In this chapter dynamic models for new plant-level survey data are estimated in order to test rationality for manufacturing plants that report expectations of capital expenditures. An advantage of such data is that rationality is tested in environments where agents may not have knowledge of each others' expectations, so strategic motives for biases or inefficiencies are minimized. Model estimates and tests suggest that weak implications of rational expectations are rejected, as are adaptive expectations. The second chapter examines expectations formation in the economists' laboratory as psychologists have documented several biases and heuristics that describe deviations from Bayesian updating-a standard assumption for economists. Indeed, Confirmation Bias predicts that individuals will exhibit systematic errors in updating their beliefs about the state of the world given a stream of information. This chapter examines this bias within a non-strategic environment that motivates experimental subjects financially to provide probability estimates that are close to those of a Bayesian. Subjects revise their estimates of the state of the world as they receive signals. Comparing their estimates to those of a Bayesian shows that subjects display conservatism by underweighting new information. In addition, subjects display confirmation bias by differentiating between confirming and disconfirming evidence. The third chapter seeks to determine, through reduced-form Phillips curves estimates and a structural model, whether the indicator relationship between capacity utilization and inflation has diminished as in recent years high levels of capacity utilization have not led to higher inflation. In Canada, the capacity utilization rate is benchmarked to survey data, thereby providing a unique opportunity to empirically analyze this macroeconomic relationship. Estimates of time-varying parameters and structural break models indicate that there have been breaks over time in the relationship. The timings of the breaks suggest that increasing competitiveness and a rules-based monetary policy may help account for the demise of the relationship. Estimates of a monopolistically competitive sticky-price model economy qualitatively lend credence to this conjecture

Market structure and business cycles: Do nominal rigidities influence the importance of real shocks?

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively unimportant in neo-Keynesian environments. A model with endogenous capital utilization captures both results depending upon the degree of nominal rigidity. In the model, MEI shocks create a wedge between the nominal returns on bonds and capital. Nominal rigidities activate this wedge and place the relative importance on MEI shocks, while TFP shocks dominate when prices are perfectly flexible

Precautionary Learning and Inflationary Biases

Recursive least squares learning is a central concept employed in selecting amongst competing outcomes of dynamic stochastic economic models. In employing least squares estimators, such learning relies on the assumption of a symmetric loss function defined over estimation errors. Within a statistical decision making context, this loss function can be understood as a second order approximation to a von-Neumann Morgenstern utility function. This paper considers instead the implications for adaptive learning of a third order approximation. The resulting asymmetry leads the estimator to put more weight on avoiding mistakes in one direction as opposed to the other. As a precaution against making a more costly mistake, a statistician biases his estimates in the less costly direction by an amount proportional to the variance of the estimate. We investigate how this precautionary bias will affect learning dynamics in a model of inflationary biases. In particular we find that it is possible to maintain a lower long run inflation rate than could be obtained in a time consistent rational expectations equilibrium

Market structure and business cycles: Do nominal rigidities influence the importance of real shocks?

This paper investigates the relative importance of shocks to total factor productivity (TFP) versus the marginal efficiency of investment (MEI) in explaining cyclical variations. The literature offers contrasting results: TFP shocks are important in neoclassical environments, while relatively unimportant in neo-Keynesian environments. A model with endogenous capital utilization captures both results depending upon the degree of nominal rigidity. In the model, MEI shocks create a wedge between the nominal returns on bonds and capital. Nominal rigidities activate this wedge and place the relative importance on MEI shocks, while TFP shocks dominate when prices are perfectly flexible