The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis
This paper examines point and density forecasts from the European Central Bank?s Survey of Professional Forecasters. We derive individual uncertainty measures along with individual point- and density-based measures of disagreement. We also explore the relationship between uncertainty and disagreement, as well as their roles in respondents? forecast performance and forecast revisions. We observe substantial heterogeneity in respondents? uncertainty and disagreement. In addition, there is little co-movement between uncertainty and disagreement, and forecast performance shows a more robust ...
Was Sarbanes-Oxley Costly? Evidence from Optimal Contracting on CEO Compensation
This paper investigates the effects of the Sarbanes-Oxley Act (SOX) on CEO compensation, using panel data constructed for the S&P 1500 firms on CEO compensation, financial returns, and reported accounting income. Empirically SOX (i) changes the relationship between a firm?s abnormal returns and CEO compensation, (ii) changes the underlying distribution of abnormal returns, and (iii) significantly raises the expected CEO compensation in the primary sector. We develop and estimate a dynamic principal agent model of hidden information and hidden actions to explain these regularities. We find ...
A Composite Likelihood Approach for Dynamic Structural Models
We describe how to use the composite likelihood to ameliorate estimation, computational, and inferential problems in dynamic stochastic general equilibrium models. We present a number of situations where the methodology has the potential to resolve well-known problems. In each case we consider, we provide an example to illustrate how the approach works and its properties in practice.
Approximating Time Varying Structural Models With Time Invariant Structures
The paper studies how parameter variation affects the decision rules of a DSGE model and structural inference. We provide diagnostics to detect parameter variations and to ascertain whether they are exogenous or endogenous. Identifi cation and inferential distortions when a constant parameter model is incorrectly assumed are examined. Likelihood and VAR-based estimates of the structural dynamics when parameter variations are neglected are compared. Time variations in the financial frictions of Gertler and Karadi's (2010) model are studied.
Selecting Primal Innovations in DSGE models
DSGE models are typically estimated assuming the existence of certain primal shocks that drive macroeconomic fluctuations. We analyze the consequences of estimating shocks that are "non-existent" and propose a method to select the primal shocks driving macroeconomic uncertainty. Forcing these non-existing shocks in estimation produces a downward bias in the estimated internal persistence of the model. We show how these distortions can be reduced by using priors for standard deviations whose support includes zero. The method allows us to accurately select primal shocks and estimate model ...
Delphic and Odyssean Monetary Policy Shocks: Evidence from the Euro Area
We use financial intraday data to identify monetary policy surprises in the euro area. We find that monetary policy statements and press conferences after European Central Bank (ECB) Governing Council meetings convey information that moves the yield curve far out. Moreover, the nature of the information revealed in a narrow window around these statements and press conferences evolved over time. Until 2013, unexpected variations in future interest rates were positively correlated with the changes in market-based measure of inflation expectations consistent with news on future macroeconomic ...
Easy Bootstrap-Like Estimation of Asymptotic Variances
The bootstrap is a convenient tool for calculating standard errors of the parameter estimates of complicated econometric models. Unfortunately, the bootstrap can be very time-consuming. In a recent paper, Honor and Hu (2017), we propose a ?Poor (Wo)man's Bootstrap? based on one-dimensional estimators. In this paper, we propose a modified, simpler method and illustrate its potential for estimating asymptotic variances.
Selection Without Exclusion
It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. We find that the additional structure in the classical sample selection model can significantly reduce the identified set for the parameters ...
Simpler Bootstrap Estimation of the Asymptotic Variance of U-statistic Based Estimators
The bootstrap is a popular and useful tool for estimating the asymptotic variance of complicated estimators. Ironically, the fact that the estimators are complicated can make the standard bootstrap computationally burdensome because it requires repeated re-calculation of the estimator. In Honor and Hu (2015), we propose a computationally simpler bootstrap procedure based on repeated re-calculation of one-dimensional estimators. The applicability of that approach is quite general. In this paper, we propose an alternative method which is specific to extremum estimators based on U-statistics. ...
Poor (Wo)man’s Bootstrap
The bootstrap is a convenient tool for calculating standard errors of the parameters of complicated econometric models. Unfortunately, the fact that these models are complicated often makes the bootstrap extremely slow or even practically infeasible. This paper proposes an alternative to the bootstrap that relies only on the estimation of one-dimensional parameters. The paper contains no new difficult math. But we believe that it can be useful.