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Jel Classification:C10 

Working Paper
A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area

This paper examines point and density forecasts of real GDP growth, inflation and unemployment from the European Central Bank?s Survey of Professional Forecasters. We present individual uncertainty measures and introduce individual point- and density-based measures of disagreement. The data indicate substantial heterogeneity and persistence in respondents? uncertainty and disagreement, with uncertainty associated with prominent respondent effects and disagreement associated with prominent time effects. We also examine the co-movement between uncertainty and disagreement and find an ...
Working Papers , Paper 1811

Working Paper
BLP Estimation Using Laplace Transformation and Overlapping Simulation Draws

We derive the asymptotic distribution of the parameters of the Berry et al. (1995, BLP) model in a many markets setting which takes into account simulation noise under the assumption of overlapping simulation draws. We show that, as long as the number of simulation draws R and the number of markets T approach infinity, our estimator is ?m = ?min(R,T) consistent and asymptotically normal. We do not impose any relationship between the rates at which R and T go to infinity, thus allowing for the case of R
Working Paper Series , Paper 2019-24

Journal Article
Is the Phillips Curve Still Alive?

A.W. Phillips's discovery that inflation is negatively correlated with unemployment served as a heuristic model for conducting monetary policy; but the flattening of the Phillips curve post-1970 has divided debate on this empirical relation into two camps: "The Phillips curve is alive and well," and "The Phillips curve is dead." However, this dichotomy oversimplifies the issue.
Review , Volume 102 , Issue 2 , Pages 121-144

Working Paper
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.
Working Paper , Paper 15-10

Working Paper
Understanding Models and Model Bias with Gaussian Processes

Despite growing interest in the use of complex models, such as machine learning (ML) models, for credit underwriting, ML models are difficult to interpret, and it is possible for them to learn relationships that yield de facto discrimination. How can we understand the behavior and potential biases of these models, especially if our access to the underlying model is limited? We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty in the underlying ...
Regional Research Working Paper , Paper RWP 23-07

Working Paper
A Local Projections Approach to Difference-in-Differences Event Studies

Many of the challenges in the estimation of dynamic heterogeneous treatment effects can be resolved with local projection (LP) estimators of the sort used in applied macroeconometrics. This approach provides a convenient alternative to the more complicated solutions proposed in the recent literature on Difference in-Differences (DiD). The key is to combine LPs with a flexible ‘clean control’ condition to define appropriate sets of treated and control units. Our proposed LP-DiD estimator is clear, simple, easy and fast to compute, and it is transparent and flexible in its handling of ...
Working Paper Series , Paper 2023-12

Working Paper
A Robust Method for Microforecasting and Estimation of Random Effects

We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average of time series- and pooled forecasts/estimators, with individual weights that are based on time series information. We show the forecast optimality of individual weights, both in terms of minimax-regret and of mean squared forecast error. We then provide feasible weights that ensure good performance ...
Working Paper Series , Paper WP 2023-26

Working Paper
A Hitchhiker’s Guide to Empirical Macro Models

This paper describes a package which uses MATLAB functions and routines to estimate VARs, local projections and other models with classical or Bayesian methods. The toolbox allows a researcher to conduct inference under various prior assumptions on the parameters, to produce point and density forecasts, to measure spillovers and to trace out the causal effect of shocks using a number of identification schemes. The toolbox is equipped to handle missing observations, mixed frequencies and time series with large cross-section information (e.g. panels of VAR and FAVAR). It also contains a number ...
Working Paper Series , Paper WP-2021-15

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 ...
Staff Reports , Paper 808

Working Paper
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.
Working Paper , Paper 18-12


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