VAR analysis and the Great Moderation

Most analyses of the U.S. Great Moderation have been based on structural VAR methods, and have consistently pointed towards good luck as the main explanation for the greater macroeconomic stability of recent years. Based on an estimated New-Keynesian model in which the only source of change is the move from passive to active monetary policy, we show that VARs may misinterpret good policy for good luck. First, the policy shift is suficient to generate decreases in the theoretical innovation variances for all series, and decreases in the variances of inflation and the output gap, without any need of sunspot shocks. With sunspots, the estimated model exhibits decreases in both variances and innovation variances for all series. Second, policy counterfactuals based on the theoretical structural VAR representations of the model under the two regimes fail to capture the truth, whereas impulse-response functions to a monetary policy shock exhibit little change across regimes. Since these results are in line with those found in the structural VARbased literature on the Great Moderation, our analysis suggests that existing VAR evidence is compatible with the ‘good policy’ explanation of the Great Moderation. JEL Classification: E38, E52DSGE Models, Great Moderation, indeterminacy, vector autoregressions

Counterfactual analysis in macroeconometrics: an empirical investigation into the effects of quantitative easing

This paper is concerned with ex ante and ex post counterfactual analyses in the case of macroeconometric applications where a single unit is observed before and after a given policy intervention. It distinguishes between cases where the policy change affects the model’s parameters and where it does not. It is argued that for ex post policy evaluation it is important that outcomes are conditioned on ex post realized variables that are invariant to the policy change but nevertheless influence the outcomes. The effects of the control variables that are determined endogenously with the policy outcomes can be solved out for the policy evaluation exercise. An ex post policy ineffectiveness test statistic is proposed. The analysis is applied to the evaluation of the effects of the quantitative easing (QE) in the UK after March 2009. It is estimated that a 100 basis points reduction in the spread due to QE has an impact effect on output growth of about one percentage point, but the policy impact is very quickly reversed with no statistically significant effects remaining within 9-12 months of the policy intervention

Counterfactual Sensitivity and Robustness

Researchers frequently make parametric assumptions about the distribution of unobservables when formulating structural models. Such assumptions are typically motived by computational convenience rather than economic theory and are often untestable. Counterfactuals can be particularly sensitive to such assumptions, threatening the credibility of structural modeling exercises. To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of unobservables in a class of structural models. In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model, e.g. equilibrium conditions, are maintained. Our methods are computationally simple to implement, with the nuisance distribution effectively profiled out via a low-dimensional convex program. Our procedure delivers sharp bounds for the identified set of counterfactuals (i.e. without parametric assumptions about the distribution of unobservables) as the neighborhoods become large. Over small neighborhoods, we relate our procedure to a measure of local sensitivity which is further characterized using an influence function representation. We provide a suitable sampling theory for plug-in estimators and apply our procedure to models of strategic interaction and dynamic discrete choice

Assessing excess profits from different entry regulations

Entry regulations affecting professional services such as pharmacies are common practice in many European countries. We assess the impact of entry regulations on profits estimating a structural model of entry using the information provided by a policy experiment. We use the case of different regional policies governing the opening of new pharmacies in Spain to show that structural models of entry ought to be estimated with data from policy experiments to pin down how entry regulations change payoffs functions of the incumbents. Contrary to the public interest rationales, regulations are not boosting only small town pharmacies payoffs nor increasing all pharmacies payoffs alike. The gains from regulations are very unevenly distributed,suggesting that private interests are shaping the current mix of entry and markup regulations.Entry, regulation, professional services

Designing Intelligent Software Agents for B2B Sequential Dutch Auctions: A Structural Econometric Approach

We study multi-unit sequential Dutch auctions in a complex B2B context. Using a large real-world dataset, we apply structural econometric analysis to recover the parameters governing the distribution of bidders’ valuations. The identification of these parameters allows us to simulate auction results under different designs and perform policy counterfactuals. We also develop a dynamic optimization approach to guide the setting of key auction parameters. Given the bounded rationality of human decision makers, we propose to augment auctioneers’ capabilities with high performance decision support tools in the form of software agents. Our paper contributes to both theory and practice of auction design. From the theoretical perspective, this is the first study that explicitly models the sequential aspects of Dutch auctions using structural econometric analysis. From the managerial perspective, this paper offers useful implications to business practitioners for complex decision making in B2B auctions

Graphical models for mediation analysis

Mediation analysis seeks to infer how much of the effect of an exposure on an outcome can be attributed to specific pathways via intermediate variables or mediators. This requires identification of so-called path-specific effects. These express how a change in exposure affects those intermediate variables (along certain pathways), and how the resulting changes in those variables in turn affect the outcome (along subsequent pathways). However, unlike identification of total effects, adjustment for confounding is insufficient for identification of path-specific effects because their magnitude is also determined by the extent to which individuals who experience large exposure effects on the mediator, tend to experience relatively small or large mediator effects on the outcome. This chapter therefore provides an accessible review of identification strategies under general nonparametric structural equation models (with possibly unmeasured variables), which rule out certain such dependencies. In particular, it is shown which path-specific effects can be identified under such models, and how this can be done