Principal Components Instrumental Variable Estimation

Instrumental variable estimators can be severely biased in finite samples when the degree of overidentification is high or when the instruments are weakly correlated with the endogenous regressors. This paper proposes an estimator based on the use of the principal components of the instruments as a means of dealing with these issues. By promoting parsimony, the proposed estimator can exhibit considerably lower bias, often without giving up asymptotic efficiency. To make the estimator operational, a simple but flexible rule to select the relevant components for estimation is suggested. Simulation evidence shows that this approach yields significant finite sample improvements over other instrumental variable estimators

Targeting Inflation in a Dollarized Economy: The Peruvian Experience

This discusses the unique experience of Peru`s Central Bank with inflation targeting in an economy characterized by a high degree of financial dollarization. The paper outlines how Peru has taken financial dollarization into consideration in the design of monetary policy, then deals with monetary policy implementation and the Central Bank`s strategy for controlling financial dollarization risks. The paper concludes with analysis and lessons drawn from the Peruvian case

Is euro area lowflation here to stay? Insights from a time‐varying parameter model with survey data

We build a time-varying parameter model that jointly explains the dynamics of euro area inflation and inflation expectations. Our goal is to explain the weak inflation during the post-financial crisis economic recovery of 2013-2019. We find that the inclusion of survey data leads to a more muted decline of trend inflation in recent years and more economic slack. Moreover, the impact of economic slack and import prices on inflation has recently strengthened, and survey respondents updated their beliefs more actively over the financial crisis period. Our model compares well against restricted specifications in terms of forecast performance and marginal likelihood