13 research outputs found
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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
The Impact of the Sense of Security from Crime on Residential Property Values in Brazilian Metropolitan Areas
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