Search Results
Showing results 1 to 5 of approximately 5.
(refine search)
Discussion Paper
Hey, Economist! Tell Us about the New Applied Macroeconomics and Econometrics Center
Marco Del Negro is the director of the Federal Reserve Bank of New York’s new research center, AMEC, which stands for the Applied Macroeconomics and Econometrics Center. Ahead of hosting its first symposium, “Heterogeneity in Macroeconomics: Implications for Policy,” Liberty Street Economics caught up with Del Negro to learn more about his vision for AMEC.
Policy impact of unexpected Fed rate movements blurred by key information cues
Unexpected Federal Reserve monetary policy moves can profoundly affect market participants, investors and the economy. The impact of policy stems not only from its direct effects—the traditional focus for economists—but also from the new information revealed about the Fed’s economic outlook.
Job vacancy, unemployment relationship clouds ‘soft landing’ prospects
Some economists have argued that because the job vacancy rate has been well above its prepandemic level, there is plenty of room for vacancies to fall before the unemployment rate must rise.
Report
Estimating dynamic panel models: backing out the Nickell Bias
We propose a novel estimator for the dynamic panel model, which solves the failure of strict exogeneity by calculating the bias in the first-order conditions as a function of the autoregressive parameter and solving the resulting equation. We show that this estimator performs well as compared with approaches in current use. We also propose a general method for including predetermined variables in fixed-effects panel regressions that appears to perform well.
Working Paper
Achievement Gap Estimates and Deviations from Cardinal Comparability
This paper assesses the sensitivity of standard empirical methods for measuring group differences in achievement to violations in the cardinal comparability of achievement test scores. The paper defines a distance measure over possible weighting functions (scalings) of test scores. It then constructs worst-case bounds for the bias in the estimated achievement gap (or achievement gap change) that could result from using the observed rather than the true test scale, given that the true and observed scales are no more than a fixed distance from each other. The worst-case weighting functions have ...