Federal Reserve Bank of St. Louis

About: Federal Reserve Bank of St. Louis is a other organization based out in St Louis, Missouri, United States. It is known for research contribution in the topics: Monetary policy & Inflation. The organization has 203 authors who have published 1650 publications receiving 46084 citations.

Mean-Variance vs. Full-Scale Optimization: Broad Evidence for the UK

Abstract: Portfolio choice by full-scale optimization applies the empirical return distribution to a parameterized utility function, and the maximum is found through numerical optimization. Using a portfolio choice setting of three UK equity indices we identify several utility functions featuring loss aversion and prospect theory, under which full-scale optimization is a substantially better approach than the mean–variance approach. As the equity indices have return distributions with small deviations from normality, the findings indicate much broader usefulness of full-scale optimization than has earlier been shown. The results hold in- and out-of-sample, and the performance improvements are given in terms of utility as well as certainty equivalents.

Analysis of Panel Vector Error Correction Models Using Maximum Likelihood, the Bootstrap, and Canonical Correlation Estimators

Abstract: In this paper, we examine the use of Box-Tiao's (1977) canonical correlation method as an alternative to likelihood-based inferences for vector error-correction models. It is now well-known that testing of cointegration ranks based on Johansen's (1995) ML-based method suffers from severe small sample size distortions. Furthermore, the distributions of empirical economic and financial time series tend to display fat tails, heteroskedasticity and skewness that are inconsistent with the usual distributional assumptions of likelihood-based approach. The testing statistic based on Box-Tiao's canonical correlations shows promise as an alternative to Johansen's ML-based approach for testing of cointegration rank in VECM models.

Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood

Abstract: We propose a simulated maximum likelihood estimator for dynamic models based on non-parametric kernel methods. Our method is designed for models without latent dynamics from which one can simulate observations but cannot obtain a closed-form representation of the likelihood function. Using the simulated observations, we nonparametrically estimate the density - which is unknown in closed form - by kernel methods, and then construct a likelihood function that can be maximized. We prove for dynamic models that this nonparametric simulated maximum likelihood (NPSML) estimator is consistent and asymptotically efficient. NPSML is applicable to general classes of models and is easy to implement in practice.

Training and Search On the Job

Abstract: The paper studies human capital accumulation over workers’ careers in an on the job search setting with heterogenous firms. In renegotiation proof employment con- tracts, more productive firms provide more training. Both general and specific training induce higher wages within jobs, and with future employers, even conditional on the future employer type. Because matches do not internalize the specific capital loss from employer changes, specific human capital can be over-accumulated, more so in low type firms. While validating the Acemoglu and Pischke (1999) mechanisms, the analysis nevertheless arrives at the opposite conclusion: That increased labor market friction reduces training in equilibrium.

M2 and Monetary Policy: A Critical Review of the Recent Debate

Abstract: Recently an intense debate has focused on M2’s usefulness as an intermediate target for monetary policy. A review of two leading contributions to the debate indicates that their empirical results are sensitive to changes in key assumptions. Moreover, their empirical results lack the deep structural foundations that are necessary for reliable policy analysis.