Real Time Changes in Monetary Policy

This paper investigates potential changes in monetary policy over the last decades using a nonparametric vector autoregression model. In the proposed model, the conditional mean and variance are time-dependent and estimated using a nonparametric local linear method, which allows for different forms of nonlinearity, conditional heteroskedasticity, and non-normality. Our results suggest that there have been gradual and abrupt changes in the variances of shocks, in the monetary transmission mechanism, and in the Fed’s reaction function. The response of output was strongest during Volcker’s disinflationary period and has since been slowly decreasing over time. There have been some abrupt changes in the response of inflation, especially in the early 1980s, but we can not conclude that it is weaker now than in previous periods. Finally, we find significant evidence that policy was passive during some parts of Burn’s period, and active during Volcker’s disinflationary period and Greenspan’s period. However, we find that the uncovered behavior of the parameters is more complex than general conclusions suggest, since they display considerable nonlinearities over time. A particular appeal of the recursive estimation of the proposed VAR-ARCH is the detection of discrete local deviations as well as more gradual ones, without smoothing the timing or magnitude of the changes.Monetary Policy, Taylor Rule, Local Estimation, Nonlinearity, Nonparametric, Monetary Policy; Taylor Rule; Local Estimation; Nonlinearity; Nonparametric; Structural Vector Autoregression; Autoregressive Conditional Heteroskedasticity;

Count data models with variance of unknown form: an application to a hedonic model of worker absenteeism

We examine an econometric model of counts of worker absences due to illness in a sluggishly adjusting hedonic labor market. We compare three estimators that parameterize the conditional variance?least squares, Poisson, and negative binomial pseudo maximum likelihood?to generalized least squares (GLS) using nonparametric estimates of the conditional variance. Our data support the hedonic absenteeism model. Semiparametric GLS coefficients are similar in sign, magnitude, and statistical significance to coefficients where the mean and variance of the errors are specified ex ante. In our data, coefficient estimates are sensitive to a regressor list but not to the econometric technique, including correcting for possible heteroskedasticity of unknown form.Publicad

Count data models with variance of unknown form: an application to a hedonic model of worker absenteeism

We examined an econometric model of counts of worker absences due to illness. The underlying theoretical model is of a sluggishly adjusting hedonic labor market. We compared results fromı three parametric estimators, nonlinear least squares plus Poissonand negative binomial pseudo maximum likelihood, to generalized least squares using nonparametric estimates of the conditional variance. Our data support the hedonic model of worker absenteeism. Semiparametric generalized least squares coefficients are similar in sign, magnitude, and statistical significance to their econometric analogs where the mean and variance of the errors were specified ex ante. Overdispersion test reject the Poisson specification. Robustness checks confirm that in our dataı parameter estimates are sensitive to regressor list but are not sensitive to econometric technique, including how we corrected for possible heteroskedasticity of unknown form

Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation