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Forecasts in a Slightly Misspecified Finite Order VAR

Müller, Ulrich K. - 2011

We propose a Bayesian procedure for exploiting small, possibly long-lag linear predictability in the innovations of a finite order autoregression. We model the innovations as having a log-spectral density that is a continuous mean-zero Gaussian process of order 1/√T. This local embedding makes...

Persistent link: https://www.econbiz.de/10013131235

Generalized Moments Estimation for Panel Data

Druska, Viliam; Horrace, William C. - 2021

This paper considers estimation of a panel data model with disturbances that are autocorrelated across cross-sectional units. It is assumed that the disturbances are spatially correlated, based on some geographic or economic proximity measure. If the time dimension of the data is large, feasible...

Persistent link: https://www.econbiz.de/10013232767

Estimating Autocorrelations in Fixed-Effects Models

Solon, Gary - 2021

series data, longitudinal data often contain serially correlated error terms, but the autocorrelation estimators commonly …'s method of correcting for the inconsistency of autocorrelation estimators by generalizing to higher than first … facilitate the identification and estimation of autocorrelation processes in both the generalized Nickell method and an …

Persistent link: https://www.econbiz.de/10013217239

Testing the Autocorrelation Structure of Disturbances in Ordinary Least Squares and Instrumental Variables Regressions

Cumby, Robert E. - 2007

This paper derives the asymptotic distribution for a vector of sample autocorrelations of regression residuals from a quite general linear model. The asymptotic distribution forms the basis for a test of the null hypothesis that the regression error follows a moving average of order q...

Persistent link: https://www.econbiz.de/10012776739

Regression Discontinuity in Time : Considerations for Empirical Applications

Hausman, Catherine - 2017

Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners,...

Persistent link: https://www.econbiz.de/10012951355

Tempered Particle Filtering

Herbst, Edward P. - 2017

The accuracy of particle filters for nonlinear state-space models crucially depends on the proposal distribution that mutates time t-1 particle values into time t values. In the widely-used bootstrap particle filter, this distribution is generated by the state-transition equation. While...

Persistent link: https://www.econbiz.de/10012955446

Factors that Fit the Time Series and Cross-Section of Stock Returns

Lettau, Martin - 2018

We propose a new method for estimating latent asset pricing factors that fit the time-series and cross-section of expected returns. Our estimator generalizes Principal Component Analysis (PCA) by including a penalty on the pricing error in expected returns. We show that our estimator strongly...

Persistent link: https://www.econbiz.de/10012913794

Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator

West, Kenneth D. - 2021

A þT consistent estimator of a heteroskedasticity and autocorrelation consistent covariance matrix estimator is …

Persistent link: https://www.econbiz.de/10013220975

Estimating Deterministic Trends in the Presence of Serially Correlated Errors

Canjels, Eugene; Watson, Mark W. - 2021

This paper studies the problems of estimation and inference in the linear trend model: yt=à+þt+ut, where ut follows an autoregressive process with largest root þ, and þ is the parameter of interest. We contrast asymptotic results for the cases þþþ 1 and þ=1, and argue that the most...

Persistent link: https://www.econbiz.de/10013223612

Automatic Lag Selection in Covariance Matrix Estimation

West, Kenneth D.; Newey, Whitney K. - 2021

heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that …

Persistent link: https://www.econbiz.de/10013226589