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VC - a method for estimating time-varying coefficients in linear models

Schlicht, Ekkehart - 2019

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. This estimator does not require that disturbances are normally distributed, but if they are, the proposed estimator is asymptotically equivalent to the maximum likelihood...

Persistent link: https://www.econbiz.de/10011990906

VC : a method for estimating time-varying coefficients in linear models

Schlicht, Ekkehart - 2020

This paper describes a moments estimator for a standard state-space model with coefficients generated by a random walk. A penalized least squares estimation is linked to the GLS (Aitken) estimates of the corresponding linear model with time-invariant parameters. The VC estimates are moments...

Persistent link: https://www.econbiz.de/10012161405

VC : a method for estimating time-varying coefficients in linear models

Schlicht, Ekkehart - 2019

Persistent link: https://www.econbiz.de/10012134019

Variance estimation in a random coefficients model

Schlicht, Ekkehart; Ludsteck, Johannes - 2006

This papers describes an estimator for a standard state-space model with coefficients generated by a random walk that is statistically superior to the Kalman filter as applied to this particular class of models. Two closely related estimators for the variances are introduced: A maximum...

Persistent link: https://www.econbiz.de/10010439372

The Fundamental Law of Active Management : Time Series Dynamics and Cross-Sectional Properties

Ding, Zhuanxin - 2011

I derive a generalized version of the fundamental law of active management under some weak conditions. I show that the original fundamental law of Grinold and various extensions are special cases of the result presented in this paper. I also show that cross-sectional ICs are usually different...

Persistent link: https://www.econbiz.de/10013133409

Forecasting Economy with Bayesian Autoregressive Distributed Lag Model : Choosing Optimal Prior in Economic Downturn

Buss, Ginters - 2009

Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag model is chosen. The results show that a sharp...

Persistent link: https://www.econbiz.de/10013156355

Effectiveness of Sovereign Oil Funds as Instruments of Fiscal Stability : A Time-Series and Panel Data Analysis of the Relationship between Real Government Spending and Real Oil Ex...

Etemad, Alexandre - 2014

To enrich the debate about the effectiveness of sovereign wealth funds, this paper explores the relationship between real government spending and real oil export revenues for a sample of six countries. This exploration uses an econometric analysis based on time-series data and its purpose is to...

Persistent link: https://www.econbiz.de/10013054166

A Primer on Spurious Statistical Significance in Time Series Regressions

Deng, Ai - 2015

Persistent link: https://www.econbiz.de/10014134907

A Panic Attack on Unit Roots and Cointegration

Bai, Jushan; Ng, Serena - 2002

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANIC - a 'Panel Analysis of Non-stationarity in Idiosyncratic and Common components'. PANIC consists of univariate...

Persistent link: https://www.econbiz.de/10014121962

Outlier detection in GARCH models

Doornik, Jurgen A.; Ooms, Marius - 2005

We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier … presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests … methods, especially when it comes to determining the date of the outlier. We apply the method to returns of the Dow Jones …

Persistent link: https://www.econbiz.de/10011346470

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