The paper investigates asymptotically efficient inference in general time series likelihood models with time varying parameters. Inference procedures for general loss functions are evaluated by a weighted average risk criterion. The weight function focusses on persistent parameter paths of moderate magnitude, and is proportional to the distribution function of a Gaussian random walk. It is show...

In this paper, we use maximum entropy to estimate the parameters in an economic model. We demonstrate the use of the generalized maximum entropy (GME) estimator, describe how to specify the GME parameter support matrix, and examine the sensitivity of GME estimates to the parameter and error bounds. We impose binding inequality restrictions through the GME parameter support matrix and develop a ...

This paper investigates the usefulness of information criteria for inference on the number of structural breaks in a standard linear regression model. In particular, we propose a modified penalty function for such criteria based on theoretical arguments, which implies each break is equivalent to estimation of three individual regression coefficients. A Monte Carlo analysis compares information ...

In empirical work on multivariate financial time series, it is common to postulate a Multivariate GARCH model. We show that the popular Gaussian quasi-maximum likelihood estimator of MGARCH models is very sensitive to outliers in the data. We propose to use robust M-estimators and provide asymptotic theory for M-estimators of MGARCH models. The Monte Carlo study and empirical application docume...

We consider a stochastic environment to study interactions among pollution growth, demographic changes, and economic growth. Drawing on the empirical findings of slow convergence patterns of pollution shocks (viz., with a long-memory), we build an analytical framework where stochastic environmental feedback effects on population changes are reflected upon aggregate economic growth. Long-memory ...

When the overidentifying restrictions of the constant-effect linear instrumental variables model fail, common IV estimators converge to different probability limits. I characterize the estimands of two stage least squares, two step GMM, and limited information maximum likelihood as functions of the single-instrument estimands from the just-identified IV regressions which consider each instrumen...

this paper presents the application of multivariate time series model (ardl) to investigate factors affecting bread waste and to explore the relationships among shortrun, longrun and error correction coefficient and the independent variables over the period 1978-2006. results reveal that gross national product and urbanization have positive effects on bread waste in the long term, while the bre...

This study aims at investigating the nature of the causal relationship between immigration and two macroeconomic indicators, GDP per capita and unemployment, in Sweden using autoregressive distributed lag (ARDL) bounds testing procedure and Granger-causality within vector error correction model (VECM) based on annual data spanning the period between 1980 and 2004. Results of the ARDL bounds tes...

This paper contributes to the literature by empirically examining whether the influence of public debt on economic growth differs between the short and the long run and presents different patterns across euro-area countries. To this end, we use annual data from both central and peripheral countries of the European Economic and Monetary Union (EMU) for the 1960-2012 period and estimate a growth ...

We derive a closed form expression for the asymptotic distribution of the LIML estimator for the coefficients of both endogenous and exogenous variables in a partially identified linear structural equation. We extend previous results of Phillips (1989) and Choi and Phillips (1992) where the focus was on IV estimators. We show that partial failure of identification affects the LIML in that its m...