In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly ...

We propose a testing procedure for assessing the presence of threshold effects in nonstationary vector autoregressive models with or without cointegration. Our approach involves first testing whether the long-run impact matrix characterizing the VECM type representation of the VAR switches according to the magnitude of some threshold variable and is valid regardless of whether the system is pur...

We review, under a historical perspective, the developement of the problem of nonfundamentalness of Moving Average (MA) representations of economic models, starting from the work by Hansen and Sargent [1980]. Nonfundamentalness typically arises when agents’ information space is larger than the econometrican’s one. Therefore it is impossible for the latter to use standard econometric techniques,...

In this paper we discuss sensitivity of forecasts with respect to the information set considered in prediction; a sensitivity measure called impact factor, IF, is defined. This notion is specialized to the case of VAR processes integrated of order 0, 1 and 2. For stationary VARs this measure corresponds to the sum of the impulse response coefficients. For integrated VAR systems, the IF has a di...

data envelopment analysis (dea) is one of the scientific method that computes the efficiency by using a powerful mathematics basic. data envelopment analysis is a non-parametric technique to evaluate the efficiency of a set of decision making units (dmu) with multi inputs and outputs . since dea’s models classifies decision making units into two categories of efficient and inefficient, so most ...

In this paper, we investigate whether incorporating common factors of CPI sub-aggregates into forecasting models increases the accuracy of forecasts of inflation. We extract factors by both static and dynamic factor models and then embed them in ARMA and VAR models. Using quarterly data of Iran’s CPI and its sub-aggregates, the models are estimated over 1990:2 to 2008:2 and out of sample ...

We study the effects of government spending by using a structural, large dimensional, dynamic factor model. We find that the government spending shock is non-fundamental for the variables commonly used in the structural VAR literature, so that its impulse response functions cannot be consistently estimated by means of a VAR. Government spending raises both consumption and investment, with no ev...