Farshid Vahid

Testing multiple equation systems for common nonlinear components

This paper provides a generalized method of moments test for common nonlinear components in multiple time series. The test statistic is presented in terms of the canonical correlations between the multiple series and a judicially chosen set of test regressors, and its performance in small samples is evaluated using Monte Carlo simulations. Two applications highlight the usefulness of this test in multivariate modelling. The first shows that the asymmetries which characterize the US and Canadian business cycles can be attributed to a common nonlinear factor, while the second shows that a single nonlinear factor can account for neglected nonlinearity in an empirical real business cycle model. Each of the implied nonlinear factor models shows potential for forecasting.

Common cycles and the importance of transitory shocks to macroeconomic aggregates

Abstract Although there has been substantial research using long-run co-movement (cointegration) restrictions in the empirical macroeconomics literature, little or no work has been done investigating the existence of short-run co-movement (common cycles) restrictions and discussing their implications. In this paper we first investigate the existence of common cycles in a aggregate data set comprising per-capita output, consumption, and investment. Later we discuss their usefulness in measuring the relative importance of transitory shocks. We show that, taking into account common-cycle restrictions, transitory shocks are more important than previously thought at business-cycle horizons. The central argument relies on efficiency gains from imposing these short-run restrictions on the estimation of the dynamic model. Finally, we discuss how the evidence here and elsewhere can be interpreted to support the view that nominal shocks may be important in the short run.

The importance of common cyclical features in VAR analysis: A Monte Carlo study

Despite the commonly held belief that aggregate data display short-run comovement, there has been little discussion about the econometric consequences of this feature of the data. We use exhaustive Monte-Carlo simulations to investigate the importance of restrictions implied by common-cyclical features for estimates and forecasts based on vector autoregressive models.

Asymmetric Nonlinear Smooth Transition Garch Models

Many studies of time varying volatility in stock markets have focused on an asymmetry in the volatility process known as “the leverage effect.” This asymmetry, first noted by Black in 1976, is characterized by the market’s asymmetric reaction to news, in the sense that stock market returns become more volatile after a negative price shock, than they do after a positive shock of the same absolute size. The first empirical models which explicitly attempted to capture the leverage effect include Engle’s (1990) A-GARCH model, Nelson’s (1991) E-GARCH model, and then the switching GARCH model proposed by Glosten, et al. (1993). Nowadays, there are many models which attempt to incorporate this asymmetry,1 and they are often conveniently summarized and compared using Engle and Ng’s (1993) “news impact curves,” which plot the predicted volatility response implied by a model, against past price shocks of given sign and size.

VARMA versus VAR for Macroeconomic Forecasting

In this article, we argue that there is no compelling reason for restricting the class of multivariate models considered for macroeconomic forecasting to vector autoregressive (VAR) models, given the recent advances in vector autoregressive moving average (VARMA) modeling methodology and improvements in computing power. To support this claim, we use real macroeconomic data, and show that VARMA models forecast macroeconomic variables more accurately than VARs.

Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?

This article develops multivariate factor models for forecasting volatility in Australian stocks. We suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large, and also work with volatility measures that have been constructed so that they contain no jump components. Out-of-sample forecast analysis shows that multivariate factor models of volatility outperform univariate models, but there is little difference between simple and sophisticated factor models.

A complete VARMA modelling methodology based on scalar components

This paper proposes an extension to scalar component methodology for the identification and estimation of VARMA models. The complete methodology determines the exact positions of all free parameters in any VARMA model with a predetermined embedded scalar component structure. This leads to an exactly identified system of equations that is estimated using full information maximum likelihood.

Minimum message length autoregressive model order selection

We derive a minimum message length (MML) estimator for stationary and nonstationary autoregressive models using the Wallace and Freeman approximation. The MML estimators model selection performance is empirically compared with AIC, AIC/sub c/, BIC and HQ in a Monte Carlo experiment by uniformly sampling from the autoregressive stationarity region. Generally applicable, uniform priors are used on the coefficients, model order and log /spl sigma//sup 2/ for the MML estimator. The experimental results show the MML estimator to have the best overall average mean squared prediction error and best ability to choose the true model order.

STRATEGY SIMILARITY AND COORDINATION

An anti-smog filtration system receives exhaust gases from a burning device and passes them through a chemical solution for separating the particulates into the solution. The remaining exhaust gases are recirculated for further separation of particulates into the solution and exhausted. The impregnated solution is passed through a mechanical filtration tank for removal of particulates therefrom and the remainder going to drain.

Economic burden of malaria in rural Tanzania: variations by socioeconomic status and season.

Objective  To determine the economic burden of malaria in a rural Tanzanian setting and identify any differences by socioeconomic status and season.