This study examines possible existence of business cycle asymmetries in Canada, France, Japan, UK, and USA real GDP growth rates using neural networks nonlinearity tests and tests based on a number of nonlinear time series models. These tests are constructed using in-sample forecasts from artificial neural networks (ANN) as well as time series models. Our study results based on neural network tests show that there is statistically significant evidence of business cycle asymmetries in these industrialized countries. Similarly, our study results based on a number of time series models also show that business cycle asymmetries do prevail in these countries. So we are not able to evaluate the impact of monetary policy or any other shocks on GDP in these countries based on linear models. Copyright Springer Science + Business Media, Inc. 2005B22, C32, C45, E32,
We investigate the persistence in monthly KSE100 excess stock
returns over the Treasury bills rates using non-Gaussian state space or
unobservable component model with stable distributions and volatility
persistence. Results from our non-Gaussian state space model, which is
an improvement over Conard and Kaul (1988), show that the conditional
distribution has a stable of 1.748 and normality is rejected even after
accounting for GARCH. There exists a statistically significant
predictable component in the KSE 100 excess stock returns. The optimal
predictor in the unconditional expectation of the series is estimated to
be 0.18 percent per annum. An evidence of highly nonconstant scales in
different periods of time exhibits a tendency towards stock market
crashes which invites remedial policy action.
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