The paper aims at examining an augmented version of Fisher hypothesis that include inflation instability. According to this hypothesis, there is a positive relation between interest rates and expected inflation. In contrast, there is a debate regarding the impact of inflation uncertainty on interest rate. According to the portfolio theory and models of asset pricing, inflation instability positively affects the interest rate. The reason is that risk-averse investors must be compensated with higher returns for higher risks. In contrast, the loanable funds theory implies a negative impact of inflation instability and interest rates since high uncertainty leads consumers to protect themselves against inflation by raising their savings which lowers consumption and interest rates. To compute inflation volatility, we applied different Autoregressive Conditional Heteroscedasticity models. The simple and augmented versions of Fisher hypothesis are examined using Markov Switch Model to account for possible regime shift in that relationship. For the original Fisher hypothesis, there is an evidence of supporting it in the first regime while that hypothesis does not hold in the second one. In the augmented version of Fisher hypothesis, portfolio theory hypothesis is verified in the first regime whereas the loanable funds hypothesis is confirmed in the second one.
The paper examines the impact of energy prices on electricity generation by different fuel sources (i.e., oil, gas, and hydropower) in Egypt by employing the autoregressive distributed lag approach and bounds test. Two models are estimated where the first accounts for oil prices only whereas the second include both gas and oil prices. In the first model, oil prices negatively affect the electricity produced from oil in the short-run with no impact in the long-run. Also, hydropower is complementary for oil in electricity production only in the short-term whereas gas is a substitute for oil in both long and short terms. In the second model, both energy prices influence electricity generation from oil in both short and long runs while gas and hydropower are respectively, substitute and complementary to oil in both long and short-run.
This paper aims at improving the prediction accuracy through using combining forecasts approaches. In forecast combination, the crucial issue is the selection of the weights to be assigned to each model. In addition to traditional methods, we propose, also, two sophisticated approaches. These suggested methods are modified Bayesian Moving Average (BMA) and Extended Time-varying coefficient (ETVC). The first technique is based on merging the traditional BMA with other frequentist combination schemes to avoid the subjective prior inside the traditional Bayesian technique. The suggested ETVC approach provides consistent time-varying parameters even if there are some measurement errors, omitted variables bias and if the true functional form is unknown. Concerning the included models, we consider both linear and nonlinear models in order to calculate the forecasts of quarterly Egyptian CPI inflation. We find that our proposed scheme ETVC is superior to the best model and all other static combination schemes including the time-varying scheme based on the random walk coefficients updated (TVR) approach. Additionally, the suggested modified Bayesian approach improves the traditional BMA and overcomes the problem of depending on the arbitrary choice for the initial priors.
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