The aim of this study is to determine the nature of any relationship between renewable energy investment, oil prices, GDP and the interest rate, using a time series approach. We concentrate on three countries with different relationships to the renewable energy industry, with Norway and the UK being oil-exporters for most of the sample and the USA an importer. Following estimation using a VAR model, the results provide evidence of considerable heterogeneity across the countries, with the USA having a strong relationship between oil prices and renewable energy, Norway having a less pronounced relationship and the UK no relationship. These results reflect the fact that the USA is predominantly an oil-importer during most of this sample and supports renewable energy relatively less than the other countries, so changes to renewable energy investment reflect other factors in the market such as the price of substitutes to a greater extent than countries where renewable energy receives more government support. The main policy implications from this study are that in countries where there is little support for the renewable energy sector, investment will be more dependent on macroeconomic aspects as well as substitutes such as oil, therefore the authorities will need to potentially increase support when oil prices are low or when the economy is in a downturn.
Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior. The single scale based traditional entropy measures yielded contradictory results about the dynamics of real world time series data of healthy and pathological subjects. Recently the multiscale entropy (MSE) algorithm was introduced for precise description of the complexity of biological signals, which was used in numerous fields since its inception. The original MSE quantified the complexity of coarse-grained time series using sample entropy. The original MSE may be unreliable for short signals because the length of the coarse-grained time series decreases with increasing scaling factor τ, however, MSE works well for long signals. To overcome the drawback of original MSE, various variants of this method have been proposed for evaluating complexity efficiently. In this study, we have proposed multiscale normalized corrected Shannon entropy (MNCSE), in which instead of using sample entropy, symbolic entropy measure NCSE has been used as an entropy estimate. The results of the study are compared with traditional MSE. The effectiveness of the proposed approach is demonstrated using noise signals as well as interbeat interval signals from healthy and pathological subjects. The preliminary results of the study indicate that MNCSE values are more stable and reliable than original MSE values. The results show that MNCSE based features lead to higher classification accuracies in comparison with the MSE based features.
This paper investigates whether there are benefits in terms of higher economic stability from incorporating stock prices into the price index targeted by the central banks. It also looks into the question of whether central banks should use stock prices as a component of the output stability index and how the index can be constructed. An optimization technique is employed to estimate weights for the various sectoral prices. The obtained weights, which depend on sectoral parameters, differ from those used in the construction of the consumer price index, CPI. Using data from the UK and the US, our analysis demonstrates that in comparison to the CPI, our measure of inflation leads to a higher output stability. Thus, in an inflation-targeting monetary policy environment, it is important to adopt a broader inflation benchmark than the CPI for the general macroeconomic stability.
This paper examines the relationship between the 'exclusion' type core inflation measures and the stability price index. Empirical results for Malaysia and Pakistan suggests that, if targeting core inflation index is to stabilize output, weights of the export-oriented sectors (energy for Malaysia and foodstuffs for Pakistan) should be reduces, in relation to the consumers' price index weights, and for import-oriented sectors, increased. It also indicates that, in order to maintain real sector stability, central bankers should include the fundamental component of the stock market prices in the price index they target.
The article examines the relationship between the real effects of inflation and its level in countries with frequent episodes of high inflation. The real effects are computed as asymmetric impulse responses of output to inflation separately for the regimes with different signs of the differences between the expected inflation and the predicted output-neutral inflation. It is found that, with the increase in inflation, such effects increase for the regime with the positive sign, relatively to the effects for the regime with the negative sign. It is also shown that this finding is valid for most countries with high inflation episodes, where inflation is greater than 4.8% for at least 25% of quarterly observations. This leads to a simple policy prescription that, in economies with frequent high inflation episodes, anti-inflationary monetary decisions are least damaging for output if undertaken in the periods when the difference between the expected and output-neutral inflation is negative.
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