Question

Explain the differences between financial data and the data use for economics and other social sciences. Asses whether the econometric modelling financial data  is less difficult  than other types of data with the aid of examples.

Answer 1

Financial data refers to the sequence of the data that shows the state of financial health of a business operation or a an operating firm  while the economic data are sequence of the data that describe the state of a country’s economic either past, present or  future depending on the specified economic  factors. The relationships between two or more economies are measured by the econometrics models where by the numerical mathematical economics and statistical economic models are used to provide the relations between the economic parameters of estimation. Econometric model is made up of the equations sets for behavior description where the equation consists of the disturbances and variables observed, observing the errors of the numerical values and specifying the probability distributions of the disturbances made.

Financial data do follows the normal distributions. Most of the financial data do not follows normal distributions which gives the econometric implication when modelling. The inferences and the model procedures used to measure non normal distributions are to the robust. Example include the time series data which provides the information about the variables over a specific period.

Financial data is usually noisy and difficult to pick up any pattern. This simply means it is variable and doesn’t follow a time path as most economic data.

Financial data have higher moments as compared to other types of data. Estimations of the parameters by used the econometric models is less difficulty as compared to the other social sciences date which most of them  don’t have any moments. The higher moment’s distribution analysis of the financial data is undertaken under the capital pricing model which gives higher moments that variance. Example is health data.

Financial data exhibits the complex relationship which can be adjusted by the econometric models. P –values of the financial data can be obtained which shows the statistically significance. The financial data are observed in every day, every hour, and every second. Information arrives randomly and so do the events. This make more easily to be modelled as compared to other econometric sciences. However, these data, have patterns of regularities in variables which can be easily identified in the fitted model, effect of a change on the variables to be assessed, and links between the variables to be established examples includes the financial statements. Financial data is usually high frequency, i.e. daily or hourly data, whereas economics data is more likely to be monthly or annual

 Financial econometrics tries to analyses these data by employing the relevant statistical procedures on the financial data which covers the basic assumptions the financial time series  where the log returns are not correlated ,identically distributed with the constant mean  and variance or statistically independent. Examples includes FTSE data obtained from US treasury.

Financial data assumption to be linear dependency. In this case, the data shows white noise which more bit complex whereby the conditional mean is not usually fulfilled when the white noise isn’t strict. But more serious problem is the condition of non-correlation in log returns. In many cases also this condition is not fulfilled. Their typical property is that only the conditional means are time dependent, other characteristics of location and variability are time invariant. Examples are seasonal market indices obtained from security exchange

Financial data refers to the sequence of the data that shows the state of financial health of a business operation or a an operating firm  while the economic data are sequence of the data that describe the state of a country’s economic either past, present or  future depending on the specified economic  factors.