Econometrics is the application of statistical methods for solving the financial issues. It has many applications like – the effect of the economic conditions on the financial markets, the asset price derivations, predicting the future financial variables and other financial decision-makings. In econometrics there is a lack of adequate test data for applying the particular methodology, this is termed as the small samples problem. There are further constraints in Econometrics with respect to data revisions and the measurement error. These problems are generally faced due to the subsequent revisions in the reference data and the incorrect data estimation or incorrect measurement of data. The frequency of observation of the financial data has far-reaching implications. For the sake of understanding, just imagine the example of the prices of stocks in the share market, they are highly volatile and keep on changing every day, hour, minutes and so on. So to have precise knowledge of these prices one needs to have large quantum of data, in tens of thousands or in millions. Financial data are very noisy in the sense that it is highly difficult to draw a certain pattern or trend from the available data. In other sense the data doesn’t have a specific distribution. But approximations are applied for modeling of the market and for analyzing the future trends, values of financial variables....
sections, e.g. the weekly prices of mid cap shares over the period of five years.
Cointergration: The macroeconomics and financial economics has empirical research
based on time series. The macroeconomic time series has a nonstationarity property,
which means that the variable doesn't return to a constant value or a linear trend. The
stationary processes has a basic tendency of moving around a linear value i.e. the mean
value and its fluctuation from this value is termed as the deviation. The variables such as
employment, asset prices, gross domestic product follow a nonstationarity property and
possess stochastic trends.
Consider the trend in the financial return series like the rate of change of daily exchange
rate. The figure shows the volatility of returns.
Earlier it was a general practice to estimate nonstationary process equations in
macroeconomic models by the simple linear regression.
Clive Granger (1981) proposed a solution to the time series by a simple regression
= dependent variable
= single exogenous regressor
= white noise
To stress the solution, Granger defined the degree of integaration of the variable. Suppose
a variable can be made nearly stationary by differencing it d times, then it can be
termed as integrated of order d or I(d). Stationary random variables are I(0).
In equation (1), if I(1) and I(1), then I(1). But there exists an
important exception, if I(0) then I(0). The linear combination,
holds same statistical properties as an I(0) variable. This
Cite this document
(“Empirical Techniques in Econometrics Essay Example | Topics and Well Written Essays - 2500 words”, n.d.)
Retrieved from https://studentshare.net/business/287974-empirical-techniques-in-econometrics
(Empirical Techniques in Econometrics Essay Example | Topics and Well Written Essays - 2500 Words)
“Empirical Techniques in Econometrics Essay Example | Topics and Well Written Essays - 2500 Words”, n.d. https://studentshare.net/business/287974-empirical-techniques-in-econometrics.
Cited: 0 times
For the sake of understanding, just imagine the example of the prices of stocks in the share market, they are highly volatile and keep on changing every day, hour, minutes and so on. So to have precise knowledge of these prices one needs to have large quantum of data, in tens of thousands or in millions…
The Journal of Accounting and Economics received the work in April 1992 and the final version by July 1994 before the work was finally published in 1995. These suggest that although the work was published in 1995, the data that were the basis for the work of Banker et al. (1994), were taken much earlier than 1995.
Smokers perform just below average and the mean difference in performance is significant at 5% level of significance [Chi-square = 8.158, p = 0.017] (see table 7 & 8).
A log likelihood ratio test was performed to show the significance of being a
In this report, the graphical analysis, multivariate estimation like Ordinary Least Squares and 2SLS as well as the time series analysis of the data based on VAR estimation are done.
The graphs in appendix 1 show the actual plots and correlograms of
Smokers spent a lot of time trying to get a change to quench their thirst and this has negative implications to their personal well being. According to Richter, the sick days one requests for, the performance of an
The data indicate that the 30 provinces of the People’s Republic of China are highly variable with regard to the gross regional product. Based on the data above, the 2007 gross regional product is as low as
In this case, there is a weak positive correlation between the two variables. Since the coefficient is positive, there is a positive linear relationship between health status and years of education. When years in education increase, there is an improvement in
Thai modest and medium-sized enterprises on the other hand, are nonetheless not entirely competitive, particularly in overseas markets, which require successful production, excellent management structures, market features, product as well as service
Other irrelevant variables were eliminated from the model. With a probability F approximately equal to zero, then it implies that the model was significant with at least one of the coefficients statistically different from zero.
It was observed that, holding all
The above data cannot be used as the dependent variable since its assumed that the data must be following a normal distribution and from the above data, the variable has outliers and is skewed to right. Therefore the assumption of normality is violated in
3 pages (750 words)Essay
Got a tricky question? Receive an answer from students like you!Try us!
Let us find you another Essay on topic Empirical Techniques in Econometrics for FREE!