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Eviews Homework Help


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Eviews Homework Help

EViews represents Econometric views and perspectives which are an amazing factual software that is utilized to dissect and assess financial data. This apparatus was created by Quantitative Micro Software (QMS). EViews programming is friendly supported by both on Windows and MAC working framework. Small scale TSP is a programming language and programming created in 1965 by Hall Robert. Since the main form, a few renditions of E-Views have delivered in the market. The most recent variant is Version 9.0 and Version 11.0 that was obviously delivered into the market in March 2015 and 2019. As a measurable bundle, E-Views is dynamic and can be utilized for performing numerous econometric and factual investigations like determining, board information examination, cross-area investigation, and time arrangement estimation. E-Views are fit for performing such jobs since it is a complex factual bundle that utilizes Windows GUI, programming language, social information, and spreadsheets. EViews is a measurable program utilized for fast and proficient information for the executives.


Topics Covered in EViews Assignment Help By Our Experts


  • Data handling
  • Simulation
  • Forecasting
  • Programming
  • Data presentation
  • Statistical and econometric analysis
  • Production of HD quality graphs and tables

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Eviews Sample Assignment Solved by the Experts

1.	What is the difference between linear and non-linear autoregressive models? 
Which models can/cannot be estimated by the OLS?

The most general linear system produces an output y that is a linear function of 
external inputs x (sometimes called innovations) and its previous outputs:

2.	How to interpret coefficients in the linear model if variables are 
	in logs? How do you Interpreate the coefficients on dummies.   
	Models that are not linear in the variables can often be made to take a 
	linear form by applying a suitable transformation or manipulation. 
	For example, consider the following exponential regression model Yt = AXβt eut 
	it can be made into a classical linear regression model by logarithmic 
	transformation as yt = α + βxt + ut where α = ln(A), yt = ln Yt and xt = ln Xt
	The coefficient estimates are interpreted as elasticities 
	(strictly, they are unit changes on a logarithmic scale). Thus a coefficient 
	estimate of β is interpreted as stating that ‘a rise in X of 1% will lead on 
	average, everything else being equal, to a rise in Y of β %’. Dummy variables are 
	qualitative variables because they are often used to numerically represent 
	a qualitative entity. Examples male = 0, female = 1. The coefficients on 
	the dummy variables can be interpreted as the average differences in the 
	values of the dependent variable for each category, given all of the other 
	factors in the model
3.	OLS estimates are BLUE. Explain it. Does it affect the residuals in 
	your model? 
	Estimators αˆ and βˆ determined by OLS will have a number of desirable properties,
	 and are known as Best Linear Unbiased Estimators (BLUE). 
	● ‘Estimator’ -- αˆ and βˆ are estimators of the true value of α and β 
	● ‘Linear’ -- αˆ and βˆ are linear estimators -- that means that the formulae 
	for αˆ and βˆ are linear combinations of the random variables (in this case, y) 
	● ‘Unbiased’ -- on average, the actual values of αˆ and βˆ will be equal to 
	their true values A brief overview of the classical linear regression model 
	● ‘Best’ -- means that the OLS estimator βˆ has minimum variance among the 
	class of linear unbiased estimators. 
	The primary property of OLS estimators is that they satisfy the criteria of
	minimizing the sum of squared residuals. The observed values of X are uncorrelated 
	with the residuals. The sum of the residuals is zero. The sample mean of the 
	residuals is zero. The predicted values of y are uncorrelated with the residuals. 
4. Can we compare autoregressive models with different left-hand sides?
5. What is R2? What does it measure?
6. Explain the White’s test for heteroscedasticity. How do you interpreate 
   an Eview printout?
	The White test is a statistical test that establishes whether the variance of 
	the errors in a regression model is constant (homoskedasticity). 
	To test for constant variance one undertakes an auxiliary regression analysis 
	and regresses the squared residuals from the original regression model onto a 
	set of regressors that contain the original regressors along with their squares 
	and cross-products and then inspects the R2. The Lagrange multiplier (LM) test 
	statistic is the product of the R2 value and sample size. This follows a chi-squared 
	distribution, with degrees of freedom equal to P − 1, where P is the number of 
	estimated parameters (in the auxiliary regression).
	The squared residuals from the original model serve as a proxy for the variance 
	of the error term at each observation. The independent variables in the auxiliary 
	regression account for the possibility that the error variance depends on the values
	of the original regressors. If the error term in the original model is in fact 
	homoskedastic (has a constant variance) then the coefficients in the auxiliary 
	regression (besides the constant) should be statistically indistinguishable from 
	zero and the R2 should be “small". Conversely, a “large" R2 counts against the 
	hypothesis of homoskedasticity. EViews presents three different types of tests 
	for heteroscedasticity and then the auxiliary regression in the first results table 
	displayed. The test statistics give us the information we need to determine whether 
	the assumption of homoscedasticity is valid or not, but the actual auxiliary 
	regression in the second table indicates the source of the heteroscedasticity 
	if any is found. In this case, both the F- and χ2 (‘LM’) versions of the test 
	statistic give the same conclusion that there is no evidence for the presence 
	of heteroscedasticity, since the p-values are considerably in excess of 0.05. 
	The third version of the test statistic, ‘Scaled explained SS’, which as the 
	name suggests is based on a normalised version of the explained sum of squares 
	from the auxiliary regression, suggests in this case that there is evidence of 
	heteroscedasticity.
7. CAPM: Why do we need the CAPM? How can we formulate the CAMP using an 
	autoregressive model? What does an intercept show? What does a BETTA measure? 
	Do these coefficients relate to market efficiency?
	One use of CAPM is to analyze the performance of mutual funds and other portfolios, 
	to compare the historical risk-adjusted returns (that's the return minus the return 
	of risk-free cash) of the fund against those of an appropriate index. 
	Alpha is the intercept, and beta is the slope, of this line. The general equation 
	of this type of line is
	r   -   Rf   =   beta x ( Km - Rf )   +   alpha
	where r is the fund's return rate, Rf is the risk-free return rate, and Km is the 
	return of the index.
	Alpha, the vertical intercept, tells how much better the fund did than CAPM 
	predicted.  The beta is the ratio of the covariance to the variance of the 
	market return. 
	This is a regression that allows us to estimate the stock's beta coefficient. 
	The CAPM equation suggests that the higher the beta, the higher the expected 
	return. 
	The beta risk is systematic or non-diversifiable or market risk. This risk is 
	rewarded  with expected return. The non-systematic or diversifiable risk is the 
	residual term in regression. The CAPM implies that the alpha is zero. 
	So we can interpret alpha  as the difference between the expected excess return 
	on the security and the actual return. 
8. Suppose you are provided with a print-screen from Eviews. Explain what 
	t-statistics and p-values tell you. Which hypotheses do they test? 
	What is F-statistics? What is AIC and BIC?

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