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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.

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- Data handling
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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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