Question

4. Is OLS estimator unbiased when we use time series data? Why or why not? Are standard errors still valid if there is serial correlation? Why or why not?

Answer 1

answer:

the Is OLS estimator is unbiased when we are use time series data? Why or why not?

• the OLS estimators is as the are BLUE (i.e. they are straight, impartial and have minimal difference among the class of all direct and fair-minded estimators). ... Along these lines, at whatever point you are intending to utilize a direct relapse show utilizing OLS, dependably check for the OLS presumptions.
• In measurements and econometric, standard minimum squares (OLS) or straight slightest squares is a technique for evaluating the obscure parameters in a direct relapse display.
• Under the asymptotic properties, we say that Wm is steady on the grounds that Wn combines to θ as n gets bigger.
• the In this manner, we have demonstrated that the OLS estimator is steady. Subsequently we require the SLR 3 to demonstrate the OLS estimator is unprejudiced.
• A measurement is said to be a fair gauge of a given parameter when the mean of the testing dissemination of that measurement can be appeared to be equivalent to the parameter being evaluated.
• as the For instance, the mean of an example is a fair gauge of the mean of the populace from which the example was drawn.
• For the most part a fair measurement is favored over a one-sided measurement. This is on the grounds that there is a long run propensity of the one-sided measurement to under/over gauge the genuine estimation of the populace parameter.
• the Absence of prejudice does not ensure that an estimator will be near the populace parameter.
• OLS relapse is an amazing procedure for displaying consistent information, especially when it is utilized related to sham variable coding and information change. ... Basic relapse is utilized to display the connection between a persistent reaction variable y and an illustrative variable x.

the standard errors are still valid if the there are is the serial correlation? Why or why not?

• Connected blunder terms in estimation models speak to the speculation that the one of a kind fluctuations of the related markers cover; that is, they measure something in like manner other than the inert builds that are spoken to in the model. ... connected inside factor estimation mistakes may infer various things.
• Sequential connection is the connection between a given variable and a slacked rendition of itself over different time interim.
• the Sequential relationships are regularly found in rehashing designs, when the dimension of a variable influences its future dimension. ... Sequential connection is otherwise called auto correlation or slacked relationship.
• the Serial connection standard errors Serial relationship happens in time-arrangement ponders when the mistakes related with a given era persist into future eras. ... Outcomes of Serial Correlation.
• the Sequential connection won't influence the impartiality or consistency of OLS estimators, however it affects their proficiency
• With positive sequential relationship, mistakes in a single day and age are emphatically corresponded with blunders in whenever period.
• the Outcomes of Serial Correlation. Sequential relationship won't influence the impartiality or consistency of OLS estimators, however it affects their productivity.
• Sequential connection happens in time-arrangement thinks about when the mistakes related with a given day and age persist into future eras. ... Sequential connection won't influence the fair-mindedness or consistency of OLS estimators, yet it affects their productivity.