Answer Question 1
The regression model is given by
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XXXXX ut XX XXXXX term XXX XXX XXXXXX regress ut on XXX lagged values , i.e.
The running of XXX XXXXXX XXXXXXXXXX XX to XXXXX XXXXXXX the regression XXXXXX are XXXXXXXX XXXXXXXXXX or not. XX XXXXXXXXXX in XXXXXX regression XXXX that XXXXXXXXXXXX XX XXX XXXXX term XXX uncorrelated XXXX each other. XXX in some cases data XXXXXXX XXXXXXXXXX XXXXXXXXXX XXXX a fitted regression line. XX XXXX situations it XX assumed that the regression function XX XXX XXXXXXXXXX to XXXXXXX the response variable y, XXX it is XXXXXXX. But XXXXX are some XXXXX XXXXX XXX XXXXXXXXXX XX XXXXXXXXXXX errors is XXX admissible. XXX XXXXXXX, XXXX XXXXXXX XX error XXXX XXXXXXX XXXX several observations XXXX XXXXXXXX XXXXXXXXXXXX a,XX made on a single XXXXXXXXXXXX unit. XXXXXXXXXX departures XXX then be XXXXXXXX XXXXXX by XXXXXXX XXXXXXXXXX function, or by XXXXXXXXXX errors. XXXX assuming in second XXXXXXXXXX that Images Not Shown is statistically XXXXXXXXXXX implies XXXX XXX XXXXXX are XXX random and serially XXXXXXXXXX XXXX XXXXX order autoregressive process. XXX implication XX it on the XXXXX regression XX that there may XX some specification XXXXX XXXX XX an omitted variable or ignoring XXXXXXXXXXXXXX. The ordinary least square (OLS) estimates are XXXXX XXXXXXXXXX irrespective of XXXXXXX XXX XXXXXX XXX XXXXXXXX XXXXXXXXXX or not. The serial correlation XX XXXXXX XXXXXX the OLS XX no XXXXXX XX a XXXXXXX variance XXXXXXXXX. XXXXX XXX XXXXXXXXX are not XXXXXXXXX in XXXX case. XXXX XXX serial correlation causes XXX estimated XXXXXXXXX of XXX XXX XXXXXXXXX to XX XXXXXX, resulting in XXXXXXXXXX hypothesis XXXXXXX. XXX t-statistics will XXXXXXXX appear to XX more XXXXXXXXXXX than they really are.
Answer XXXXXXXX 2:
XXXXXX Question 3:
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XXXXXX Question 4:
Answer Question X:
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