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Question 1 (1 point) Heteroskedasticity is a concern when estimating a regression model with Cross-sectional data Time series data Both cross-sectional and time series data Neither cross-sectional nor time series data Question 2 (1 point) Serial correlation is a concern when estimating a regression model with Cross-sectional data Time series data Both cross-sectional and time series data Neither cross-sectional nor time series data If the variance of the error term is non-constant, var(a;) = 012, a regression model most likely suffers from? Endogeneity Heteroskedasticity Multicollinearity Serial Correlation Question 4 (1 point) If observations of the error term are correlated, corr(s, &;) * 0 for all i and j, a regression model most likely suffers from? Endogeneity Heteroskedasticity Multicollinearity Serial CorrelationQuestion 5 (1 point) When estimating a regression with cross-sectional data, yi = o + Bx; +8j, you should use? Clustered Standard Errors Conventional Standard Errors Newey-West Standard Errors Robust Standard Errors Question 6 (1 point) When estimating an autoregressive (AR) model with time series data, yt = o + pyt-1 +Et (the AR model contains only lags of the dependent variable), you should use? Clustered Standard Errors Conventional Standard Errors Newey-West Standard Errors Robust Standard Errors Question 7 (1 point) When estimating a regression model with time series data, yt =