Heteroskedasticity can be addressed by applying robust standard errors, which provide consistent standard error estimates even when heteroskedasticity is present. This adjustment ensures that inference drawn from the regression coefficients remains valid despite the presence of non-constant variance in the error terms.

Multicollinearity is a problem because it causes the standard errors of the regression coefficients to be inflated, which can lead to less precise estimates and make it difficult to determine the individual effect of each explanatory variable. This reduces the reliability of hypothesis tests.

The Durbin-Watson test is commonly used to detect autocorrelation in the residuals of a regression model. It tests the null hypothesis that there is no first-order autocorrelation in the residuals, with values near 2 indicating no autocorrelation and values approaching 0 or 4 indicating positive or negative autocorrelation, respectively.

The fixed-effects model is preferable when the individual effects are correlated with the explanatory variables. This model accounts for unobserved heterogeneity by allowing each cross-sectional unit to have its own intercept, thus controlling for time-invariant characteristics.

Instrumental Variables (IV) are used in econometrics to address endogeneity problems, which occur when an explanatory variable is correlated with the error term. IVs help in obtaining consistent estimates by providing a source of variation that is correlated with the endogenous explanatory variable but uncorrelated with the error term.