Econometrics Questions

Which of the following describes the k-fold cross-validation method?Multiple select question.The choice of the model will be sensitive to how the data are partitioned.The k-fold method is less sensitive to data partitioning than the holdout methodThe sample data set is partitioned into two independent and mutually exclusive data sets—the training set and the validation set.The sample data are partitioned into k subsets, where one of the k subsets is used as the validation set

Another commonly used transformation that captures nonlinearities is based on the natural logarithm. Which of the following variables are commonly log-transformed? Select all that apply!Multiple select question.ScoresHouse pricesIncomeAge

The exponential regression model is specified as ln(y) = β0 + β1x + ε. What does β1 × 100 measure?The approximate change in E(y) when x increases by 100 unitsThe approximate percentage change in E(y) when x increases by 100 unitsThe approximate percentage change in E(y) when x increases by one unitThe approximate change in E(y) when x increases by one unit

R2 measures the percentage of sample variations of the response variable explained by the model. Which of the following are true when comparing linear and log-transformed regression models?Select all that apply Multiple select question.Using R2 we can compare the percentage of explained variations of y with that of ln(y).We need to compute the percentage of explained variations of yWe cannot compare the percentage of explained variations of y with that of ln(y).The normal R2will help to explain the percentage variation of y

When an estimated model begins to describe the quirks of the data rather than the real relationships between variables, this is called:OverfittingCross-validationValidationMetrics

What is the linear regression model applied to a binary response variable called?The Binary response modelThe linear probability regression modelThe response and regress modelThe logistic regression model

In the semi-log regression model not all variables are transformed into logs. A semi-log model that transforms only the predictor variable is often called:Quadratic regression modelExponential regression modelLogarithmic regression modelLog-log regression model

In the holdout method, the sample data set is partitioned into two independent and mutually exclusive data sets—the training set and the validation set.TrueFalse

The logistic regression model cannot be estimated with standard ordinary least squares (OLS) procedures. Instead, we rely on which method?No known method identifiedLogistics least squaresMaximum likelihood estimation (MLE)Predicted probabilities

Which of the following is true of Cross-validation? Select all that apply!Multiple select question.The k-fold cross-validation method is a cross validation methodSometimes the data are partitioned into an optional third set called a training data setThe sample is partitioned into a training set and a validation set to assess how well the estimated model predicts with unseen dataThe holdout method is a cross validation method

There are numerous applications where the relationship between the predictor variable and the response variable cannot be represented by a straight line and, therefore, must be captured by an appropriate curve. What are some simple transformations of the variables for nonlinear relationships? Select all that apply!Multiple select question.Dummy variablesSquaresGoodness of fitNatural logarithms

For 0 < β1 < 1, the log-log regression model implies a positive relationship between x and E(y); as x increases, E(y) increases at a slower rate. This may be appropriate in the food expenditure example where we expect food expenditure to react positively to changes in income, with the impact diminishing at higher income levels. If β1 < 0, what is the relationship between x and E(y)?As x increases, E(y) decreases at a faster rateAs x increases, E(y) decreases at a slower rateAs x increases, E(y) increases at a slower rateImplies a positive and increasing relationship between x and y

Select all that applyPredictions with the exponential regression model are made by yˆ=exp(b0+b1x+se2/2). Which of the following is true?b0 and b1 are the standard errors of the estimatesse2 is the standard error of the estimateb0 and b1 are the coefficient estimatesse is the standard error of the estimate

Select all that applyA useful method to interpret the estimated coefficient is to highlight the changing impact of x on p. For instance, given x = 10, we compute the predicted probability as 0.4256. For x = 11, the predicted probability is pˆ=0.4700. Therefore, as x increases by one unit from 10 to 11, the predicted probability changes. Which of the following is true? Select all that apply!The predicted probability increases by 0.0444 if x increases from 20 to 21The predicted probability changes by 0.0444 but it could increase or decreaseThe predicted probability increases by 0.0444The increase in pˆ will not be the same if x increases from 20 to 21

The accuracy rate is calculated as the number of correct predictions divided by the '_______________' number of observations.

In the log-log regression model, both the response variable and the predictor variable are transformed into natural logs. We can write this model ln(y)=β0+β1ln(x)+ε. For 0 < β1 < 1, the log-log regression model implies a positive relationship between x and E(y); as x increases, E(y) increases at a '_________________' rate.

In a quadratic regression model y = β0 + β1x + β2x2 + ε, the coefficient β2 determines the relationship between x and y. Which of the following is true? Select all that apply!is U-shaped (β2 > 0) or inverted U-shaped (β2 < 0).Multiple select question.The relationship between x and y is U-shaped when (β2 < 0)The relationship between x and y is U-shaped when (β2 > 0)The relationship between x and y is an inverted U-shaped when (β2 > 0)The relationship between x and y is an inverted U-shaped when (β2 < 0)

The natural logarithm converts changes in a variable into '_____________________' changes

Using the estimated equation Salary = 44.0073 + 6.6227GPA + 6.6071MIS + 6.7309Statistics. For a graduate with a GPA of 3.5, compute the predicted salary (in $1,000s) for a business graduate with neither an MIS concentration nor a Statistics minor.$73,918$73,794$67,187$80,525

In regression models, we use both numerical and dummy (categorical) variables as predictor variables. What are the 3 interaction variables discussed in the chapter? Select all that apply!Multiple select question.The interaction of two numerical variables.The interaction of two dummy variables with a numerical variableThe interaction of two dummy variablesThe interaction of a dummy variable with a numerical variable