Econometrics Questions

In a simple linear regression analysis the quantity that gives the amount by which the dependent variable changes for a unit change in the independent variable is called the a. coefficient of determination. b. slope of the regression line. c. correlation coefficient. d. standard error.

In a linear regression model, the variable (or variables) used for predicting or explaining values of the response variable are known as the __________. It(they) is(are) denoted by x. a. dependent variable b. independent variable c. residual variable d. regression variable

. The value of an independent variable from the prior period is referred to as a a. lagged variable. b. dummy variable. c. predictor variable. d. categorical variable.

For causal modeling, __________ are used to detect linear or nonlinear relationships between the independent and dependent variables. a. descriptive statistics on the data b. scatter charts c. contingency tables d. pie charts

The process of __________ might be used to determine the value of the smoothing constant that minimizes the mean squared error. a. quantization b. nonlinear optimization c. clustering d. curve fitting

Which of the following is true of the exponential smoothing coefficient? a. It is a randomly generated value between -1 and +1. b. It is small for a time series that has relatively little random variability. c. It is chosen as the value that minimizes a selected measure of forecast accuracy such as the mean squared error. d. It is computed in relation with the order value, k, for the moving averages.

A causal model provides evidence of __________ between an independent variable and the variable to be forecast. a. a causal relationship b. an association c. no relationship d. a seasonal relationship

Autoregressive models a. use the average of the most recent data values in the time series as the forecast for the next period. b. are used to smooth out random fluctuations in time series. c. relate a time series to other variables that are believed to explain or cause its behavior. d. occur whenever all the independent variables are previous values of the time series.

The exponential smoothing forecast for period t + 1 is a weighted average of the a. forecast value in period t with weight α and the actual value for period t with weight 1 - α. b. actual value in period t + 1 with weight α and the forecast for period t with weight 1 - α. c. forecast value in period t - 1 with weight α and the forecast for period t with weight 1 - α. d. actual value in period t with weight α and the forecast for period t with weight 1 - α.

__________ uses a weighted average of past time series values as the forecast. a. The qualitative method b. Exponential smoothing c. Correlation analysis d. The causal model

A time series with a seasonal pattern can be modeled by treating the season as a a. predictor variable. b. dependent variable. c. dummy variable. d. quantitative variable.

With reference to time series data patterns, a cyclical pattern is the component of the time series that a. shows a periodic pattern lasting one year or less. b. does not vary with respect to time. c. shows a periodic pattern lasting more than one year. d. is characterized by a linear variation of the dependent variable with respect to time.

Causal models a. provide evidence of a causal relationship between an independent variable and the variable to be forecast. b. use the average of the most recent data values in the time series as the forecast for the next period. c. occur whenever all the independent variables are previous values of the same time series. d. relate a time series to other variables that are believed to explain or cause its behavior.

If the forecasted value of the time series variable for period 2 is 22.5 and the actual value observed for period 2 is 25, what is the forecast error in period 2? a. 3 b. 2 c. 2.5 d. -2.5

The moving averages method refers to a forecasting method that a. is used when considerable trend, cyclical, or seasonal effects are present. b. uses regression relationship based on past time series values to predict the future time series values. c. relates a time series to other variables that are believed to explain or cause its behavior. d. uses the average of the most recent data values in the time series as the forecast for the next period.

Which of the following measures of forecast accuracy is susceptible to the problem of positive and negative forecast errors offsetting one another? a. Mean absolute error b. Mean forecast error c. Mean squared error d. Mean absolute percentage error

A positive forecast error indicates that the forecasting method ________ the dependent variable. a. overestimated b. underestimated c. accurately estimated d. closely approximated

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean squared error.Period Actual Demand Forecasted Demand1 12 - -2 15 123 14 154 18 16 a. 3.33 b. 4.67 c. 5.33 d. 6.67

The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast. These methods measure forecast accuracy by a. determining how well a particular forecasting method is able to reproduce the time series data that are already available. b. using the current value to estimate how well the model generates previous values correctly. c. predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were. d. adjusting the scale of the data.

Which of the following statements is the objective of the moving averages and exponential smoothing methods? a. To maximize forecast accuracy measures b. To smooth out random fluctuations in the time series c. To characterize the variable fluctuations by an exponential equation d. To transform a nonstationary time series into a stationary series