Econometrics Midterm MCQs

In a static model, one or more explanatory variables affect the dependent variable with a lag.

False

The Least Absolute Deviations (LAD) estimator in a linear model minimises the sum of squared residuals.

False

The measurement error always results in biased OLS estimation.

False

Time series regression is based on series which exhibit serial correlation.

False

Sequential exogeneity is implied by dynamic completeness.

True

The homoskedasticity assumption in time series regression suggests that the variance of the error term cannot be a function of time.

True

Weakly dependent processes are said to be integrated of order zero.

True

Sample selection based on the dependent variable is called _____.

endogenous sample selection

The model: Yt = β0 + β1ct + ut, t = 1,2,.......n, is an example of a(n):

finite distributed lag model

Refer to the following model: yt = α0 + β0st + β1st-1 + β2st-2 + β3st-3 + ut. This is an example of a(n):

finite distributed lag model of order 3

A regression model suffers from functional form misspecification if _____. a. a key variable is categorical variable. b. the dependent variable is a dummy variable. c. the coefficient of a key variable is zero.

none of the above is right

An explanatory variable is called exogenous if it is correlated with the error term.

False

Which of the following is true? a. A functional form misspecification can occur if the level of a variable is used when the logarithm is more appropriate. b. A functional form misspecification occurs only if a key variable is uncorrelated with the error term. c. A functional form misspecification does not lead to biasedness in the ordinary least squares estimators. d. A functional form misspecification does not lead to inconsistency in the ordinary least squares estimators.

A functional form misspecification can occur if the level of a variable is used when the logarithm is more appropriate.

Which of the following statements about time series regression is true? a. A model with a lagged dependent variable cannot satisfy the strict exogeneity assumption. b. Stationarity is critical for OLS to have its standard asymptotic properties. c. Efficient static models can be estimated for nonstationary time series. d. In an autoregressive model, the dependent variable in the current time period varies with the error term of previous time periods.

A model with a lagged dependent variable cannot satisfy the strict exogeneity assumption

Under assumptions TS.1 through TS.5, which of the following statements will be true of the following model: yt = α0 + α1dt + ut? a. d can have a lagged effect on y. b. ut can be correlated with past and future values of d. c. Changes in the error term cannot cause future changes in d. d. Changes in d cannot cause changes in y at the same point of time.

Changes in the error term cannot cause future changes in d

The model yt = yt - 1 + et, t = 1, 2, ... represents a:

random walk process.

In the model yt = α0 + α1xt1 + α2xt2 + ..... + αkxtk + ut, the explanatory variables, xt = (xt1, xt2 ...., xtk), are sequentially exogenous if:

E(ut|xt , xt-1, ..., x1) = E(ut) = 0 for any t

Consider the model: yt = α0 + α1xt + α2zt + ut. Let Xt=( xt, zt ). Assume that {( Xt, yt): t=1,2...} is stationary and weakly dependent, and no perfect collinearity. The condition sufficient for consistency of OLS is:

E(ut|xt, zt) = 0.

A stochastic process {xt: t = 1,2,....} with a finite second moment [E(xt2) < ∞] is covariance stationary if:

E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends only on 'h' and not on 't'

Which of the following types of sampling always causes bias or inconsistency in the ordinary least squares estimators? a. Random sampling b. Exogenous sampling c. Endogenous sampling d. Stratified sampling

Endogenous sampling

True or False. Like cross-sectional observations, we can assume that most time series observations are independently distributed.

False. Most time series processes are correlated over time, and many of them strongly correlated. This means they cannot be independent across observations, which simply represent different time periods. Even series that do appear to be roughly uncorrelated - such as stock returns - do not appear to be independently distributed.

True or False. A trending variable cannot be used as the dependent variable in multiple regression analysis.

False. Trending variables are used all the time as dependent variables in a regression model. We do need to be careful in interpreting the results because we may simply find a spurious association between yt and trending explanatory variables. Including a trend in the regression is a good idea with trending dependent or independent variables. As discussed in Section 10.5, the usual R-squared can be misleading when the dependent variable is trending.

Which of the following is true of measurement error? a. If measurement error in a dependent variable has zero mean, the ordinary least squares estimators for the intercept are biased and inconsistent. b. If measurement error in an independent variable is uncorrelated with the observed variables, the ordinary least squares estimators are unbiased. c. If measurement error in an independent variable is uncorrelated with other independent variables, all estimators are biased. d. If measurement error in a dependent variable is correlated with the independent variables, the ordinary least squares estimators are unbiased.

If measurement error in an independent variable is uncorrelated with the observed variables, the ordinary least squares estimators are unbiased.

Which of the following is true of measurement error?

If measurement error in an independent variable is uncorrelated with the variable, the ordinary least squares estimators are unbiased.

Which of the following is an assumption necessary for OLS to be unbiased in time series regression? a. A time series process follows a model that is nonlinear in parameters. b. In a time series process, no independent variable is a perfect linear combination of the others. c. In a time series process, at least one independent variable is a constant. d. For each time period, the expected value of the error ut, given the explanatory variables for all time periods, is positive.

In a time series process, no independent variable is a perfect linear combination of the others.

Which of the following is an assumption on which time series regression is based?

In a time series process, no independent variable is a perfect linear combination of the others.

Which of the following is true of Regression Specification Error Test (RESET)?

It tests if the functional form of a regression model is misspecified.

Which of the following is true of Regression Specification Error Test (RESET)? a. It tests if the functional form of a regression model is misspecified. b. It detects the presence of dummy variables in a regression model. c. It helps in the detection of heteroskedasticity when the functional form of the model is correctly specified. d. It helps in the detection of multicollinearity among the independent variables in a regression model.

It tests if the functional form of a regression model is misspecified.

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation? a. OLS is more computationally intensive than LAD. b. OLS is more sensitive to outlying observations than LAD. c. OLS is justified for very large sample sizes while LAD is justified for smaller sample sizes. d. OLS is designed to estimate the conditional median of the dependent variable while LAD is designed to estimate the conditional mean.

OLS is more sensitive to outlying observations than LAD

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation?

OLS is more sensitive to outlying observations than LAD.

Which of the following assumptions is needed for the plug-in solution to the omitted variables problem to provide consistent estimators? a. The error term in the regression model exhibits heteroskedasticity. b. The error term in the regression model is uncorrelated with all the independent variables. c. The proxy variable is uncorrelated with the dependent variable. d. The proxy variable is correlated with the unobserved key variable.

The error term in the regression model is uncorrelated with all the independent variables.

Which of the following statements is true of dynamically complete models? a. There is scope of adding more lags to the model to better forecast the dependent variable. b. The problem of serial correlation does not exist in dynamically complete models. c. All econometric models are dynamically complete. d. Sequential endogeneity is implied by dynamic completeness.

The problem of serial correlation does not exist in dynamically complete models.

Which of the following statements is true? a. A random walk process is stationary. b. The variance of a random walk process increases as a linear function of time. c. Adding a drift term to a random walk process makes it stationary. d. The variance of a random walk process with a drift decreases as an exponential function of time.

The variance of a random walk process increases as a linear function of time.

Which of the following correctly identifies a difference between cross-sectional data and time series data?

Time series data is based on temporal ordering, whereas cross-sectional data is not.

Covariance stationarity focusses only on the first two moments of a stochastic process.

True

Dummy variables can be used to address the problem of seasonality in regression models.

True

True or False. The OLS estimator in a time series regression is unbiased under the first three Gauss- Markov assumptions.

True. This follows immediately from Theorem 10.1. In particular, we do not need the homoskedasticity and no serial correlation assumptions.

True or False. Seasonality is not an issue when using annual time series observations.

True. With annual data, each time period represents a year and is not associated with any season.

Suppose ut is the error term for time period 't' in a time series regression model the explanatory variables are xt = (xt1, xt2 ...., xtk). The assumption that the errors are contemporaneously homoskedastic implies that:

Var(ut|xt) = σ2

Suppose ut is the error term for time period 't' in a time series regression model the explanatory variables are xt = (xt1, xt2 ...., xtk). The assumption that the errors are contemporaneously homoskedastic implies that:

Var(ut|xt) = σ2.

If ut refers to the error term at time 't' and yt - 1 refers to the dependent variable at time 't - 1', for an AR(1) process to be homoskedastic, it is required that:

Var(ut|yt - 1) = Var(yt|yt-1) = σ2

Which of the following statements is true? a. The average of an exponential time series is a linear function of time. b. The average of a linear sequence is an exponential function of time. c. When a series has the same average growth rate from period to period, it can be approximated with an exponential trend. d. When a series has the same average growth rate from period to period, it can be approximated with a linear trend.

When a series has the same average growth rate from period to period, it can be approximated with an exponential trend.

A static model is postulated when:

a change in the independent variable at time 't' is believed to have an immediate effect on the dependent variable

A regression model suffers from functional form misspecification if _____. a. a key variable is binary.

an interaction term is omitted

A process is stationary if:

any collection of random variables in a sequence is taken and shifted ahead by h time periods, the joint probability distribution remains unchanged

Which of the following statements is true of dynamically complete models? a. There is scope of adding more lags to the model to better forecast the dependent variable. b. The problem of serial correlation does not exist in dynamically complete models. c. All econometric models are dynamically complete. d. Sequential endogeneity is implied by dynamic completeness.

b. The problem of serial correlation does not exist in dynamically complete models.

Which of the following statements is true? a. A random walk process is stationary. b. The variance of a random walk process increases as a linear function of time. c. Adding a drift term to a random walk process makes it stationary. d. The variance of a random walk process with a drift decreases as an exponential function of time.

b. The variance of a random walk process increases as a linear function of time.

The Least Absolute Deviations (LAD) estimators in a linear model minimize the sum of squared residuals.

false

A seasonally adjusted series is one which:

has seasonal factors removed from it.

A proxy variable _____.

is used when data on a key independent variable is unavailable

Consider the following equation for household consumption expenditure: Consmptn=β0+β1Inc+β2Consmptn-1+u, where 'Consmptn' measures the monthly consumption expenditure of a household, 'Inc' is the household income and 'Consmptn-1' is the consumption expenditure in the previous month. Consmptn-1 is a _____ variable.

lagged dependent

The model yt =et +β1et-1+β2et-2,t=1,2,.....,where et is an i.i.d. sequence with zero mean and variance σ2 e represents a(n):

moving average process of order two.

A stochastic process refers to a:

sequence of random variables indexed by time.

The method of data collection in which the population is divided into nonoverlapping, exhaustive groups is called _____.

stratified sampling

A covariance stationary time series is weakly dependent if:

the correlation between the independent variable at time 't' and the independent variable at time 't + h' goes to 0 as h → ∞.

Adding a time trend can make an explanatory variable more significant if:

the dependent and independent variables have different kinds of trends, but movement in the independent variable about its trend line causes movement in the dependent variable away from its trend line.

3. If a process is said to be integrated of order one, or I(1), _____.

the first difference of the process is weakly dependent

f a process is said to be integrated of order one, or I(1), _____.

the first difference of the process is weakly dependent

Consider the following regression model: log(y) = β0 + β1x1 + β2x1 x3+ β3x3 + u, with βk≠0 (k=0,1,...,3). This model will suffer from functional form misspecification if _____.

the interaction term x1x3 is omitted from the model

Refer to the following model: yt =α0 +β0st+β1st-1 +β2st-2+β3st-3 +ut. β0+β1+β2+β3 represents:

the long-run change in y given a permanent increase in s

The classical errors-in-variables (CEV) assumption is that _____.

the measurement error is uncorrelated with the unobserved explanatory variable

The classical errors-in-variables (CEV) assumption is that _____.

the measurement error is uncorrelated with the unobserved variable

A measurement error occurs in a regression model when _____.

the observed value of a variable used in the model differs from its actual value

If an explanatory variable is strictly exogenous it implies that:

the variable cannot react to what has happened to the dependent variable in the past.

The sample size for a time series data set is the number of:

time periods over which we observe the variables of interest.

Consider the following regression model: log(y)= β0+β1x1+β2x12+ β3x3+u. This model will suffer from functional form misspecification if _____.

x12 is omitted from the model