Economics Theme

Economics Theme 4 - Econometrics


By: Dr. Nabil Chaiban


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M
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M D A T A F R E Q U E N C Y
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Y L B
     
V T I
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A E C A
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R D E T R E N D I N G O S
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B A I N N E
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C I U A T T E N U A T I O N B I A S D
             
O N X B T T E
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N O I L E M P I R I C A L A N A L Y S I S
             
F M L E V N T
           
I I I E T I
           
D A A H E M
           
E L R Y L A
           
N D Y P A T
           
C I R O S O
           
E S E T T R
         
L T G H I
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E R R O R V A R I A N C E C
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V I E V S E S T I M A T E
           
E B S E I T
           
L U S R S Y
       
T I A M
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I O G E C O N O M E T R I C M O D E L
       
O N E D
   
N E
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C O N D I T I O N A L V A R I A N C E

Across

  1. The interval at which time series data are collected. Yearly, quarterly, and monthly are the most common data frequencies.
  2. The practice of removing the trend from a time series.
  3. Bias in an estimator that is always toward zero; thus, the expected value of an estimator with attenuation bias is less in magnitude than the absolute value of the parameter.
  4. A study that uses data in a formal econometric analysis to test a theory, estimate a relationship, or determine the effectiveness of a policy.
  5. The variance of the error term in a multiple regression model.
  6. The numerical value taken on by an estimator for a particular sample of data.
  7. An equation relating the dependent variable to a set of explanatory variables and unobserved disturbances, where unknown population parameters determine the ceteris paribus effect of each explanatory variable.
  8. The variance of one random variable, given one or more other random variables.

Down

  1. A variable that takes on the value zero or one.
  2. The hypothesis against which the null hypothesis is tested.
  3. An estimator whose expectation, or sampling mean, is different from the population value it is supposed to be estimating.
  4. A model where the elasticity of the dependent variable. with respect to an explanatory variable, is constant; in multiple regression, both variables appear in logarithmic form.
  5. The probability distribution of the number of successes out of n independent Bernoulli trials, where each trial has the same probability of success.
  6. A regression used to compute a test statistic-such as the test statistics for heteroskedasticity and serial correlation or any other regression that does not estimate the model of primary interest.
  7. The percentage of samples in which we want our confidence interval to contain the population value; 95% is the most common confidence level, but 90% and 99% are also used.
  8. The sum of n numbers divided by n.