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Introduction to  Hypothesis Testing Istanbul Bilgi University FEC 512 Financial Econometrics-I Asst. Prof. Dr. Orhan Erdem
What is a Hypothesis? ,[object Object],[object Object],[object Object],[object Object],Example:  The mean  age of the citizens of this  city is    =  50.
[object Object],[object Object],[object Object],[object Object],[object Object],The Null Hypothesis, H 0
The Alternative Hypothesis, H A ,[object Object],[object Object],[object Object],[object Object],[object Object]
Formulating Hypotheses ,[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Formulating Hypotheses
A Trial as a Hypothesis Test ,[object Object],[object Object],[object Object],[object Object],[object Object]
What to Do with an “Innocent” Defendant ,[object Object],[object Object],[object Object],[object Object],[object Object]
Population Claim:   the population mean age is 50. (Null Hypothesis: REJECT Suppose the sample mean age  is 20:  x = 20 Sample Null Hypothesis 20 likely if    = 50?  Is Hypothesis Testing Process If not likely,   Now select a random sample H 0 :    = 50 ) x
Reason for Rejecting H 0 Sampling Distribution of x    = 50 If  H 0  is true If it is unlikely that we would get a sample mean of this value ... ... then we reject the null hypothesis that    = 50. 20 ... if in fact this were  the population mean… x
Level of Significance,     ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Level of Significance  and the Rejection Region H 0 :  μ   ≥   50   H 1 :  μ  <  50 0 H 0 :  μ   ≤   50   H 1 :  μ  >  50   Represents critical value Lower-tail test Level of significance =   0 Upper-tail test Two-tail test Rejection region is shaded /2 0  /2  H 0 :  μ  =  50   H 1 :  μ   ≠   50
Errors in Making Decisions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Errors in Making Decisions ,[object Object],[object Object],[object Object],(continued)
Outcomes and Probabilities Actual Situation Decision Do Not Reject H 0 No error (1 -  )  Type II Error   (  β  ) Reject H 0 Type I Error (  )  Possible Hypothesis Test Outcomes H 0  False H 0  True Key: Outcome (Probability) No Error   ( 1 -  β  )
Power of the Test ,[object Object],[object Object],[object Object]
Hypothesis Tests for the Mean ,[object Object],σ  Known σ  Unknown Hypothesis  Tests for  
Test of Hypothesis  f or the Mean  ( σ  Known) ,[object Object],The  decision rule  is: σ  Known σ  Unknown Hypothesis  Tests for   Consider the test (Assume the population is normal)
Level of Significance  and the Rejection Region H 0 :  μ   ≥   50   H A :  μ  <  50 50 H 0 :  μ   ≤   50   H A :  μ  >  50 H 0 :  μ  =  50   H A :  μ   ≠   50   /2 Lower tail test Level of significance =   50 /2  Upper tail test Two tailed test 5 0  - ? ? - ? ? Reject H 0 Reject H 0 Reject H 0 Reject H 0 Do not reject H 0 Do not reject H 0 Do not reject H 0 Example: Example: Example:
Level of Significance  and the Rejection Region H 0 :  μ   ≥   50   H A :  μ  <  50 0 H 0 :  μ   ≤   50   H A :  μ  >  50 H 0 :  μ  =  50   H A :  μ   ≠   50   /2 Lower tail test Level of significance =   0 /2  Upper tail test Two tailed test 0  -z α z α -z α /2 z α /2 Reject H 0 Reject H 0 Reject H 0 Reject H 0 Do not reject H 0 Do not reject H 0 Do not reject H 0 Example: Example: Example:
Upper Tail Tests Reject H 0 Do not reject H 0  z α 0 μ 0 H 0 :  μ   ≤   μ 0   H 1 :  μ  >  μ 0   Critical value Z Alternate rule:
Lower Tail Tests Reject H 0 Do not reject H 0 ,[object Object],[object Object], -z α x α - z α x α 0 μ H 0 :  μ   ≥   μ 0   H A :  μ  <  μ 0   I always find a corresponding z value to x
Two Tailed Tests ,[object Object],[object Object],Do not reject H 0 Reject H 0 Reject H 0  /2 -z α /2 x α /2 ±  z α /2 x α /2 0 μ 0 H 0 :   μ =  μ 0   H A :   μ      μ 0 z α /2 x α /2 Lower Upper x α /2 Lower Upper  /2
Example: Upper-Tail Z Test  for Mean  (   Known) ,[object Object],H 0 :  μ   ≤ 52  the average is not over 52 per month H 1 :  μ  > 52  the average  is  greater than 52  (i.e., sufficient evidence exists to support the  manager’s claim) Form hypothesis test:
[object Object],[object Object],Reject H 0 Do not reject H 0  = .10 1.28 0 Reject H 0 Example: Find Rejection Region (continued)
[object Object],[object Object],[object Object],Example: Sample Results (continued)
Example: Decision ,[object Object],Reject H 0 Do not reject H 0   = .10 1.28 0 Reject H 0 Do not reject H 0  since z = 0.88  <  1.28 i.e.: there is not sufficient evidence that the mean  age  is over   52 z  = 0.88 (continued)
Calculating the Test Statistic    Known Large  Samples    Unknown Hypothesis  Tests for   Small  Samples The test statistic is: But is sometimes approximated using a z: (continued)
Calculating the Test Statistic    Known Large  Samples    Unknown Hypothesis  Tests for   Small  Samples The test statistic is: (The population must be approximately normal) (continued)
Review: Steps in Hypothesis Testing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Risk and Return of an Equity Mutual Fund ( σ  unknown) ,[object Object]
Solution Test the claim that the true mean # of  ABC Fund return  is at least  1.9 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Hypothesis Testing Example ,[object Object],Reject H 0 Do not reject H 0    = .05 - t α = -1.6 8 0 This is a one-tailed test with    = .05.  Since  σ  is  not  known , the cutoff value is a  t  value . But since we have 36 data, z-value can also be used. Reject H 0  if  t  <  t   = -1. 68  ;  otherwise do not reject H 0 (continued)
[object Object],[object Object],Hypothesis Testing Example
[object Object],Hypothesis Testing Example Reject H 0 Do not reject H 0    = .05 -1. 68 0 -0 . 66 Since  t  = -1.6 8   <-0.66 ,  we  fail to  reject the null hypothesis   that the mean  return for ABC Fund is at least 1.9%   (continued) z
[object Object],[object Object],[object Object],[object Object],[object Object],p-Value Approach to Testing
[object Object],[object Object],Example: p-Value Solution Reject H 0   = .10 Do not reject H 0 1.28 0 Reject H 0 Z  = .88 (continued) p-value = .1894 Do not reject H 0  since p-value = .1894  >     = .10
p-value example ,[object Object],p-value = 0.26    = .05 -1.68 -0.66 x
[object Object],[object Object],[object Object],p-value example Here:  p-value =  0.26     = .05 Since  .05  <  0.26 , we  fail to  reject the null hypothesis (continued)
Type II Error  (Revisited) ,[object Object],[object Object],Reject  H 0 :  μ     52 Do not reject  H 0  :  μ     52 52 50 Suppose we fail to reject  H 0 :  μ     52   when in fact the true mean is  μ  = 50 
Type II Error ,[object Object],Reject  H 0 :       52 Do not reject  H 0  :       52 52 50 This is the true distribution of  x  if    = 50 This is the range of  x where H 0  is  not rejected (continued)
Type II Error ,[object Object],Reject  H 0 :  μ     52 Do not reject  H 0  :  μ     52  52 50 β Here,  β  = P( x    cutoff )  if  μ  = 50 (continued)
[object Object],Calculating  β Reject  H 0 :  μ     52 Do not reject  H 0  :  μ     52  52 50 So  β  = P( x    50.766 ) if  μ  = 50 (for H 0  :  μ     52) 50.766
[object Object],Calculating  β Reject  H 0 :  μ     52 Do not reject  H 0  :  μ     52  52 50 (continued) Probability of type II error:  β  = .1539
Type I & II Error Relationship ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Factors Affecting Type II Error ,[object Object],[object Object],[object Object],[object Object],[object Object]
Making Errors ,[object Object],[object Object],[object Object],[object Object]
Making Errors ,[object Object],[object Object],[object Object],[object Object]
Making Errors (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Power ,[object Object],[object Object],[object Object]
Type II Error ,[object Object],Reject  H 0 :  μ     52 Do not reject  H 0  :  μ     52  52 50 β (continued) Power
Power (cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Reducing Both Type I and Type II Error ,[object Object],[object Object],[object Object]

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FEC 512.05

  • 1. Introduction to Hypothesis Testing Istanbul Bilgi University FEC 512 Financial Econometrics-I Asst. Prof. Dr. Orhan Erdem
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  • 9. Population Claim: the population mean age is 50. (Null Hypothesis: REJECT Suppose the sample mean age is 20: x = 20 Sample Null Hypothesis 20 likely if  = 50?  Is Hypothesis Testing Process If not likely, Now select a random sample H 0 :  = 50 ) x
  • 10. Reason for Rejecting H 0 Sampling Distribution of x  = 50 If H 0 is true If it is unlikely that we would get a sample mean of this value ... ... then we reject the null hypothesis that  = 50. 20 ... if in fact this were the population mean… x
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  • 12. Level of Significance and the Rejection Region H 0 : μ ≥ 50 H 1 : μ < 50 0 H 0 : μ ≤ 50 H 1 : μ > 50   Represents critical value Lower-tail test Level of significance =  0 Upper-tail test Two-tail test Rejection region is shaded /2 0  /2  H 0 : μ = 50 H 1 : μ ≠ 50
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  • 15. Outcomes and Probabilities Actual Situation Decision Do Not Reject H 0 No error (1 - )  Type II Error ( β ) Reject H 0 Type I Error ( )  Possible Hypothesis Test Outcomes H 0 False H 0 True Key: Outcome (Probability) No Error ( 1 - β )
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  • 19. Level of Significance and the Rejection Region H 0 : μ ≥ 50 H A : μ < 50 50 H 0 : μ ≤ 50 H A : μ > 50 H 0 : μ = 50 H A : μ ≠ 50   /2 Lower tail test Level of significance =  50 /2  Upper tail test Two tailed test 5 0  - ? ? - ? ? Reject H 0 Reject H 0 Reject H 0 Reject H 0 Do not reject H 0 Do not reject H 0 Do not reject H 0 Example: Example: Example:
  • 20. Level of Significance and the Rejection Region H 0 : μ ≥ 50 H A : μ < 50 0 H 0 : μ ≤ 50 H A : μ > 50 H 0 : μ = 50 H A : μ ≠ 50   /2 Lower tail test Level of significance =  0 /2  Upper tail test Two tailed test 0  -z α z α -z α /2 z α /2 Reject H 0 Reject H 0 Reject H 0 Reject H 0 Do not reject H 0 Do not reject H 0 Do not reject H 0 Example: Example: Example:
  • 21. Upper Tail Tests Reject H 0 Do not reject H 0  z α 0 μ 0 H 0 : μ ≤ μ 0 H 1 : μ > μ 0 Critical value Z Alternate rule:
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  • 28. Calculating the Test Statistic  Known Large Samples  Unknown Hypothesis Tests for  Small Samples The test statistic is: But is sometimes approximated using a z: (continued)
  • 29. Calculating the Test Statistic  Known Large Samples  Unknown Hypothesis Tests for  Small Samples The test statistic is: (The population must be approximately normal) (continued)
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