講者：USER
日期：2021-10-15
觀看: 640
  • 00:00 1.
    LS Estimators and Properties
  • 00:12 2.
    Goodness of Fit
  • 00:13 3.
    Simple Regression
  • 00:14 4.
    Estimating Simple Regression
  • 00:15 5.
    Simple Regression
  • 00:16 6.
    Estimating Simple Regression
  • 00:16 7.
    Simple Regression Example
  • 00:17 8.
    Positive Definite Matrix
  • 00:17 9.
    Second Order Condition
  • 00:18 10.
    Digression to Derivative of Matrix:
  • 00:18 11.
    Find the Least Squares Estimator
  • 00:23 12.
    Estimation
  • 00:24 13.
    Find the Least Squares Estimator
  • 00:24 14.
    Estimation
  • 00:25 15.
    Linear Regression Model (3)
  • 00:26 16.
    Estimation
  • 00:27 17.
    Find the Least Squares Estimator
  • 00:28 18.
    Digression to Derivative of Matrix:
  • 00:29 19.
    Second Order Condition
  • 00:29 20.
    Positive Definite Matrix
  • 00:30 21.
    Simple Regression Example
  • 00:31 22.
    Estimating Simple Regression
  • 02:52 23.
    Simple Regression
  • 03:22 24.
    Goodness of Fit
  • 11:36 25.
    Simple Regression
  • 11:37 26.
    Estimating Simple Regression
  • 11:39 27.
    Simple Regression Example
  • 13:33 28.
    Estimating Simple Regression
  • 13:33 29.
    Simple Regression
  • 13:34 30.
    Goodness of Fit
  • 16:33 31.
    LS Estimators and Properties
  • 19:55 32.
    Four Assumptions
  • 24:46 33.
    Four Assumptions
  • 24:54 34.
    A Digression to Variance Covariance Matrix of a Vector
  • 24:55 35.
    Four Assumptions
  • 25:10 36.
    Four Assumptions
  • 25:37 37.
    Four Assumptions
  • 25:50 38.
    A Digression to Variance Covariance Matrix of a Vector
  • 33:56 39.
    Assumption A3
  • 38:42 40.
    Properties of LS Estimator: Unbiased
  • 38:43 41.
    Assumption A3
  • 38:44 42.
    A Digression to Variance Covariance Matrix of a Vector
  • 38:44 43.
    Four Assumptions
  • 40:04 44.
    Four Assumptions
  • 40:04 45.
    LS Estimators and Properties
  • 41:13 46.
    Four Assumptions
  • 41:14 47.
    Four Assumptions
  • 41:15 48.
    A Digression to Variance Covariance Matrix of a Vector
  • 41:16 49.
    Assumption A3
  • 41:16 50.
    Properties of LS Estimator: Unbiased
  • 49:29 51.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 52:31 52.
    Properties of LS Estimator: Unbiased
  • 53:20 53.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:00:47 54.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:01:08 55.
    Properties of LS Estimator: Unbiased
  • 1:01:09 56.
    Assumption A3
  • 1:01:10 57.
    A Digression to Variance Covariance Matrix of a Vector
  • 1:01:10 58.
    Four Assumptions
  • 1:01:27 59.
    A Digression to Variance Covariance Matrix of a Vector
  • 1:01:27 60.
    Assumption A3
  • 1:01:28 61.
    Properties of LS Estimator: Unbiased
  • 1:01:28 62.
    VAR-COV Of 𝛽 :VAR( 𝛽 )
  • 1:01:29 63.
    What Have We Got So Far?
  • 1:04:49 64.
    Gauss Markov Theorem (I)
  • 1:04:51 65.
    Gauss Markov Theorem (II)
  • 1:04:51 66.
    Gauss Markov Theorem (III)
  • 1:04:52 67.
    What do we need to do for statistical inference?
  • 1:04:53 68.
    Properties of the OLS Estimator
  • 1:04:55 69.
    What do we need to do for statistical inference?
  • 1:04:56 70.
    Gauss Markov Theorem (III)
  • 1:04:56 71.
    Gauss Markov Theorem (II)
  • 1:04:57 72.
    Gauss Markov Theorem (I)
  • 1:04:58 73.
    Gauss Markov Theorem (II)
  • 1:04:59 74.
    Gauss Markov Theorem (I)
  • 1:05:03 75.
    What Have We Got So Far?
  • 1:05:20 76.
    Gauss Markov Theorem (I)
  • 1:09:05 77.
    Gauss Markov Theorem (II)
  • 1:15:43 78.
    Gauss Markov Theorem (III)
  • 1:15:47 79.
    Gauss Markov Theorem (II)
  • 1:16:06 80.
    Gauss Markov Theorem (III)
  • 1:21:02 81.
    What do we need to do for statistical inference?
  • 1:29:20 82.
    Properties of the OLS Estimator
  • 1:33:14 83.
    Statistical Inference
  • 1:33:26 84.
    Hypothesis Testing
  • 1:33:27 85.
    Statistical Inference
  • 1:47:17 86.
    Hypothesis Testing
  • 1:47:18 87.
    The Presidential Election Example
  • 1:47:19 88.
    The Outcome of the US Presidential Election 1892-2012
  • 1:47:19 89.
    The US Presidential Election Again
  • 1:47:20 90.
    The Presidential Election
  • 1:53:17 91.
    The US Presidential Election Again
  • 1:53:18 92.
    The Outcome of the US Presidential Election 1892-2012
  • 1:53:20 93.
    The Presidential Election Example
  • 1:53:24 94.
    Hypothesis Testing
  • 1:53:26 95.
    The Presidential Election Example
  • 1:53:30 96.
    The Presidential Election Example
  • 1:56:17 97.
    The Outcome of the US Presidential Election 1892-2012
  • 1:57:44 98.
    The US Presidential Election Again
  • 2:02:39 99.
    The Presidential Election
  • 2:03:12 100.
    The US Presidential Election Again
  • 2:03:17 101.
    The Presidential Election
  • 2:04:50 102.
    Presidential Election Estimation
  • 2:06:48 103.
    The Presidential Election
  • 2:07:51 104.
    The US Presidential Election Again
  • 2:07:53 105.
    The Outcome of the US Presidential Election 1892-2012
  • 2:07:54 106.
    The US Presidential Election Again
  • 2:07:54 107.
    The Presidential Election
  • 2:07:55 108.
    Presidential Election Estimation
  • 2:08:03 109.
    The Presidential Election
  • 2:08:16 110.
    Presidential Election Estimation
  • 2:09:15 111.
    The Presidential Election
  • 2:09:16 112.
    The US Presidential Election Again
  • 2:11:09 113.
    The Presidential Election
  • 2:11:09 114.
    Presidential Election Estimation
  • 2:15:57 115.
    The Presidential Election
  • 2:16:00 116.
    Presidential Election Estimation
  • 2:16:02 117.
    The Presidential Election
  • 2:16:16 118.
    Presidential Election Estimation
  • 2:16:17 119.
    Hypothesis Testing: Ex. 3a)
  • 2:17:04 120.
    Presidential Election Estimation
  • 2:17:10 121.
    Hypothesis Testing: Ex. 3a)
  • 2:18:10 122.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 2:21:40 123.
    Hypothesis Testing: Ex. 3a)
  • 2:21:41 124.
    Presidential Election Estimation
  • 2:21:42 125.
    The Presidential Election
  • 2:22:39 126.
    Presidential Election Estimation
  • 2:22:40 127.
    Hypothesis Testing: Ex. 3a)
  • 2:22:40 128.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 2:22:47 129.
    Hypothesis Testing: Ex. 3a)
  • 2:22:48 130.
    Presidential Election Estimation
  • 2:22:48 131.
    The Presidential Election
  • 2:22:48 132.
    The US Presidential Election Again
  • 2:22:49 133.
    The Presidential Election
  • 2:23:12 134.
    Presidential Election Estimation
  • 2:23:13 135.
    Hypothesis Testing: Ex. 3a)
  • 2:23:14 136.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
  • 2:24:02 137.
    Statistical Inference for Linear Restrictions
  • 2:24:04 138.
    Alternative Test for H0: b2 =b3 (b2-b3 =0)
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metrics-lec 2-multiple regression-1-20211015
長度: 2:24:52, 瀏覽: 641, 最近修訂: 2021-10-15
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    • 00:00 1.
      LS Estimators and Properties
    • 00:12 2.
      Goodness of Fit
    • 00:13 3.
      Simple Regression
    • 00:14 4.
      Estimating Simple Regression
    • 00:15 5.
      Simple Regression
    • 00:16 6.
      Estimating Simple Regression
    • 00:16 7.
      Simple Regression Example
    • 00:17 8.
      Positive Definite Matrix
    • 00:17 9.
      Second Order Condition
    • 00:18 10.
      Digression to Derivative of Matrix:
    • 00:18 11.
      Find the Least Squares Estimator
    • 00:23 12.
      Estimation
    • 00:24 13.
      Find the Least Squares Estimator
    • 00:24 14.
      Estimation
    • 00:25 15.
      Linear Regression Model (3)
    • 00:26 16.
      Estimation
    • 00:27 17.
      Find the Least Squares Estimator
    • 00:28 18.
      Digression to Derivative of Matrix:
    • 00:29 19.
      Second Order Condition
    • 00:29 20.
      Positive Definite Matrix
    • 00:30 21.
      Simple Regression Example
    • 00:31 22.
      Estimating Simple Regression
    • 02:52 23.
      Simple Regression
    • 03:22 24.
      Goodness of Fit
    • 11:36 25.
      Simple Regression
    • 11:37 26.
      Estimating Simple Regression
    • 11:39 27.
      Simple Regression Example
    • 13:33 28.
      Estimating Simple Regression
    • 13:33 29.
      Simple Regression
    • 13:34 30.
      Goodness of Fit
    • 16:33 31.
      LS Estimators and Properties
    • 19:55 32.
      Four Assumptions
    • 24:46 33.
      Four Assumptions
    • 24:54 34.
      A Digression to Variance Covariance Matrix of a Vector
    • 24:55 35.
      Four Assumptions
    • 25:10 36.
      Four Assumptions
    • 25:37 37.
      Four Assumptions
    • 25:50 38.
      A Digression to Variance Covariance Matrix of a Vector
    • 33:56 39.
      Assumption A3
    • 38:42 40.
      Properties of LS Estimator: Unbiased
    • 38:43 41.
      Assumption A3
    • 38:44 42.
      A Digression to Variance Covariance Matrix of a Vector
    • 38:44 43.
      Four Assumptions
    • 40:04 44.
      Four Assumptions
    • 40:04 45.
      LS Estimators and Properties
    • 41:13 46.
      Four Assumptions
    • 41:14 47.
      Four Assumptions
    • 41:15 48.
      A Digression to Variance Covariance Matrix of a Vector
    • 41:16 49.
      Assumption A3
    • 41:16 50.
      Properties of LS Estimator: Unbiased
    • 49:29 51.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 52:31 52.
      Properties of LS Estimator: Unbiased
    • 53:20 53.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:00:47 54.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:01:08 55.
      Properties of LS Estimator: Unbiased
    • 1:01:09 56.
      Assumption A3
    • 1:01:10 57.
      A Digression to Variance Covariance Matrix of a Vector
    • 1:01:10 58.
      Four Assumptions
    • 1:01:27 59.
      A Digression to Variance Covariance Matrix of a Vector
    • 1:01:27 60.
      Assumption A3
    • 1:01:28 61.
      Properties of LS Estimator: Unbiased
    • 1:01:28 62.
      VAR-COV Of 𝛽 :VAR( 𝛽 )
    • 1:01:29 63.
      What Have We Got So Far?
    • 1:04:49 64.
      Gauss Markov Theorem (I)
    • 1:04:51 65.
      Gauss Markov Theorem (II)
    • 1:04:51 66.
      Gauss Markov Theorem (III)
    • 1:04:52 67.
      What do we need to do for statistical inference?
    • 1:04:53 68.
      Properties of the OLS Estimator
    • 1:04:55 69.
      What do we need to do for statistical inference?
    • 1:04:56 70.
      Gauss Markov Theorem (III)
    • 1:04:56 71.
      Gauss Markov Theorem (II)
    • 1:04:57 72.
      Gauss Markov Theorem (I)
    • 1:04:58 73.
      Gauss Markov Theorem (II)
    • 1:04:59 74.
      Gauss Markov Theorem (I)
    • 1:05:03 75.
      What Have We Got So Far?
    • 1:05:20 76.
      Gauss Markov Theorem (I)
    • 1:09:05 77.
      Gauss Markov Theorem (II)
    • 1:15:43 78.
      Gauss Markov Theorem (III)
    • 1:15:47 79.
      Gauss Markov Theorem (II)
    • 1:16:06 80.
      Gauss Markov Theorem (III)
    • 1:21:02 81.
      What do we need to do for statistical inference?
    • 1:29:20 82.
      Properties of the OLS Estimator
    • 1:33:14 83.
      Statistical Inference
    • 1:33:26 84.
      Hypothesis Testing
    • 1:33:27 85.
      Statistical Inference
    • 1:47:17 86.
      Hypothesis Testing
    • 1:47:18 87.
      The Presidential Election Example
    • 1:47:19 88.
      The Outcome of the US Presidential Election 1892-2012
    • 1:47:19 89.
      The US Presidential Election Again
    • 1:47:20 90.
      The Presidential Election
    • 1:53:17 91.
      The US Presidential Election Again
    • 1:53:18 92.
      The Outcome of the US Presidential Election 1892-2012
    • 1:53:20 93.
      The Presidential Election Example
    • 1:53:24 94.
      Hypothesis Testing
    • 1:53:26 95.
      The Presidential Election Example
    • 1:53:30 96.
      The Presidential Election Example
    • 1:56:17 97.
      The Outcome of the US Presidential Election 1892-2012
    • 1:57:44 98.
      The US Presidential Election Again
    • 2:02:39 99.
      The Presidential Election
    • 2:03:12 100.
      The US Presidential Election Again
    • 2:03:17 101.
      The Presidential Election
    • 2:04:50 102.
      Presidential Election Estimation
    • 2:06:48 103.
      The Presidential Election
    • 2:07:51 104.
      The US Presidential Election Again
    • 2:07:53 105.
      The Outcome of the US Presidential Election 1892-2012
    • 2:07:54 106.
      The US Presidential Election Again
    • 2:07:54 107.
      The Presidential Election
    • 2:07:55 108.
      Presidential Election Estimation
    • 2:08:03 109.
      The Presidential Election
    • 2:08:16 110.
      Presidential Election Estimation
    • 2:09:15 111.
      The Presidential Election
    • 2:09:16 112.
      The US Presidential Election Again
    • 2:11:09 113.
      The Presidential Election
    • 2:11:09 114.
      Presidential Election Estimation
    • 2:15:57 115.
      The Presidential Election
    • 2:16:00 116.
      Presidential Election Estimation
    • 2:16:02 117.
      The Presidential Election
    • 2:16:16 118.
      Presidential Election Estimation
    • 2:16:17 119.
      Hypothesis Testing: Ex. 3a)
    • 2:17:04 120.
      Presidential Election Estimation
    • 2:17:10 121.
      Hypothesis Testing: Ex. 3a)
    • 2:18:10 122.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 2:21:40 123.
      Hypothesis Testing: Ex. 3a)
    • 2:21:41 124.
      Presidential Election Estimation
    • 2:21:42 125.
      The Presidential Election
    • 2:22:39 126.
      Presidential Election Estimation
    • 2:22:40 127.
      Hypothesis Testing: Ex. 3a)
    • 2:22:40 128.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 2:22:47 129.
      Hypothesis Testing: Ex. 3a)
    • 2:22:48 130.
      Presidential Election Estimation
    • 2:22:48 131.
      The Presidential Election
    • 2:22:48 132.
      The US Presidential Election Again
    • 2:22:49 133.
      The Presidential Election
    • 2:23:12 134.
      Presidential Election Estimation
    • 2:23:13 135.
      Hypothesis Testing: Ex. 3a)
    • 2:23:14 136.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
    • 2:24:02 137.
      Statistical Inference for Linear Restrictions
    • 2:24:04 138.
      Alternative Test for H0: b2 =b3 (b2-b3 =0)
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