Simian Crease
Simian Crease
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  • Просмотров 7 186
17.6. Distribution of OLS estimate of coefficient of unit root process (pure random walk)
17.7. Distribution of OLS estimates of the coefficient of unit root process (case of pure random walk)
본 영상은 OLS 방식으로 추정한 unit root process의 coefficient의 분포를 도출한다.
This video derives the distribution of the OLS estimate tracking coefficient of unit root process. Wald statistic, which tracks the same coefficient, and its distribution are also presented.
Click the following link to get the lecture note presented in the video.
drive.google.com/file/d/194-LhjdoB_MWzdIvKihPup7Og9MdOYNg/view?usp=drive_link
★Reference: Hamilton, James Douglas. Time series analysis. Princeton university press, 2020.
Просмотров: 31

Видео

17. 5. Basic stochastic calculus used for writing the sum of unit root process37:5717. 5. Basic stochastic calculus used for writing the sum of unit root process
17. 5. Basic stochastic calculus used for writing the sum of unit root process
Просмотров 61Год назад
17.5. Basic stochastic calculus used for writing the sum of unit root process Unit root process의 합은 Wiener process가 포함된 stochastic integral로 변환될 수 있으며, 그러한 integral은 특정한 분포를 따른다. 본 영상에서는 stochastic integral의 기초와 더불어 잘 알려진 stochastic integral의 분포들을 도출한다. This video derives the distributions of well-known forms of stochastic integrals. The distributions are used when sums of unit process are desc...
17.4. Limiting behavior of sums of unit root process Part 218:3517.4. Limiting behavior of sums of unit root process Part 2
17.4. Limiting behavior of sums of unit root process Part 2
Просмотров 23Год назад
17.4. Limiting behavior of the sums of unit root process (Part 2) 본 영상에서는 unit root process의 합이 따르는 분포들을 도출하고 도출된 결과들을 정리한 표를 제시한다. This video continues to derive stochastic integral stemming from the sums of unit root processes. A summary gathering the integrals is presented. Click the following link to get the lecture note presented in the video. drive.google.com/file/d/1d9BiswG2xHof6M3MlpOt2...
17.3. Limiting behavior of sums of unit root process Part 119:1017.3. Limiting behavior of sums of unit root process Part 1
17.3. Limiting behavior of sums of unit root process Part 1
Просмотров 18Год назад
17.3. Limiting behavior of the sums of unit root process (motivating stochastic integral) Unit root process를 따르는 데이터의 합은 특정 상수값으로 수렴하지 않고 random한 결과를 도출하지만, 그러한 데이터의 합은 특정한 분포를 따른다. 본 영상에서는 그러한 unit root process의 합이 따르는 분포를 도출한다. 한편, 그러한 데이터의 합은 stochastic integral의 형식으로 변환할 수 있고, 잘 알려진 stochastic integral의 결과물들은 분포의 형식으로 계산되므로, 결론적으로 unit root process의 데이터의 합이 따르는 분포를 도출할 수 있다. Sums of unit ro...
17.2. Introduction to Browian motion and functional CLT31:3417.2. Introduction to Browian motion and functional CLT
17.2. Introduction to Browian motion and functional CLT
Просмотров 73Год назад
17.2. Introduction to Brownian motion and functional CLT 본 동영상은 unit root process의 limiting behavior를 표현하는 도구로서 자주 활용되는 Brownian motion 과 Functional CLT의 개념을 설명한다. This video introduces the concept of Brownian motion and functional central limit theorem both of which are frequently used in writing the limiting behavior of the statistics constructed from the data drawn from unit root process. Cl...
17.1. Problems of using OLS estimate for unit root process19:5917.1. Problems of using OLS estimate for unit root process
17.1. Problems of using OLS estimate for unit root process
Просмотров 18Год назад
17.1. Problems of using OLS estimate for unit root process Stationary process에 대하여 도출된 OLS 방식의 parameter estimation technique을 unit root process에 적용하게 될 경우, OLS estimator는 특정 상수값으로 수렴하지 않게 되는 문제점을 구체적인 사례를 들어 설명한다. Conventional OLS technique developed for stationary process does not guarantee that OLS estimator derived for unit root process converges. This video illustrates why OLS approach bec...
16.5. Valid statistical test of trend stationary process13:5016.5. Valid statistical test of trend stationary process
16.5. Valid statistical test of trend stationary process
Просмотров 14Год назад
16.5. Valid statistical test of trend stationary process Stationary process의 parameter들을 추적하는 OLS estimate은 Normality 따르고, 그러한 OLS estimate을 기반으로 construct되는 Wald statistics은 Chi-square분포를 따르지만, 그러한 분포는 stationarity 가정을 위배하고 있는 trend-stationary process에 대해서는 원칙적으로 적용할 수 없다. 본 영상에서는 그러한 stationarity condition이 위배되는 trend-stationary process에 대해서도 동일한 Wald statistics를 활용한 Chi-square test가 유효함을 증명한...
16.4. distribution of OLS estimates for trend stationary process12:1016.4. distribution of OLS estimates for trend stationary process
16.4. distribution of OLS estimates for trend stationary process
Просмотров 312 года назад
16.4. Distribution of OLS estimates of trend stationary process 본 영상은 trend가 존재하는 프로세스에서 trend를 제거한 뒤, OLS 방식으로 추정한 OLS estimate의 분포를 도출한다. This video derives the distribution of the OLS estimates of the parameters of trend stationary process. Click the following link to get the lecture note presented during the video. drive.google.com/file/d/1Z4NGB8n7NeQrpCboFEKiO4x4VPbnhxrr/view?usp=sharing ★...
16 .3. Detrending trend stationary AR process15:1516 .3. Detrending trend stationary AR process
16 .3. Detrending trend stationary AR process
Просмотров 312 года назад
16 .3. Detrending trend stationary AR process 기존의 OLS방식의 parameter 추정 기법을 활용하기 위해서는, 적용대상이 되는 프로세스가 stationary해야하므로, trend-stationary process에 대하여는 기존 방식을 활용할 수 없다. 본 영상에서는 trend가 포함된 AR process에서 trend를 제거하여 기존의 OLS 방식의 parameter 추정이 가능하도록 하는 방법을 설명한다. In order to use conventional OLS estimation technique, the process subject to the technique should be stationary. This video shows how to de-tr...
16.2. Statistical test of the parameters of trend stationary process26:3016.2. Statistical test of the parameters of trend stationary process
16.2. Statistical test of the parameters of trend stationary process
Просмотров 172 года назад
16.2. Statistical test of OLS estimates of trend stationary process 직전 영상에서 Trend-stationary process의 parameter들을 OLS 방식으로 추정하는 기법에 대하여 설명하였다. 본 영상에서는 그렇게 도출된 OLS statistic을 통계적으로 검정하고자 할 경우, 올바른 statistic을 construct하는 방식을 설명하고 그러한 statistic이 t-distribution을 따름을 보인다. This video shows that the OLS estimates of the parameters of trend stationary process can be tested by t-test. Click the followin...
16.1. OLS estimation for trend stationary processes28:4916.1. OLS estimation for trend stationary processes
16.1. OLS estimation for trend stationary processes
Просмотров 272 года назад
16.1. Distribution of OLS estimates of trend stationary process 본 영상은 Trend-stationary process의 parameter들을 OLS 방식으로 추정할 경우 기존의 stationary process에서 사용하던 statistic을 변형하여 사용해야 하는 이유에 대해 설명하고, 그러한 변형된 statistic의 분포를 도출한다. OLS estimation of the parameters of trend stationary process should use the statistic different from that of conventional stationary process. This lecture explains why such modi...
15. 5 . Dickey Fuller test20:2515. 5 . Dickey Fuller test
15. 5 . Dickey Fuller test
Просмотров 432 года назад
15.5. Converting non-stationary process into stationary series and introduction of Dickey-Fuller test 본 강의는 trend-stationary process 및 unit root process 각각을 stationary process로 변환하는 방식을 살펴본 뒤, 주어진 시계열이 unit root process인지 검증하는 Dickey-Fuller test를 제시/설명한다.. This lecture explains how each of trend-stationary and unit root process can be converted into stationary process. Dickey-Fuller test evalua...
15.4. Forecast error of trend stationary process and unit root process16:1715.4. Forecast error of trend stationary process and unit root process
15.4. Forecast error of trend stationary process and unit root process
Просмотров 132 года назад
15.4. Forecast error of trend stationary process and unit root process 본 강의는 현재로부터 먼 미래에 관하여 추정할 경우, 해당 예측의 예측 오차는 trend-stationary process의 경우에는 일정한 상수값으로 수렴하지만 unit root process의 예측 오차는 더 먼 미래에 관하여 예측할수록 예측 오차가 무한대로 발산함을 보인다. Under certain conditions, forecast error of trend-stationary process converges while the error of unit root process diverges. Click the following link to get the lecture...
15.3. Forecast of unit root processs10:2115.3. Forecast of unit root processs
15.3. Forecast of unit root processs
Просмотров 232 года назад
15.3. Properties of the forecast of unit root process Trend를 내포하는 non-stationary 프로세스의 k-period 이후 미래 추정치는 (1) drift term이 k배 만큼 compounding되어 영향을 미치며, (2) 가장 최근 실제 관측치에 의해서 영향을 받는다. Non-stationary process with time-trend shows k-period ahead forecast which is affected by .drift term with compounding rate of k and is influenced by the most recent observation. Click the following link to get the...
15 2 Trend-stationary and unit root process and converting exponential trend into linear drift11:0115 2 Trend-stationary and unit root process and converting exponential trend into linear drift
15 2 Trend-stationary and unit root process and converting exponential trend into linear drift
Просмотров 393 года назад
15.2. Trend-stationary process, unit root process and conversion of exponential trend into linear drift 본 영상은 trend를 내포하는 non-stationary 프로세스를 표현하는 두가지 방식 (trend-stationary process and unit root process)을 살펴보고, 지수적으로 증가하는 변수를 내포한 프로세스를 stationary process로 변환하는 방식을 설명한다. This video presents two ways of writing non-stationary process; that is trend-stationary process and unit root process. The vi...
D.2. Sketchy proof of ln(x+1)=x6:49D.2. Sketchy proof of ln(x+1)=x
D.2. Sketchy proof of ln(x+1)=x
Просмотров 183 года назад
D.2. Sketchy proof of ln(x 1)=x
15.1. Condition for stationary process16:5515.1. Condition for stationary process
15.1. Condition for stationary process
Просмотров 313 года назад
15.1. Condition for stationary process
14.2. Example of estimators that fall on special case of GMM estimators17:5914.2. Example of estimators that fall on special case of GMM estimators
14.2. Example of estimators that fall on special case of GMM estimators
Просмотров 193 года назад
14.2. Example of estimators that fall on special case of GMM estimators
14.1. Generalized method of moments (GMM): Introduction22:2514.1. Generalized method of moments (GMM): Introduction
14.1. Generalized method of moments (GMM): Introduction
Просмотров 623 года назад
14.1. Generalized method of moments (GMM): Introduction
D.1. Asymptotic distribution of GMM estimators20:32D.1. Asymptotic distribution of GMM estimators
D.1. Asymptotic distribution of GMM estimators
Просмотров 603 года назад
D.1. Asymptotic distribution of GMM estimators
13.4. Updating time varying coefficients by using Kalman filter24:2613.4. Updating time varying coefficients by using Kalman filter
13.4. Updating time varying coefficients by using Kalman filter
Просмотров 723 года назад
13.4. Updating time varying coefficients by using Kalman filter
13.3. Estimating MLE parameters by using Kalman filter31:2813.3. Estimating MLE parameters by using Kalman filter
13.3. Estimating MLE parameters by using Kalman filter
Просмотров 943 года назад
13.3. Estimating MLE parameters by using Kalman filter
13.2. Example of application of Kalman filter to forecast time series (MA1)19:3813.2. Example of application of Kalman filter to forecast time series (MA1)
13.2. Example of application of Kalman filter to forecast time series (MA1)
Просмотров 433 года назад
13.2. Example of application of Kalman filter to forecast time series (MA1)
C.5. Update rule for sequential linear projections28:10C.5. Update rule for sequential linear projections
C.5. Update rule for sequential linear projections
Просмотров 143 года назад
C.5. Update rule for sequential linear projections
13.1. Application of Kalman filter to time series analysis (introduction and update rule)47:3213.1. Application of Kalman filter to time series analysis (introduction and update rule)
13.1. Application of Kalman filter to time series analysis (introduction and update rule)
Просмотров 1233 года назад
13.1. Application of Kalman filter to time series analysis (introduction and update rule)
C.4. Numerical example of Kalman filter33:58C.4. Numerical example of Kalman filter
C.4. Numerical example of Kalman filter
Просмотров 843 года назад
C.4. Numerical example of Kalman filter
C.3. Intuition of the Kalman filter (Kalman Gain)28:09C.3. Intuition of the Kalman filter (Kalman Gain)
C.3. Intuition of the Kalman filter (Kalman Gain)
Просмотров 923 года назад
C.3. Intuition of the Kalman filter (Kalman Gain)
12.4. Deriving posteriors by employing GLS regression17:3312.4. Deriving posteriors by employing GLS regression
12.4. Deriving posteriors by employing GLS regression
Просмотров 123 года назад
12.4. Deriving posteriors by employing GLS regression
12. 3. Bayesian updating rule for regression coefficients and unknown variance28:2312. 3. Bayesian updating rule for regression coefficients and unknown variance
12. 3. Bayesian updating rule for regression coefficients and unknown variance
Просмотров 133 года назад
12. 3. Bayesian updating rule for regression coefficients and unknown variance
12.2. Bayesian updating rule for regression coefficients with known variance25:0512.2. Bayesian updating rule for regression coefficients with known variance
12.2. Bayesian updating rule for regression coefficients with known variance
Просмотров 153 года назад
12.2. Bayesian updating rule for regression coefficients with known variance

Комментарии

  • @구르믈-y3f
    @구르믈-y3f Год назад

    좋은 강의 감사합니다!

  • @minjilee5744
    @minjilee5744 Год назад

    안녕하세요. 대학원 경제학과 박사과정 학생입니다. 올려주시는 영상들이 공부하는데 정말 많은 도움이 되고 있습니다. 어디에 감사인사 드릴지 몰라 댓글남깁니다. 감사합니다!

  • @lukisetiawanwu
    @lukisetiawanwu 2 года назад

    i need english subs

    • @simiancrease9335
      @simiancrease9335 2 года назад

      Dear audience. My apology for the inconvenience. I am working on the issue and will get back ASAP.

  • @구르믈-y3f
    @구르믈-y3f 2 года назад

    감사합니다 영상 잘 보고 있습니다

  • @fouedhamouda7356
    @fouedhamouda7356 2 года назад

    Could you please translate in english?

    • @simiancrease9335
      @simiancrease9335 2 года назад

      I am currently working on the translation issue and will get back to you ASAP.

    • @fouedhamouda7356
      @fouedhamouda7356 2 года назад

      @@simiancrease9335 Thank you very much

  • @saheresaedi4662
    @saheresaedi4662 3 года назад

    Thanks,it was useful 🙏🏻

  • @lucaslee7552
    @lucaslee7552 3 года назад

    감사합니다. 많은 도움 되었습니다~

  • @서명철-b8j
    @서명철-b8j 4 года назад

    좋은 강의 감사합니다 !

  • @토레타가좋다-e6k
    @토레타가좋다-e6k 4 года назад

    좋은 강의 감사합니다^^