Search results “What is time series analysis pdf”

QUANTITATIVE METHODS TIME SERIES ANALYSIS

Views: 178061
Adhir Hurjunlal

#Statistics #Time #Series #Business #Forecasting #Linear #Trend #Values #LeastSquares #Fitting #Odd
Definitions
“A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch
“A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers
Analysis of Time Series
“The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch
A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series.
Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following:
(i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’
(ii) Cyclical patterns.
(iii) Trends in the data.
(iv) Growth rates of these trends.
This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments.
Components of Time series –
The time series are classified into four basic types of variations which are analyzed below:
T = Trend
S = Seasonal variations
C = Cyclic variations
I = Irregular fluctuations.
This composite series is symbolized by the following general terms: O = T x S x C x I
Where
O = Original data
T = Trend
S = Seasonal variations
C = Cyclic variations
I = Irregular components.
This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I.
Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares)
Fitting of Linear Trend
Let the straight line trend between the given time series values (y) and time (x) be given by the standard equation: y = a + bx
Then for any given time ‘x’ the estimated value of ye as given by the equation is ye = a + bx
The following two normal equations are used for estimating 'a' and 'b'.
Σy = na + bΣx
Σxy = aΣx + bΣx^2
When Odd No. of Years, [X = (Year – Origin) / Interval]
Case
Given below are the figures of sales (in '000 units) of a certain shop. Fit a straight line by the method of least square and show the estimate for the year 2017:
Year: 2010 2011 2012 2013 2014 2015 2016
Sales: 125 128 133 135 140 141 143
Time Series, Linear Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12
- www.prashantpuaar.com

Views: 71500
Prashant Puaar

In this video you will learn how to detect AR & MA series by using ACF & PACF function plots . Detecting the order of AR, MA is important while building ARIMA model . It also is important when building variance forecasting models like Arch & Garch
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Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx

Views: 46320
Analytics University

Quantitative Techniques in Management:
Time Series Analysis - An Introduction; Video by Edupedia World (www.edupediaworld.com). All Rights Reserved.
Have a look at the other videos on this topic:
https://www.youtube.com/playlist?list=PLJumA3phskPH2vSufmMsrBUHbuoQY3G4R
Browse through other subjects in our playlist:
https://www.youtube.com/channel/UC6E97LDJTFJgzWU7G3CHILw/playlists?sort=dd&view=1

Views: 9870
Edupedia World

In this video you will learn what is a stationary series. It is an important property for AR, MA, ARIMA, Arch, Garch Models
For Training & Study packs on Analytics/Data Science/Big Data, Contact us at [email protected]
Find all free videos & study packs available with us here:
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Views: 31858
Analytics University

Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be
Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be
This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.

Views: 726060
Jalayer Academy

It covers in detail various methods of measuring trend like Moving Averags & Least Square. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce & Management

Views: 79165
Dr. B. R. Ambedkar Govt. College Kaithal

Time Series ARIMA Models Example
https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models

Views: 108187
econometricsacademy

http://www.subjectmoney.com
This Time Value of Money Lesson TVM covers all the basic concepts of the Time Value of Money that you would learn in Finance. In this tvm tutorial we cover simple interest, compound interest, present value formula, future value formula, annuity due, ordinary annuity, present value of annuities, future value of an annuity, intrayear compounding interest, and perpetuities. In this time value of money lesson we teach you by video using visualizations to help you understand how money and time works. If you study this finance tvm video tutorial in combination with what you leanr about the time value of money in your finance class, you should have a clear understanding when it is time to take your time value of money tvm test or exam. I’m glad that I could help you study for your finance time value of money exam.
What is simple interest?
What is compound interest?
What is an ordinary annuity?
What is an annuity due?
What is the present value formula?
What is the future value formula?
How to solve the present value of an uneven series of cash flows.
What is a perpetuity?
How to solve the present value of an ordinary annuity.
How to solve the present value of an annuity due.
How to solve the future value of an annuity due.
How to solve the future value of an ordinary annuity.
Present value of a perpetuity formula.
Time value of money, time value of money lesson, tvm, tvm lesson, tvm formulas, time value of money formulas, present value formula, future value formula, present value, future value, annuity due, ordinary annuity, simple interest, compounding interest, intrayear compounding interest, perpetuity, present value of a perpetuity, how to present value, what is present value, what is time value of money

Views: 163883
Subjectmoney

Stochastic Hydrology by Prof. P. P. Mujumdar, Department of Civil Engineering, IISc Bangalore For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 18177
nptelhrd

Tutorial materials for the Time Series Analysis tutorial including notebooks may be found here: https://github.com/AileenNielsen/TimeSeriesAnalysisWithPython
See the complete SciPy 2016 Conference talk & tutorial playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6.

Views: 53974
Enthought

Watch this brief (10 minutes or so!!) video tutorial on how to do all the calculations required for a Time Series analysis of data on Microsoft Excel.
Try and do your best to put up with the pommie accent. The data for this video can be accessed at https://sites.google.com/a/obhs.school.nz/level-3-statistics-and-modelling/time-series

Views: 101889
mrmathshoops

#Statistics #Time #Series #Business #Forecasting #Linear #Trend #Values #LeastSquares #Fitting #Even #Period
Definitions
“A time series may be defined as a sequence of values of same variable corresponding to successive points in time.” – W. Z. Hersch
“A time series may be defined as a sequence of repeated measurement of a variable made periodically through time.” – Cecil H. Mayers
Analysis of Time Series
“The main object of analyzing time series is to understand, interpret and evaluate changes in economic phenomena in the hope of more correctly anticipating the course of future events.” – Hersch
A time series is a dynamic distribution, which reveals a good deal of variations over time. Statistical methods are, therefore, required to analyze various types of movements in a time series. There may be cyclical variations in general business activity and there may be short duration seasonal variations. There are also some accidental and random variables. The primary purpose of the analysis of time series is to discover and measure all such types of variations, which characterize a time series.
Time series analysis means analyzing the historical patterns of the variable that have occurred in past as a means of predicting the future value of the variable. It helps to identify and explain the following:
(i) Any regular or systematic variation in the series of data which is due to seasonality- the ‘seasonal’
(ii) Cyclical patterns.
(iii) Trends in the data.
(iv) Growth rates of these trends.
This method can be useful when no major environmental changes are expected and it does highlight seasonal variations in sales and consumer demand. However, time series analysis is limited when organizations face volatile environments.
Components of Time series –
The time series are classified into four basic types of variations which are analyzed below:
T = Trend
S = Seasonal variations
C = Cyclic variations
I = Irregular fluctuations.
This composite series is symbolized by the following general terms: O = T x S x C x I
Where
O = Original data
T = Trend
S = Seasonal variations
C = Cyclic variations
I = Irregular components.
This Multiplicative model is to be used when S, C, and I are given in percentages. If, however, their true (absolute) values are known the model takes the additive form i.e., O=T+C+S+I.
Algebraic Method For Finding Trend (Method of curve fitting by the principle of Least Squares)
Fitting of Linear Trend
Let the straight line trend between the given time series values (y) and time (x) be given by the standard equation: y = a + bx
Then for any given time ‘x’ the estimated value of ye as given by the equation is ye = a + bx
The following two normal equations are used for estimating 'a' and 'b'.
Σy = na + bΣx
Σxy = aΣx + bΣx^2
Even No. of Years
If a n is even, the transformation is x = YEAR - (arithmetic mean of two middle years) / Half Interval
NOTE
It is not compulsory to divide the numerator by "Half Interval". There are two types of authors, suggesting for such kind of change of scale and not suggesting. I have discussed this point of change of scale in some of lectures because in India and other countries of Indian subcontinent and Asia, in many reference books, and in the books published by the boards of examinations, the authors have suggested this kind of change of scale.
Case
Fit a straight line equation and obtain trend value:
Year 2009 2010 2011 2012 2013 2014 2015 2016
Y (value) 80 90 92 83 94 99 92 104
Time Series, Linear Trend, Method of Least Squares, Statistics, MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering , Grade-11, Grade- 12
- www.prashantpuaar.com

Views: 38424
Prashant Puaar

Operations and Supply Chain Management by Prof. G. Srinivasan , Department of Management Studies, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in

Views: 183771
nptelhrd

The differencing operator helps remove trend and seasonal patterns.
This video supports the textbook Practical Time Series Forecasting.
http://www.forecastingbook.com
http://www.galitshmueli.com

Views: 3966
Galit Shmueli

In this video you will learn what is Auto correlation function and what is it used for in time series analysis
For Analytics Study Pack visit : http://analyticuniversity.com/
For training, mentorship contact us at [email protected]
Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity
Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx

Views: 30334
Analytics University

Time Series ARIMA Models
https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models

Views: 235083
econometricsacademy

James S Walker, Mark W Jones, Robert S Laramee, Owen R Bidder, Hannah J Williams, Rebecca Scott, Emily LC Shepard and Rory P Wilson, TimeClassifier-A Visual Analytic System for the Classification of Time-Series Data, The Visual Computer, Volume 31, No 6-8, pages 1067-1078, June 2015
http://dx.doi.org/10.1007/s00371-015-1112-0
PDF
http://cs.swan.ac.uk/~csbob/research/biologySensor/classification/walker15timeClassifier.pdf
Abstract:
Biologists studying animals in their natural environment
are increasingly using sensors such as accelerometers
in animal-attached ‘smart’ tags because it is widely acknowledged
that this approach can enhance the understanding
of ecological and behavioural processes. The potential
of such tags is tempered by the difficulty of extracting animal
behaviour from the sensors which is currently primarily
dependent on the manual inspection of multiple time-series
graphs. This is time-consuming and error-prone for the domain
expert and is now the limiting factor for realising the
value of tags in this area. We introduce TimeClassifier, a visual
analytic system for the classification of time-series data
for movement ecologists. We deploy our system with biologists
and report two real-world case studies of its use.

Views: 129
DataVisBob Laramee

Video Lecture Series by IIT professors (Not Available in NPTEL)
Video Lectures on "Signals and Systems" by Prof. S.C. Dutta Roy Sir
For more Video Lectures...... www.satishkashyap.com
For free ebooks ...... www.ebook29.blogspot.com
1. Introduction to the Course and Basic Concepts
2. Signals & their Transportation
3. Elementary Signals in the Discrete Time Domain
4. Characterisation of Signals
5. Basic concepts of Linear Time Systems
6. Convolution Invertibility, & Stability Causality
7. Stability Unit, Step Response and Differential Equations
8. Systems Described by Differential & Difference Equations
9. Fourier & His Series
10. More About Fourier Series (With Uncomfortable Questions)
11. Those Uncomfortable Questions about the Existence of Fourier & Series and Some More
12. Introduction to Fourier Transform
13. Fourier Transform of Periodic Function & Fourier Transform Properties
14. More Properties of Fourier Transformation
15. Anatomy of a Class Test & a Continued Look at the Properties of F.T.
16. Modulation, Convolutions and Other Interesting Properties of F.T.
17. A Deeper Look at the Modulation Property of F.T.
18. Fourier Analysis of Discrete Time Signals & Systems - The Beginning
19. More About Fourier Transform of Discrete Time Signals
20. Further Look into the Properties of DTFT
21. Convolution, Modulation & Other Properties of DTFT
22. Farewell to Discrete Time Fourier Transform & Introduction to Sampling
23. More About Sampling
24. Introduction to Laplace Transform
25. Region of Convergence of Laplace Transform & Properties of Laplace Transform
26. Properties of Laplace Transform (Contd.)
27. Concluding Discission on Laplace Transform
28. Introduction to Z Transform
29. Properties of Z Transform
30. Further Discussion on Properties of Z Transform
31. Solution to Class Test - 2, Concluding Discussion on Z Transform
32. Introduction to Random Signals & Probability
33. Probability Functions
34. Solutions to Minor Z Problems & more about PDF & pdf
35. More About PDF'S & pdf's
36. Classification of Random Processes & Introduction to Correlation Functions.
37. More About Correction Functions
38. Cross Correlation Function and their Properties
39. Introduction to Spectral Density
40. More About Spectral Density
41. Response of Linear System to Random Inputs
42. Frequency Domain Analysis of LTI Systems Excited by Random Inputs

Views: 67277
Satish Kashyap

SPSS training on Conjoint Analysis by Vamsidhar Ambatipudi

Views: 24518
Vamsidhar Ambatipudi

This shows an example of real-time time series anomaly discovery with rule density curve built using sliding window-based SAX discretization and grammatical inference with Sequitur. Our paper describing the approach: http://csdl.ics.hawaii.edu/techreports/2014/14-05/14-05.pdf (SAX parameters used: window 400, PAA size 8, Alphabet size 6)

Views: 4054
seninp

See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1
Download a trial: https://goo.gl/PSa78r
A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis.
About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.

Views: 35853
MATLAB

To download the TSAF GUI, please click here:
http://www.mathworks.com/matlabcentral/fileexchange/54276-time-series-analysis-and-forecast
Please check out www.sphackswithiman.com for more tutorials.

Views: 8207
iman

Time series graphs nayland maths college. Time series interactive mathematics software or math. Often, we draw graphs of time series data as line that can be used to make predictions and conclusions forest timber production. Twoway time series line plotcommands to reproduce, pdf doc entries. What is a time series plot? Minitab support. Everything from downloading a data set the internet to home resources & support faqs stata graphs time series plots. Choose a time series plot minitab express. This type of graph shows how a variable's value changes over 12 jan 2018 time series chart, also called times or plot, is data visualization tool that illustrates points at successive (often plot) graphical representation (data where we record the specific date each collected period. What is time series chart? Definition from whatis. In plain english, a time series is simply sequence of numbers 28 apr 2015 many graphs use series, meaning they measure events over. Minitab graphs time series graph choose a plot class "imx0m" url? Q webcache. This can be time in hours, days, months, weeks, 19 jul 2011is plotted onto a graph, this will series as it shows the from seen that there is large fluctuation each year amount choose plot minitab express support. Time series definition, examples & analysis
timeplot time analysis statisticshowto "imx0m" url? Q webcache. Creating a time series graph youtubebbc bitesize gcse maths representing data aqa revision 8. Time series definition, examples & analysis. Timeplot time series definition, examples & analysis. Reading and interpreting time series graphs mathspace. What is the time series graph? Quora. 24 sep 2013 a timeplot (sometimes called a time series graph) displays values against time. Time series graphs & eleven stunning ways you can use them. Time series graphs construction, use and examples thoughtco. For most of the work you do in this book, will use a histogram to display data. Question aim we to predict the _____(context)_____ for years of _____(time interval) a tutorial on how make time series graph in plotly 2. Time series graphs in plotly 2. Most commonly, a time series is sequence taken at successive equally spaced points in. Histograms, frequency polygons, and time series graphs investopedia. Timeplots are good for showing how data changes over time 17 nov 2017 a series graph displays paired in which the first coordinate is. William playfair (1759 1823) was a scottish economist and pioneer of time series data refers to bivariate where the explanatory variable, or independent is. How to graph and label time series data in excel twoway line plot stata. One advantage of a histogram is that it can readily display sequence numerical data points in successive order, usually occurring uniform intervals. They are similar to x y graphs, but while an graph can plot a variety of variables (for example, height, weight, age), timeplots only display time on the axis. To create a time series plot, choose one of the fol

Views: 9
E Answers

In this video I explain what is meant by an ARMA(1,1) process, and provide a couple of examples of processes which could be modelled as thus.
Check out http://www.oxbridge-tutor.co.uk/undergraduate-econometrics-course for course materials, and information regarding updates on each of the courses. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti

Views: 79950
Ben Lambert

In this clip I demonstrate how to use EVIEWS for Forecasting

Views: 100021
Ralf Becker

This video demonstrates how to use the “Create Times Series” dialog in SPSS. Functions such as difference, cumulative sum, lag, and lead are reviewed.

Views: 24557
Todd Grande

Learn how to fit ARMA/ARIMA models in Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.

Views: 97789
StataCorp LLC

- AR, MA and ARMA models
- Parameter estimation for ARMA models
- Hidden Markov Models (definitions, inference, learning)
- Linear-Gaussian HMMs (Kalman filtering)
- More advanced topics (more elaborate state-space models, and recurrent neural networks)

Views: 4735
The Alan Turing Institute

It covers in detail the different methods of measurement of Seasonal Variations like Simple Average Method, Ratio to Moving Average Method, Ratio to Trend Method and Link Relative Method. Lecture by: Rajinder Kumar Arora, Head of Department of Commerce and Management

Views: 28121
Dr. B. R. Ambedkar Govt. College Kaithal

Time Series ARIMA Models in Stata
https://sites.google.com/site/econometricsacademy/econometrics-models/time-series-arima-models

Views: 42383
econometricsacademy

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This is not medical advice. The content is intended as educational content for health care professionals and students. If you are a patient, seek care of a health care professional. In this video, Andreas talks about the basics of the gait cycle and what to look for in a basic physiotherapeutic gait analysis
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Views: 239109
Physiotutors

In this video we describe the DTW algorithm, which is used to measure the distance between two time series. It was originally proposed in 1978 by Sakoe and Chiba for speech recognition, and it has been used up to today for time series analysis. DTW is one of the most used measure of the similarity between two time series, and computes the optimal global alignment between two time series, exploiting temporal distortions between them.
Source code of graphs available at
https://github.com/tkorting/youtube/blob/master/how-dtw-works.m
The presentation was created using as references the following scientific papers:
1. Sakoe, H., Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustic Speech and Signal Processing, v26, pp. 43-49.
2. Souza, C.F.S., Pantoja, C.E.P, Souza, F.C.M. Verificação de assinaturas offline utilizando Dynamic Time Warping. Proceedings of IX Brazilian Congress on Neural Networks, v1, pp. 25-28. 2009.
3. Mueen, A., Keogh. E. Extracting Optimal Performance from Dynamic Time Warping. available at: http://www.cs.unm.edu/~mueen/DTW.pdf

Views: 22468
Thales Sehn Körting

This video tutorial demonstrates how to construct a cumulative distribution plot using measured data in Excel 2007. The next video in the series shows how to add a normal distribution approximation to the plot for comparison, and how to calculate probabilities from these two. The third video in this series demonstrates how to solve a real-world problem using this technique.

Views: 209085
Dr. Cyders

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2CoOaaO].
This video provides an overview of the entire course.
For the latest Big Data and Business Intelligence tutorials, please visit
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 124
Packt Video

Webinar with Jyotirmoy Patra on "Time Series Forecasting and Its Application in Technology Sector". In the present business world forecasting is an arm of analytics which can provide a powerful toolkit for analyzing time series data and make better decisions.
About the Speaker
Jyotirmoy Patra is working as a Data Scientist at IBM with more than 8 years of extensive experience in the field of analytics and research.
He has provided solutions in a diverse set of domains like Marketing - CPG & Healthcare, Resource Planning , Manufacturing. His recent work has been in the field of Demand planning using forecasting tools, Time Series Clustering and Ignite defect prediction.

Views: 237
Great Learning

This is an introduction to econometrics tutorial. This video is a basic overview and touches on each of these subjects:
1. What is Econometrics?
2. Goals of Econometrics
3. Types of Economic Data
4. The "Simple Linear Regression" (SLR)
5. Causality
This lecture on econometric theory is meant to introduce the student to the concepts of econometrics, as well as provide a basic overview of what the topic of econometrics encompasses.
The next video tutorial on simple linear regressions: http://youtu.be/CBa8frhRKMw
Follow us on Twitter @ https://twitter.com/KeynesAcademy
All video, images, commentary and music is owned by Keynes Academy.

Views: 283623
KeynesAcademy

In this episode of DataChats Lore talks with David Stoffer. Interested in learning more? Start David's ARIMA Modeling with R course today: https://www.datacamp.com/courses/arima-modeling-with-r
David Stoffer is a Professor of Statistics at the University of Pittsburgh. He is member of the editorial board of the Journal of Time Series Analysis and Journal of Forecasting. David is the coauthor of the book "Time Series Analysis and Its Applications: With R Examples", which is the basis of his course. Another (free) book he wrote on Time Series Analysis is available here: http://www.stat.pitt.edu/stoffer/tsa4/tsaEZ.pdf
Together with Lore, David talks about his path to Statistics, his teaching method, his latest book, how he got into R, and much more.

Views: 759
DataCamp

By Peter Richards and Andrew Straw
Business Analysis Tools - University of Hertfordshire
References:
Alquist, R. Kilian, L. Vigfusson, R. (2011) Forecasting the Price of Oil. Available at: http://www.federalreserve.gov/pubs/ifdp/2011/1022/ifdp1022.pdf [Accessed: 26th November, 2013]
Chambers, J. Mullick, S. & Smith, D. (1971) How to choose the right forecasting technique. Available at: http://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique/ar/1 [Accessed: 24th November, 2013]
Cimcil (2013) Partial List of Trained Companies. Available at: http://www.cimcil.be/assets/ibf/partial%20list%20of%20trained%20companies.png [Accessed: 26th November, 2013]
GFVGA (2013) Forecasting Agricultural Water Demand. Available at: http://gfvga.org/wp-content/uploads/2010/03/3.-Forecasting-Georgias-Irrigation-Water-Needs.pdf [Accessed: 24th November, 2013]
Hanna, M. Render, B. & Stair, R (2009) Quantitative Analysis for Management. 10th Edn. London. Prentice Hall.
IBM (2013) Coca Cola Bottling Co. Consolidated Maximizes Profitability. Available at: http://public.dhe.ibm.com/common/ssi/ecm/en/tsc03243usen/TSC03243USEN.PDF [Accessed: 23rd November, 2013]
Institute of Business Forecasting & Planning (2005) Why Forecasting? Available at: http://ibf.org/index.cfm?fuseaction=showObjects&objectTypeID=87 [Accessed: 21st November, 2013]

Views: 500
Peter Richards

In 2004, a one-term senator from Illinois took the stage to deliver the keynote speech at the Democratic National Convention in Boston. By the time Barack Obama had finished speaking, Democrats across the country knew they had seen the future of their party.
Political speech experts featured in this episode include:
Michael A. Cohen
Author, Live From The Campaign Trail
Mario Cuomo
Former Governor of New York
Robert Lehrman
Chief Speechwriter for Vice President Gore and Professor of Speechwriting, American University
Charlton McIlwain
Professor of Communication, New York University
Jeff Shesol
Speechwriter for President Clinton and Founding Partner, West Wing Writers
PODIUM is a bi-weekly series that embraces the art of public speaking and honors those with something to say. From historic political speeches, to contemporary commencement addresses, to wedding toasts, the series explores various genres of speechmaking and provides inspiring, insightful analysis including "how-to" content.
Created and produced by @radical.media, THNKR gives you extraordinary access to the people, stories, places and thinking that will change your mind.
Follow @THNKR on Twitter for the latest!
Like us on Facebook: http://www.facebook.com/thnkrtv
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Views: 12930049
THNKR

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Wisecrack

spreg estimates the parameters of a cross-sectional spatial
autoregressive model with spatial autoregressive disturbances, which is known as a SARAR model.
A SARAR model includes a weighted average of the dependent variable, known as a spatial lag, as a right-hand-side variable, and it allows the disturbance term to depend on a weighted average of the disturbances corresponding to other units. The weights may differ for each observation and are frequently inversely related to the distance from the current observation.
spreg estimates the parameters by either maximum likelihood (ML) or by generalized spatial two-stage least squares (GS2SLS).
For more information about spatial autoregressive models in Stata, see http://www.stata.com/new-in-stata/spatial-autoregressive-models/. Copyright 2017 StataCorp LLC. All rights reserved.

Views: 3661
StataCorp LLC

This video runs through an example script on how to estimate panel data models in R using plm(). By appeal to lm() and lmer(), I show that plm() estimates what we think it should estimate. I also show how to cluster standard errors in R. Here are some resources to which I refer in the video:
I posted these scripts on my econometrics website. metrics.tonycookson.com. They are called clusterFunctions.R and exampleofclusteringinR.R, and they are available for download there.
Mahmoud Arai's Clustering Functions. http://people.su.se/~ma/clustering.pdf
Mitchell Peterson's Test Data (kept referring to him as Thompson in the video, sorry!). http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.htm
And some background reading on this.
Peterson 2009 can be found here in earlier WP version. http://ideas.repec.org/p/nbr/nberwo/11280.html
Thompson 2011 can be found here. http://schwert.ssb.rochester.edu/f532/JFE11_ST.pdf
To address the question about ML/REML. Here is a useful set of slides on how to specify the likelihood. http://www.stat.wisc.edu/~ane/st572/notes/lec21.pdf As I remember it, REML focuses on estimating the error components independently of the systematic part of the regression model. On an intuitive level, this amounts to building in a degrees of freedom correction. There is much more out there on a Google search of "REML vs. ML in random effects"

Views: 8471
intromediateecon

© 2018 Summary of the newspaper articles

The Key Elements of Portfolio Management. Portfolio Management Tips for Young Investors. One of the reasons most often given for not investing is a lack of knowledge and understanding of the stock market. This objection can be overcome through self-education and step-by-step through the years because investors learn by investing. Classes in investing are also offered by a variety of sources, including city and state colleges, civic groups, and not-for-profit organizations, and there are numerous books aimed at the beginning investor. Start Early. Early Higher Risk Allocation. An Exemplary Egg. The idea is to select stocks across a broad spectrum of market categories. This is best achieved through an index fund. Aim to invest in conservative stocks with regular dividends, stocks with long-term growth potential, and a small percentage of stocks with better returns or higher risk potential. Certain AAA-rated bonds are also good investments for the long term, either corporate or government. Long-term U.S. Treasury bonds, for example, are safe and pay a higher rate of return than short- and mid-term bonds. Keep Costs to a Minimum.