GRETL. (Gnu Regression, Econometrics and. Timeseries Library)


 Dennis Horton
 2 years ago
 Views:
Transcription
1 1 GRETL (Gnu Regression, Econometrics and Timeseries Library)
2 2 In this project you should analyze generated and real data. Analysis of each set of data should contain: a) Descriptive statistics. b) Time series plot. c) Checking of normality. d) If data are nonstationary take, for example logdifferences to assure stationarity. e) Descriptive statistics, time series plot, checking of normality, analysis of stationarity of new data. f) Analysis of correlogram, finding AR and MA processes order. g) Estimating ARMA processes (in gretl) h) Compare estimated models using information criterions. i) Choosing the best ARMA model. j) Estimating ARMAGARCH processes (in Ox) k) Compare estimated models using information criterions. l) Choosing the best ARMAGARCH model.
3 3 Projekty oddajemy w wersji papierowej. Kazdy projekt bedzie "broniony" indywidualnie. W projekcie prosze zamiescic kolejne kroki dochodzenia do ostatecznego modelu (co obserwujemy, jakie modele beda rozpatrywane w zwiazki z tym, jakie sa kryteria wyboru optymalnego modelu itd, warto porobic troche rysunkow) Which financial time series features do you observe? Which class of models do you chose and why? What are the probably orders of the models? Which model is the best for given data? Is it really the best existing model? wygenerowane dane pochodza z modeli poznanych na wykladzie ARMA + szeroka klasa modeli GARCH z roznymi efektami + rozne rozklady warunkowe rzedy modeli sa zdroworozsadkowe czyli zawiaraja sie w ARMA(2,2), GARCH(2,1) proponowane narzedzia do analizy to GRETL oraz OX z pakietem
4 4 1. How to get and install gretl a) Go to page or and download gretl (Section Gretl for Windows, gretl exe)
5 5 b) Install gretl with default parameters After that, gretl will be installed, but usually in Polish language, to run gretl in English language you have to click: Narzędzia > Ustawienia > Ogólne or Tools > Preferences > General Choose Wybór języka dla GUI > English
6 Choose Wybór języka dla GUI > English or Language Preserence > Polish 6
7 7 2. To load data to gretl from ASCI (text) file, you have to choose from menu: File > Open data > Import > Text/CSV When gretl loads chosen file it will open window with question about structure of data. Answer Yes a) Choose Time series, then click Forward b) Choose Daily (5 days), then click Forward c) Type 1970/01/01 as a starting date, then click Forward (any other date will do) d) Click Apply (if everything is OK).
8 8
9 9
10 right mouse button 10
11 11 3. To load data to gretl from Excel file: File > Open data > Import > Excell Gretl will open first window, click Yes. Then it will open next window with the same question like previous, so you have to choose the same steps.
12 5. With loaded and set data you can: a) Get a time series plot: click with right mouse button second variable name (first is a constant added by gretl) and choose Time series plot b) Get a descriptive statistics: click with right mouse button and choose Descriptive statistics c) Get a correlogram: click with right mouse button and choose Correlogram (you have to choose a proper lag, in most cases the default lag will be good) After choosing lag two windows will open, first with graph of autocorrelation and partial autocorrelation, second with coefficient of autocorrelation and partial autocorrelation functions (with significance of each coefficient). 12
13 13 6. Transformations of variables: a) returns: Add > Define new variable In opened window type: new_variable = (x x(1))/x(1) where x name of variable b) logarithmic returns: Add > Log differences of selected variables
14 14
15 15 8. Checking of normality: Variable > Frequency distribution Variable > Frequency distribution> Against Normal QQ plot for rates 6 y = x Normal quantiles
16 16 The lower the pvalue, the less likely the result is if the null hypothesis is true, and consequently the more "significant" the result is, in the sense of statistical significance. One often rejects the null hypothesis when the pvalue is less than 0.05 or 0.01, corresponding respectively to a 5% or 1% chance of rejecting the null hypothesis when it is true (Type I error).
17 17 9. Checking AR and MA processes order: Variable > Correlogram other data than APATOR!! ACF for Data /T^ lag PACF for Data /T^ lag
18 Estimating ARMA processes: a) Model > Time series > ARIMA b) Choose dependent variable.
19 H0: parameter insignificant pvalue<0.05 reject H0 19
20 You can save the residuals of model by choosing Save>Residuals in the window with models characteristics
21 21 ACF for uhat /T^ lag PACF for uhat /T^ lag
22 TEST of ARCH effect in residuals 22
23 GARCH models 23
24 GARCH model 24
25 Example data_gretl.xls 25
26 Density 26 Example data_gretl.xls 30 Test statistic for normality: Chisquared(2) = pvalue = Data N( , ) Data
27 27 Example data_gretl.xls ACF for Data /T^ lag PACF for Data /T^ lag
28 28
29 29
30 30 Residual ACF /T^ lag Residual PACF /T^ lag
31 31
32 32 check the normality of ARMA residuals do the ARCH test
33 33 Test for normality of residual  Null hypothesis: error is normally distributed Test statistic: Chisquare(2) = with pvalue = 1.88e005
34 34
35 35 Test for ARCH of order 5  Null hypothesis: no ARCH effect is present Test statistic: LM = with pvalue = P(Chisquare(5) > ) = e018 Test for ARCH of order 5 coefficient std. error tratio pvalue alpha(0) e e012 *** alpha(1) e08 *** alpha(2) * alpha(3) alpha(4) *** alpha(5) ***
36 36
37 Density standardized residuals Test statistic for normality: Chisquared(2) = pvalue = uhat6 N( ,1.0021) uhat6 37
38 squared residuals of ARMA model 38
39 39 squared standardized residuals ACF for usq /T^ lag PACF for usq /T^ lag
40 Density 40 if it is not Gaussian distribution we need tstudent distribution or skewed tst leverage effect OX Test statistic for normality: Chisquared(2) = pvalue = uhat6 N( ,1.0021) uhat6
Time Series Graphs. Model ACF PACF. White Noise All zeros All zeros. AR(p) Exponential Decay P significant lags before dropping to zero
Time Series Graphs Model ACF PACF White Noise All zeros All zeros AR(p) Exponential Decay P significant lags before dropping to zero MA(q) q significant lags before dropping to zero Exponential Decay ARMA(p,q)
More informationBooth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm
Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has
More informationPromotional Forecast Demonstration
Exhibit 2: Promotional Forecast Demonstration Consider the problem of forecasting for a proposed promotion that will start in December 1997 and continues beyond the forecast horizon. Assume that the promotion
More informationAnalysis of Financial Time Series with EViews
Analysis of Financial Time Series with EViews Enrico Foscolo Contents 1 Asset Returns 2 1.1 Empirical Properties of Returns................. 2 2 Heteroskedasticity and Autocorrelation 4 2.1 Testing for
More informationChapter 12: Time Series Models
Chapter 12: Time Series Models In this chapter: 1. Estimating ad hoc distributed lag & Koyck distributed lag models (UE 12.1.3) 2. Testing for serial correlation in Koyck distributed lag models (UE 12.2.2)
More informationKSTAT MINIMANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINIMANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
More informationTime Series Analysis
Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina GarcíaMartos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and GarcíaMartos
More informationTime Series  ARIMA Models. Instructor: G. William Schwert
APS 425 Fall 25 Time Series : ARIMA Models Instructor: G. William Schwert 585275247 schwert@schwert.ssb.rochester.edu Topics Typical time series plot Pattern recognition in auto and partial autocorrelations
More informationModeling and Forecasting of Gold Prices on Financial Markets
Modeling and Forecasting of Gold Prices on Financial Markets Rebecca Davis Department of Mathematical Sciences Pentecost University College AccraGhana. Vincent Kofi Dedu Department of Mathematics Kwame
More informationSoftware Review: ITSM 2000 Professional Version 6.0.
Lee, J. & Strazicich, M.C. (2002). Software Review: ITSM 2000 Professional Version 6.0. International Journal of Forecasting, 18(3): 455459 (June 2002). Published by Elsevier (ISSN: 01692070). http://0
More informationThe SAS Time Series Forecasting System
The SAS Time Series Forecasting System An Overview for Public Health Researchers Charles DiMaggio, PhD College of Physicians and Surgeons Departments of Anesthesiology and Epidemiology Columbia University
More informationVector Time Series Model Representations and Analysis with XploRe
01 Vector Time Series Model Representations and Analysis with plore Julius Mungo CASE  Center for Applied Statistics and Economics HumboldtUniversität zu Berlin mungo@wiwi.huberlin.de plore MulTi Motivation
More informationRegression analysis in practice with GRETL
Regression analysis in practice with GRETL Prerequisites You will need the GNU econometrics software GRETL installed on your computer (http://gretl.sourceforge.net/), together with the sample files that
More informationAnalysis of the Volatility of the Electricity Price in Kenya Using Autoregressive Integrated Moving Average Model
Science Journal of Applied Mathematics and Statistics 2015; 3(2): 4757 Published online March 28, 2015 (http://www.sciencepublishinggroup.com/j/sjams) doi: 10.11648/j.sjams.20150302.14 ISSN: 23769491
More informationSales forecasting # 2
Sales forecasting # 2 Arthur Charpentier arthur.charpentier@univrennes1.fr 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting
More informationExam Solutions. X t = µ + βt + A t,
Exam Solutions Please put your answers on these pages. Write very carefully and legibly. HIT Shenzhen Graduate School James E. Gentle, 2015 1. 3 points. There was a transcription error on the registrar
More informationEviews Tutorial. File New Workfile. Start observation End observation Annual
APS 425 Professor G. William Schwert Advanced Managerial Data Analysis CS3110L, 5852752470 Fax: 5854615475 email: schwert@schwert.ssb.rochester.edu Eviews Tutorial 1. Creating a Workfile: First you
More informationITSMR Reference Manual
ITSMR Reference Manual George Weigt June 5, 2015 1 Contents 1 Introduction 3 1.1 Time series analysis in a nutshell............................... 3 1.2 White Noise Variance.....................................
More informationAdvanced Forecasting Techniques and Models: ARIMA
Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: admin@realoptionsvaluation.com
More informationJOHANNES TSHEPISO TSOKU NONOFO PHOKONTSI DANIEL METSILENG FORECASTING SOUTH AFRICAN GOLD SALES: THE BOXJENKINS METHODOLOGY
DOI: 0.20472/IAC.205.08.3 JOHANNES TSHEPISO TSOKU North West University, South Africa NONOFO PHOKONTSI North West University, South Africa DANIEL METSILENG Department of Health, South Africa FORECASTING
More informationTIME SERIES ANALYSIS
TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi0 02 lmb@iasri.res.in. Introduction Time series (TS) data refers to observations
More informationEXPORT PRICE VOLATILITY OF REFINED PETROLEUM PRODUCTS FROM A SMALL HYDROCARBON BASED ECONOMY. Roger Hosein, Don Charles, Martin Franklin
1 EXPORT PRICE VOLATILITY OF REFINED PETROLEUM PRODUCTS FROM A SMALL HYDROCARBON BASED ECONOMY Roger Hosein, Don Charles, Martin Franklin EXPORT PRICE VOLATILITY OF REFINED PETROLEUM PRODUCTS FROM A SMALL
More informationAdvanced timeseries analysis
UCL DEPARTMENT OF SECURITY AND CRIME SCIENCE Advanced timeseries analysis Lisa Tompson Research Associate UCL Jill Dando Institute of Crime Science l.tompson@ucl.ac.uk Overview Fundamental principles
More informationDurbinWatson Significance Tables
DurbinWatson Significance Tables Appendix A The DurbinWatson test statistic tests the null hypothesis that the residuals from an ordinary leastsquares regression are not autocorrelated against the alternative
More informationGraphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models
Graphical Tools for Exploring and Analyzing Data From ARIMA Time Series Models William Q. Meeker Department of Statistics Iowa State University Ames, IA 50011 January 13, 2001 Abstract Splus is a highly
More informationData analysis and regression in Stata
Data analysis and regression in Stata This handout shows how the weekly beer sales series might be analyzed with Stata (the software package now used for teaching stats at Kellogg), for purposes of comparing
More informationPerform hypothesis testing
Multivariate hypothesis tests for fixed effects Testing homogeneity of level1 variances In the following sections, we use the model displayed in the figure below to illustrate the hypothesis tests. Partial
More informationTime Series Analysis: Basic Forecasting.
Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rssmatters Jon Starkweather, PhD 1 Jon Starkweather, PhD jonathan.starkweather@unt.edu
More informationA Multiplicative Seasonal BoxJenkins Model to Nigerian Stock Prices
A Multiplicative Seasonal BoxJenkins Model to Nigerian Stock Prices Ette Harrison Etuk Department of Mathematics/Computer Science, Rivers State University of Science and Technology, Nigeria Email: ettetuk@yahoo.com
More informationForecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model
Tropical Agricultural Research Vol. 24 (): 23 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute
More informationTime Series Analysis
Time Series 1 April 9, 2013 Time Series Analysis This chapter presents an introduction to the branch of statistics known as time series analysis. Often the data we collect in environmental studies is collected
More informationThe University of Chicago, Booth School of Business Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay Solutions to Final Exam
The University of Chicago, Booth School of Business Business 41202, Spring Quarter 2013, Mr. Ruey S. Tsay Solutions to Final Exam Problem A: (40 points) Answer briefly the following questions. 1. Describe
More informationDescriptive Statistics
Descriptive Statistics Descriptive statistics consist of methods for organizing and summarizing data. It includes the construction of graphs, charts and tables, as well various descriptive measures such
More informationPractice 3 SPSS. Partially based on Notes from the University of Reading:
Practice 3 SPSS Partially based on Notes from the University of Reading: http://www.reading.ac.uk Simple Linear Regression A simple linear regression model is fitted when you want to investigate whether
More informationRob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1
Rob J Hyndman Forecasting using 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Outline 1 Regression with ARIMA errors 2 Example: Japanese cars 3 Using Fourier terms for seasonality 4
More informationTime Series Laboratory
Time Series Laboratory Computing in Weber Classrooms 205206: To log in, make sure that the DOMAIN NAME is set to MATHSTAT. Use the workshop username: primesw The password will be distributed during the
More informationTime Series Analysis of Aviation Data
Time Series Analysis of Aviation Data Dr. Richard Xie February, 2012 What is a Time Series A time series is a sequence of observations in chorological order, such as Daily closing price of stock MSFT in
More informationTime Series Analysis with R  Part I. Walter Zucchini, Oleg Nenadić
Time Series Analysis with R  Part I Walter Zucchini, Oleg Nenadić Contents 1 Getting started 2 1.1 Downloading and Installing R.................... 2 1.2 Data Preparation and Import in R.................
More informationTIME SERIES ANALYSIS
TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi 110 012 ram_stat@yahoo.co.in 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these
More informationExamples. David Ruppert. April 25, 2009. Cornell University. Statistics for Financial Engineering: Some R. Examples. David Ruppert.
Cornell University April 25, 2009 Outline 1 2 3 4 A little about myself BA and MA in mathematics PhD in statistics in 1977 taught in the statistics department at North Carolina for 10 years have been in
More informationTime Series Analysis
JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),
More informationAnalysis and Computation for Finance Time Series  An Introduction
ECMM703 Analysis and Computation for Finance Time Series  An Introduction Alejandra González Harrison 161 Email: mag208@exeter.ac.uk Time Series  An Introduction A time series is a sequence of observations
More informationCOMP6053 lecture: Time series analysis, autocorrelation. jn2@ecs.soton.ac.uk
COMP6053 lecture: Time series analysis, autocorrelation jn2@ecs.soton.ac.uk Time series analysis The basic idea of time series analysis is simple: given an observed sequence, how can we build a model that
More informationSPSS Guide: Regression Analysis
SPSS Guide: Regression Analysis I put this together to give you a stepbystep guide for replicating what we did in the computer lab. It should help you run the tests we covered. The best way to get familiar
More informationForecasting Using Eviews 2.0: An Overview
Forecasting Using Eviews 2.0: An Overview Some Preliminaries In what follows it will be useful to distinguish between ex post and ex ante forecasting. In terms of time series modeling, both predict values
More informationTHE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay. Solutions to Homework Assignment #2
THE UNIVERSITY OF CHICAGO, Booth School of Business Business 41202, Spring Quarter 2014, Mr. Ruey S. Tsay Solutions to Homework Assignment #2 Assignment: 1. Consumer Sentiment of the University of Michigan.
More informationThreshold Autoregressive Models in Finance: A Comparative Approach
University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC)  Conference Papers Faculty of Informatics 2011 Threshold Autoregressive Models in Finance: A Comparative
More informationForecasting the US Dollar / Euro Exchange rate Using ARMA Models
Forecasting the US Dollar / Euro Exchange rate Using ARMA Models LIUWEI (9906360)  1  ABSTRACT...3 1. INTRODUCTION...4 2. DATA ANALYSIS...5 2.1 Stationary estimation...5 2.2 DickeyFuller Test...6 3.
More informationTime Series Analysis and Forecasting
Time Series Analysis and Forecasting Math 667 Al Nosedal Department of Mathematics Indiana University of Pennsylvania Time Series Analysis and Forecasting p. 1/11 Introduction Many decisionmaking applications
More informationPayroll. 4. Print Checks. Table of Contents Print Checks...2 All...3 Department...4 Print Single Posting...5
4. Print Checks Table of Contents Print Checks...2 All...3 Department...4 Print Single Posting...5 Click on 4. Print Checks from the Main Menu and the following window will appear: The best practice is
More informationDoing Multiple Regression with SPSS. In this case, we are interested in the Analyze options so we choose that menu. If gives us a number of choices:
Doing Multiple Regression with SPSS Multiple Regression for Data Already in Data Editor Next we want to specify a multiple regression analysis for these data. The menu bar for SPSS offers several options:
More informationabout is Ox, developed by J.A. Doornik at Nuffield College, Oxford, United Kingdom.
Economy Informatics, no. 1/2001 35 A software package for time series analysis Adrian HOSPODAR Training Division, RomTelecom, ahospoda@excite.com Abstract: This paper introduces a software package dedicated
More informationSearch Marketing Cannibalization. Analytical Techniques to measure PPC and Organic interaction
Search Marketing Cannibalization Analytical Techniques to measure PPC and Organic interaction 2 Search Overview How People Use Search Engines Navigational Research Health/Medical Directions News Shopping
More informationInternational Business & Economics Research Journal December 2006 Volume 5, Number 12
Performance Of Deterministic And Stochastic Trends Models In Forecasting The Behavior Of The Canadian Dollar And The Japanese Yen Against The US Dollar Arav Ouandlous, (Email: ouandlou@savstate.edu), Savannah
More informationChapter 9: Univariate Time Series Analysis
Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of
More informationDirections for using SPSS
Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...
More informationSPSS for Exploratory Data Analysis Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav)
Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav) Organize and Display One Quantitative Variable (Descriptive Statistics, Boxplot & Histogram) 1. Move the mouse pointer
More informationTime Series Analysis
Time Series Analysis hm@imm.dtu.dk Informatics and Mathematical Modelling Technical University of Denmark DK2800 Kgs. Lyngby 1 Outline of the lecture Identification of univariate time series models, cont.:
More informationLINKING MONEY SUPPLY WITH THE GROSS DOMESTIC PRODUCT IN ROMANIA
LINKING MONEY SUPPLY WITH THE GROSS DOMESTIC PRODUCT IN ROMANIA Daniela Zapodeanu 1 Mihail Ioan Cociuba 2 ABSTRACT: Evolution of money supply and gross domestic product are in a close relationship, in
More informationForecasting the PhDsOutput of the Higher Education System of Pakistan
Forecasting the PhDsOutput of the Higher Education System of Pakistan Ghani ur Rehman, Dr. Muhammad Khalil Shahid, Dr. Bakhtiar Khan Khattak and Syed Fiaz Ahmed Center for Emerging Sciences, Engineering
More informationChapter 7 The ARIMA Procedure. Chapter Table of Contents
Chapter 7 Chapter Table of Contents OVERVIEW...193 GETTING STARTED...194 TheThreeStagesofARIMAModeling...194 IdentificationStage...194 Estimation and Diagnostic Checking Stage...... 200 Forecasting Stage...205
More informationForecasting Thai Gold Prices
1 Forecasting Thai Gold Prices Pravit Khaemasunun This paper addresses forecasting Thai gold price. Two forecasting models, namely, MultipleRegression, and AutoRegressive Integrated Moving Average (ARIMA),
More informationThe Report of Modeling Stock Market Volatility in Four Asian Tigers. During Global Subprime Crisis Using ARMAGARCH Approach
Dr. Mak, Billy S C The Report of Modeling Stock Market Volatility in Four Asian Tigers During Global Subprime Crisis Using ARMAGARCH Approach BY MUNG Tin Hong 12012084 Finance Concentration LI Yeung 12002127
More informationDoes the interest rate for business loans respond asymmetrically to changes in the cash rate?
University of Wollongong Research Online Faculty of Commerce  Papers (Archive) Faculty of Business 2013 Does the interest rate for business loans respond asymmetrically to changes in the cash rate? Abbas
More informationSimple Linear Regression in SPSS STAT 314
Simple Linear Regression in SPSS STAT 314 1. Ten Corvettes between 1 and 6 years old were randomly selected from last year s sales records in Virginia Beach, Virginia. The following data were obtained,
More informationBill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1
Bill Burton Albert Einstein College of Medicine william.burton@einstein.yu.edu April 28, 2014 EERS: Managing the Tension Between Rigor and Resources 1 Calculate counts, means, and standard deviations Produce
More informationUSE OF ARIMA TIME SERIES AND REGRESSORS TO FORECAST THE SALE OF ELECTRICITY
Paper PO10 USE OF ARIMA TIME SERIES AND REGRESSORS TO FORECAST THE SALE OF ELECTRICITY Beatrice Ugiliweneza, University of Louisville, Louisville, KY ABSTRACT Objectives: To forecast the sales made by
More informationSome useful concepts in univariate time series analysis
Some useful concepts in univariate time series analysis Autoregressive moving average models Autocorrelation functions Model Estimation Diagnostic measure Model selection Forecasting Assumptions: 1. Nonseasonal
More informationIntroduction to Regression and Data Analysis
Statlab Workshop Introduction to Regression and Data Analysis with Dan Campbell and Sherlock Campbell October 28, 2008 I. The basics A. Types of variables Your variables may take several forms, and it
More informationUsing JMP Version 4 for Time Series Analysis Bill Gjertsen, SAS, Cary, NC
Using JMP Version 4 for Time Series Analysis Bill Gjertsen, SAS, Cary, NC Abstract Three examples of time series will be illustrated. One is the classical airline passenger demand data with definite seasonal
More informationThe scatterplot indicates a positive linear relationship between waist size and body fat percentage:
STAT E150 Statistical Methods Multiple Regression Three percent of a man's body is essential fat, which is necessary for a healthy body. However, too much body fat can be dangerous. For men between the
More informationStudying Material Inventory Management for Sock Production Factory
Studying Inventory Management for Sock Production Factory Pattanapong Ariyasit*, Nattaphon Supawatcharaphorn** Industrial Engineering Department, Faculty of Engineering, Sripatum University Email: pattanapong.ar@spu.ac.th*,
More informationMGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal
MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims
More informationChapter 25 Specifying Forecasting Models
Chapter 25 Specifying Forecasting Models Chapter Table of Contents SERIES DIAGNOSTICS...1281 MODELS TO FIT WINDOW...1283 AUTOMATIC MODEL SELECTION...1285 SMOOTHING MODEL SPECIFICATION WINDOW...1287 ARIMA
More informationNCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
More informationLecture 14: Correlation and Autocorrelation Steven Skiena. skiena
Lecture 14: Correlation and Autocorrelation Steven Skiena Department of Computer Science State University of New York Stony Brook, NY 11794 4400 http://www.cs.sunysb.edu/ skiena Overuse of Color, Dimensionality,
More information9th Russian Summer School in Information Retrieval Big Data Analytics with R
9th Russian Summer School in Information Retrieval Big Data Analytics with R Introduction to Time Series with R A. Karakitsiou A. Migdalas Industrial Logistics, ETS Institute Luleå University of Technology
More informationExtended control charts
Extended control charts The control chart types listed below are recommended as alternative and additional tools to the Shewhart control charts. When compared with classical charts, they have some advantages
More informationForecasting Share Prices of Axis and ICICI Banks by Econometric Modeling
Forecasting Share Prices of Axis and ICICI Banks by Econometric Modeling Monika Saxena Assistant Professor Indus Business Academy Knowledge Park 3, Plot No 44 Greater Noida India Abstract The objective
More informationUnit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data
DEPARTMENT OF ECONOMICS ISSN 14415429 DISCUSSION PAPER 20/14 Unit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data Vinod Mishra and Russell
More informationPASS Sample Size Software. Linear Regression
Chapter 855 Introduction Linear regression is a commonly used procedure in statistical analysis. One of the main objectives in linear regression analysis is to test hypotheses about the slope (sometimes
More informationDetekce změn v autoregresních posloupnostech
Nové Hrady 2012 Outline 1 Introduction 2 3 4 Change point problem (retrospective) The data Y 1,..., Y n follow a statistical model, which may change once or several times during the observation period
More informationmean, median, mode, variance, standard deviation, skewness, and kurtosis.
Quantitative Methods Assignment 2 Part I. Descriptive Statistics 1. EXCEL Download the Alabama homicide data to use in this short lab and save it to your flashdrive or to the desktop. Pick one of the variables
More informationAppendix 1: Time series analysis of peakrate years and synchrony testing.
Appendix 1: Time series analysis of peakrate years and synchrony testing. Overview The raw data are accessible at Figshare ( Time series of global resources, DOI 10.6084/m9.figshare.929619), sources are
More informationTimeSeries Regression and Generalized Least Squares in R
TimeSeries Regression and Generalized Least Squares in R An Appendix to An R Companion to Applied Regression, Second Edition John Fox & Sanford Weisberg last revision: 11 November 2010 Abstract Generalized
More informationA model to predict client s phone calls to Iberdrola Call Centre
A model to predict client s phone calls to Iberdrola Call Centre Participants: Cazallas Piqueras, Rosa Gil Franco, Dolores M Gouveia de Miranda, Vinicius Herrera de la Cruz, Jorge Inoñan Valdera, Danny
More informationPROGNOSIS OF MONTHLY UNEMPLOYMENT RATE IN THE EUROPEAN UNION THROUGH METHODS BASED ON ECONOMETRIC MODELS
PROGNOSIS OF MONTHLY UNEMPLOYMENT RATE IN THE EUROPEAN UNION THROUGH METHODS BASED ON ECONOMETRIC MODELS Gagea Mariana Alexandru Ioan Cuza University of Iasi, Faculty of Economics and Business Administration,
More informationUsing Minitab for Regression Analysis: An extended example
Using Minitab for Regression Analysis: An extended example The following example uses data from another text on fertilizer application and crop yield, and is intended to show how Minitab can be used to
More informationData Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Data Mining Techniques Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools Occam s razor.......................................................... 2 A look at data I.........................................................
More informationStudying Achievement
Journal of Business and Economics, ISSN 21557950, USA November 2014, Volume 5, No. 11, pp. 20522056 DOI: 10.15341/jbe(21557950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us
More informationMultiple Linear Regression
Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is
More informationTurkey s Energy Demand
Current Research Journal of Social Sciences 1(3): 123128, 2009 ISSN: 20413246 Maxwell Scientific Organization, 2009 Submitted Date: September 28, 2009 Accepted Date: October 12, 2009 Published Date:
More informationStatistics and research
Statistics and research Usaneya Perngparn Chitlada Areesantichai Drug Dependence Research Center (WHOCC for Research and Training in Drug Dependence) College of Public Health Sciences Chulolongkorn University,
More informationApplication of ARIMA models in soybean series of prices in the north of Paraná
78 Application of ARIMA models in soybean series of prices in the north of Paraná Reception of originals: 09/24/2012 Release for publication: 10/26/2012 Israel José dos Santos Felipe Mestrando em Administração
More informationTechnology StepbyStep Using StatCrunch
Technology StepbyStep Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate
More informationProjects in time series analysis MAP565
Projects in time series analysis MAP565 Programming : Any software can be used: Scilab, Matlab, R, Octave, Python,... The scripts must be archived in a single file (zip, tar or tgz) and should not be printed
More informationMultiple Regression Analysis in Minitab 1
Multiple Regression Analysis in Minitab 1 Suppose we are interested in how the exercise and body mass index affect the blood pressure. A random sample of 10 males 50 years of age is selected and their
More informationCointegration and the ECM
Cointegration and the ECM Two nonstationary time series are cointegrated if they tend to move together through time. For instance, we have established that the levels of the Fed Funds rate and the 3year
More informationData Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
More informationIBM SPSS Forecasting 22
IBM SPSS Forecasting 22 Note Before using this information and the product it supports, read the information in Notices on page 33. Product Information This edition applies to version 22, release 0, modification
More information