time series 2002

35 %
65 %
Information about time series 2002

Published on August 21, 2007

Author: Freedom

Source: authorstream.com

Time series Regression : Descriptive Methods :  Time series Regression : Descriptive Methods Jun Choi Ghiyoung Im AGENDA:  AGENDA Overview Definition Examples Problems andamp; Treatment Pros andamp; Cons of Time-Series Regression Application to IS Field References Slide3:  TIME SERIES A collection of data Xt (t = 1, 2, …, T) with the interval between Xt and Xt+1 being fixed and constant. Interested in not only the particular values of the observations but in the order in which they appear. The purpose Find the particular mechanism to use in forecasting the future. Put that mechanism to use in forecasting the future. Slide4:  TIME SERIES - A time series is a sequence of observations which are ordered in time (or space). If observations are made on some phenomenon throughout time, it is most sensible to display the data in the order in which they arose, particularly since successive observations will probably be dependent. Time series are best displayed in a scatter plot. The series value X is plotted on the vertical axis and time t on the horizontal axis. - Time series regression models are specially suitable for evaluating short-term effects of time-varying exposures. In time-series studies, a single population is assessed with reference to its change over the time. Slide5:  A firm’s performance (Y) is related to its IS investment (X1) and Marketing intensity (X2), for the past 16 years. EX 1 Slide6:  The size of organization (y) is related to the amount of IS investment five years earlier (x) for the past 20 years. EX 2 Slide7:  MULTICOLLINEARITY One independent variable is excessively linearly correlated with another independent variable HETEROSCEDASTICY The error terms don’t have a constant variance AUTOCORRELATION Error terms are correlated through time PROBLEMs that may occur in Time Series Slide8:  AUTOCORRELATED ERROR TERMS Autocorrelated error terms is when the error terms are correlated with each other. This is only a consideration when the model is a 'time series' model. There are three treatments for autocorrelated error terms: finding an important omitted variable transforming the variables based upon generalized least squares introducing Time as a variable on the right hand side of the equation Slide9:  The Durbin-Watson Statistic is used to test for the presence of first-order autocorrelation in the residual of a regression equation. The test compares the residual for time period t with the residual from time period t-1 and develops a statistic that measure the significance of the correlation between these successive comparisons. The formula for the statistic is : THE DURBIN-WATSON STATISTIC Slide10:  The statistic is used to test for the presence of both positive and negative correlation in the residuals. The statistic has a range of from 0 to 4, with a midpoint of 2. The Null Hypothesis ( H0 ) is that there is no significant correlation. Slide11:  STRENGTHS Validate the relationship over time. (Consistency, Specification, Transformation) Explain the past and forecast the future. WEAKNESSES Time, money, and energy matter Still ambiguous in context and process PROS andamp; CONS of TIME-SERIES Slide12:  Motivation: Productivity paradox Firm-level investment in IT and corresponding productivity Underlying theory: economic theory (Cobb-Douglas andamp; CES production function, Tobin’s q, etc.) APPLICATION to IS Field Slide13:  CD function: Q= (IL, L, IK, K) Q: Output, IL: IT labor, IK: IT capital CD function: ln(Q)ij = 1ln(IL)ij + 2ln(L)ij + 3ln(IK)ij+ 4ln(K)ij : elasticity of each of the input factors i: individual firm, j: year * Source: Kudyba ISR2002 APPLICATION Example * Slide14:  APPLICATION Example * IT Capital Regression Results (CD Prod Function) Output (Sales) Slide15:  REFERENCES Ostrom Jr. (1990). Time series regression. Beverly Hills, CA: Sage. Nelson (1973). Applied Time Series Analysis. San Francisco: Holden-Day. McCleary andamp; Hay, Jr. (1980). Applied Time Series Analysis for the Social Sciences. Beverly Hills, CA: Sage. Kudyba (2002). Increasing returns to information technology. ISR. Hitt andamp; Brynjolfsson (1996). Productivity, business profitability, and consumer surplus: Three different measures of information technology value. MIS Quarterly. Bharadwaj et al. (1999). Information technology effects on firm performance as measured by Tobin's q. Management Science.

Add a comment

Related presentations

Related pages

Signing Time! (TV Series 2002– ) - IMDb

With Rachel Coleman, Alex Brown, Leah Coleman. An educational show on PBS that teaches children ASL, or American Sign Language, through song and repetition.
Read more

2002 World Series - Wikipedia, the free encyclopedia

The 2002 World Series was a best-of-seven playoff series to determine the champion of Major League Baseball for the 2002 season. It was the 98th series ...
Read more

Street Time (TV Series 2002–2003) - IMDb

With Rob Morrow, Michelle Nolden, Alexander Conti, Scott Cohen. Follows a fair parole officer and his parolee who wants to go straight but his former ...
Read more

Introduction to Time Series and Forecasting

The Bartlett Press, Inc. brockwel 8·i·2002 1:59 p.m. Page iii. Peter J. Brockwell Richard A. Davis. Introduction to Time Series and Forecasting. Second ...
Read more

Editorial - The Comprehensive R Archive Network

Vol. 2/2, June 2002 2 Time Series in R 1.5.0 by Brian D. Ripley R has shipped with a package ts since 0.65.0 in mid 1999; this package has been enhanced ...
Read more

Bootstrapping Financial Time Series - Ruiz - 2002 ...

July 2002 Pages 271–300 ... It is well known that time series of returns are characterized by volatility clustering and excess kurtosis.
Read more

Segmented regression analysis of interrupted time series ...

How to Cite. Wagner, A. K., Soumerai, S. B., Zhang, F. and Ross-Degnan, D. (2002), Segmented regression analysis of interrupted time series studies in ...
Read more

Time Series (Final) - Australian Bureau of Statistics

COMPONENTS OF A TIME SERIES A time series can be thought of as comprising three separate components: the trend, any calendar related effects, and
Read more

Illiquidity and stock returns: cross-section and time ...

This paper shows that over time, expected market illiquidity positively affects ex ante stock excess return, suggesting that expected stock excess return ...
Read more