A01=Haim Y. Bleikh
A01=Warren L.Young
advanced time series adjustment techniques
analyzing
ARIMA Methodology
ARIMA Model
ARIMA Process
Author_Haim Y. Bleikh
Author_Warren L.Young
Auto Correlation Function
Box Jenkins Technique
business cycle research
Case Shiller Home Price Index
Case Shiller Index
Category1=Non-Fiction
Category=KC
Category=KFCR
Category=KFFM
Category=KJC
Category=KJMV5
Category=KJT
Category=NL-KF
Category=NL-KJ
causality
component
COP=United Kingdom
Cyclical Component
decomposition
econometric modelling
empirical economic data
eq_bestseller
eq_business-finance-law
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Format=BB
Granger's Causality
grangers
Granger’s Causality
HMM=234
IMPN=Ashgate Publishing Limited
irregular
Irregular Components
ISBN13=9781409441922
Lag Length Selection
Language_English
Non-stationary Time Series
OLS Regression
OLS Standard Error
PA=Available
Partial Auto Correlation Function
PD=20140723
Price=€100 to €200
procedure
PS=Active
PUB=Taylor & Francis Ltd
Real Gdp
Regression Models
risk management analytics
root
seasonal
Seasonal Adjustment
seasonal adjustment methods
Seasonal Adjustment Procedure
Seasonal Adjustment Program
Seasonal ARIMA
Seasonal ARIMA Model
stochastic processes
Subject=Business & Management
Subject=Finance & Accounting
Time Series
Time Series Adjustment
unit
Unit Root
WG=408
WMM=156
Product details
- ISBN 9781409441922
- Weight: 408g
- Dimensions: 156 x 234mm
- Publication Date: 23 Jul 2014
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
- Language: English
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In Time Series Analysis and Adjustment the authors explain how the last four decades have brought dramatic changes in the way researchers analyze economic and financial data on behalf of economic and financial institutions and provide statistics to whomsoever requires them. Such analysis has long involved what is known as econometrics, but time series analysis is a different approach driven more by data than economic theory and focused on modelling. An understanding of time series and the application and understanding of related time series adjustment procedures is essential in areas such as risk management, business cycle analysis, and forecasting. Dealing with economic data involves grappling with things like varying numbers of working and trading days in different months and movable national holidays. Special attention has to be given to such things. However, the main problem in time series analysis is randomness. In real-life, data patterns are usually unclear, and the challenge is to uncover hidden patterns in the data and then to generate accurate forecasts. The case studies in this book demonstrate that time series adjustment methods can be efficaciously applied and utilized, for both analysis and forecasting, but they must be used in the context of reasoned statistical and economic judgment. The authors believe this is the first published study to really deal with this issue of context.
Haim Y. Bleikh is a resident researcher at the Taub Center for Social Policy Studies in Israel. Warren L. Young is Associate Professor of Economics at Bar Ilan University, Israel.
