A01=Haim Y. Bleikh
A01=Warren L.Young
advanced time series adjustment techniques
Age Group_Uncategorized
Age Group_Uncategorized
analyzing
ARIMA Methodology
ARIMA Model
ARIMA Process
Author_Haim Y. Bleikh
Author_Warren L.Young
Auto Correlation Function
automatic-update
Box Jenkins Technique
business cycle research
Case Shiller Home Price Index
Case Shiller Index
Category1=Non-Fiction
Category=KFCR
Category=KFFM
Category=KJC
Category=KJM
Category=KJT
causality
component
COP=United Kingdom
Cyclical Component
decomposition
Delivery_Pre-order
econometric modelling
empirical economic data
eq_bestseller
eq_business-finance-law
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Granger's Causality
grangers
Granger’s Causality
irregular
Irregular Components
Lag Length Selection
Language_English
Non-stationary Time Series
OLS Regression
OLS Standard Error
PA=Temporarily unavailable
Partial Auto Correlation Function
Price_€20 to €50
procedure
PS=Active
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
softlaunch
stochastic processes
Time Series
Time Series Adjustment
unit
Unit Root
Product details
- ISBN 9780367669485
- Weight: 270g
- Dimensions: 156 x 234mm
- Publication Date: 30 Sep 2020
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
- 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.
