'Partial autocorrelation' presentation slideshows

Applied Business Forecasting and Planning

Applied Business Forecasting and Planning. The Box-Jenkins Methodology for ARIMA Models. Introduction. Autoregressive Integrated Moving Average models (ARIMA models) were popularized by George Box and Gwilym Jenkins in the early 1970s.

2.12k views • 172 slides

STAT 497

STAT 497. LECTURE NOTES 2. THE AUTOCOVARIANCE AND THE AUTOCORRELATION FUNCTIONS. For a stationary process {Y t } , the autocovariance between Y t and Y t-k is and the autocorrelation function is. THE AUTOCOVARIANCE AND THE AUTOCORRELATION FUNCTIONS. PROPERTIES: 1. 2. 3.

514 views • 41 slides

STAT 497

STAT 497. LECTURE NOTES 2. THE AUTOCOVARIANCE AND THE AUTOCORRELATION FUNCTIONS. For a stationary process {Y t } , the autocovariance between Y t and Y t-k is and the autocorrelation function is. THE AUTOCOVARIANCE AND THE AUTOCORRELATION FUNCTIONS. PROPERTIES: 1. 2. 3.

740 views • 41 slides

Applied Econometric Time Series Third Edition

1.77k views • 31 slides

EC 485: Time Series Analysis in a Nut Shell

EC 485: Time Series Analysis in a Nut Shell. Data Preparation: Plot data and examine for stationarity Examine ACF for stationarity If not stationary, take first differences If variance appears non-constant, take logarithm before first differencing

297 views • 22 slides

Econometrics 1

Econometrics 1. Lecture 11 Stationarity and AR MA and ARMA Processes. Stationary and Nonstationary Series. Spurious Regression. Four ways of checking stationarity. Partial autocorrelation function and Ljung and Box statistics. Unit Root and Non-stationarity. Time Dependent Variance.

547 views • 20 slides

Univariate analysis: Analysis of a the properties of a X and Y

Univariate analysis: Analysis of a the properties of a X and Y Bivariate analysis: Analysis of the relationship between X and Y. Definition of time series: a series of succesive observation over time; e.g. daily, monthly, yearly Output series: Y t Input series: X t

401 views • 20 slides

ARIMA

ARIMA. Using Stata. Time Series Analysis. Stochastic Data Generating Process Stable and Stationary Process Autoregressive Process: AR(p) Moving Average Process: MA(q) ARMA(p,q) Integrated Nonstationary Process ARIMA(p,d,q). AR(p). MA(q). ARMA(p,q). Time Series Analysis.

929 views • 14 slides