Time Series Analysis

Partial Autocorrelation Function
A detailed examination of the partial autocorrelation function (PACF), explaining its meaning and significance in time series analysis.
Koyck transformation
A method used to transform an infinite geometric lag model into a finite model with a lagged dependent variable.
Augmented Dickey-Fuller Test
A statistical test used to determine the presence of unit root in time series data, thus helping in analysis of data stationarity.
Autocovariance
Covariance between a random variable and its lagged values in time series analysis
Autocovariance Function
A sequence of autocovariances of a covariance stationary time series process as a function of the lag length.
Cointegration
Understanding the concept of cointegration in time series analysis, particularly in relation to non-stationary variables that share a common stochastic trend.
Covariance Stationary Process (Second-Order Stationary Process, Weakly Stationary Process)
Understanding the concept of covariance stationary process in time-series analysis, including its definition, historical development, and analytical frameworks in economics.
detrending
Detrending: Definition, Methods, and Analytical Frameworks in Economics
Dickey–Fuller
A statistical test used to determine if a time series is a random walk or stationary.
Lag Operator
Definition and explanation of the lag operator in economics.
Order of Integration
The minimum number of times it is necessary to difference a non-stationary time series to produce a stationary series.
Partial Autocorrelation Coefficient
The definition and meaning of Partial Autocorrelation Coefficient in econometrics and time series analysis.
Persistence
Definition of persistence and its significance in time series analysis, particularly focusing on serial correlation or autocorrelation.
White Noise in Economics
An entry exploring the concept of white noise, particularly within economic contexts such as time series analysis and autoregressive moving average models.
Wold’s Decomposition Theorem
A fundamental theorem in time series analysis that decomposes a zero-mean covariance stationary stochastic process into a deterministic and a non-deterministic part.
Yule–Walker equations
Difference equations relating the autocorrelation coefficients of an autoregressive process to the coefficients on the lags.