In econometrics, the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance...

In the statistical analysis of time series, autoregressive–moving-average (ARMA) models are a way to describe of a (weakly) stationary stochastic process...

In statistics, econometrics, and signal processing, an autoregressive (AR) model is a representation of a type of random process; as such, it can be used...

variances are important parts of autoregressive conditional heteroskedasticity (ARCH) models. The conditional variance of a random variable Y given another...

White, Halbert (1980). "A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity". Econometrica. 48 (4): 817–838...

role in data analysis aimed at identifying the extent of the lag in an autoregressive (AR) model. The use of this function was introduced as part of the Box–Jenkins...

process model. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. VAR models are often...

changes of variance over time (heteroskedasticity). These models represent autoregressive conditional heteroskedasticity (ARCH) and the collection comprises...

of Finance, May 1982 V. 37: #2 1982 – Robert Engle, Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation, Seminal...

elements of the matrix are equal to each other. On the other hand, an autoregressive matrix is often used when variables represent a time series, since correlations...

be to predict the likelihood of a homeowner defaulting on a mortgage. Conditional random fields, an extension of logistic regression to sequential data...

Granger causality analysis is usually performed by fitting a vector autoregressive model (VAR) to the time series. In particular, let X ( t ) ∈ R d × 1...

via scatterplots Quantitative measures of dependence Descriptions of conditional distributions The main reason for differentiating univariate and bivariate...

is no linear relationship. The partial correlation coincides with the conditional correlation if the random variables are jointly distributed as the multivariate...

Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR) Frequency domain...

{\displaystyle t\mapsto F_{X|Y=y}^{-1}(t)} is the inverse of the conditional cdf (i.e., conditional quantile function) of x ↦ F X | Y ( x | y ) {\displaystyle...

ISBN 9780412039911. Park SY, Bera AK (2009). "Maximum entropy autoregressive conditional heteroskedasticity model". J. Econom. 150 (2): 219–230. doi:10.1016/j.jeconom...

Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR) Frequency domain...

in Missouri, Illinois, and the surrounding regions Autoregressive conditional heteroskedasticity, a time series regression model of the standard deviation...

(X_{1}\mid X_{2}=x_{2})=1-\rho ^{2};} thus the conditional variance does not depend on x2. The conditional expectation of X1 given that X2 is smaller/bigger...

variables contained in high-dimensional contingency tables. If some of the conditional independences are revealed, then even the storage of the data can be...

introduced the technique in 1983, defining the propensity score as the conditional probability of a unit (e.g., person, classroom, school) being assigned...

S2CID 2884450. McQuarrie, A. D. R.; Tsai, C.-L. (1998). Regression and Time Series Model Selection. World Scientific. Sparse Vector Autoregressive Modeling...

Cross-correlation (XCF) ARMA model ARIMA model (Box–Jenkins) Autoregressive conditional heteroskedasticity (ARCH) Vector autoregression (VAR) Frequency domain...

Park, Sung Y.; Bera, Anil K. (2009). "Maximum entropy autoregressive conditional heteroskedasticity model" (PDF). Journal of Econometrics. 150 (2): 219–230...

Correlogram Autocovariance Autoregressive conditional duration Autoregressive conditional heteroskedasticity Autoregressive fractionally integrated moving...

moving average (EWMA). Technically it can also be classified as an autoregressive integrated moving average (ARIMA) (0,1,1) model with no constant term...

subpopulations defined by X = 1 and X = 0 are defined in terms of the conditional probabilities given X, i.e., P(Y |X): Y = 1 Y = 0 X = 1 p 11 p 11 + p...

{\displaystyle \beta _{j}} . An example of a linear time series model is an autoregressive moving average model. Here the model for values { X t {\displaystyle...

financial market volatility and for the GARCH (generalized autoregressive conditional heteroskedasticity) model. Tim Bollerslev received his MSc in economics...