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

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...

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

{\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...

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

interpreted within the context of information theory. Bayes factor Conditional entropy Conditional probability Empirical likelihood Likelihood principle Likelihood-ratio...

Park, Sung Y.; Bera, Anil K. (2009). "Maximum entropy autoregressive conditional heteroskedasticity model". Journal of Econometrics. 150 (2): 219–230. CiteSeerX 10...

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

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

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...

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

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

{Var} [X\mid Y])+\operatorname {Var} (\operatorname {E} [X\mid Y]).} The conditional expectation E ⁡ ( X ∣ Y ) {\displaystyle \operatorname {E} (X\mid Y)}...

as one of the parameters. As another example, consider a first-order autoregressive model, defined by xi = c + φxi−1 + εi, with the εi being i.i.d. Gaussian...

ISBN 9780412039911. Park SY, Bera AK (2009). "Maximum entropy autoregressive conditional heteroskedasticity model". Journal of Econometrics. 150 (2): 219–230. doi:10...

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

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

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

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...

commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability...

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

standard deviation of a slice of the multivariate distribution (i.e. the conditional distribution) along the line in the direction of the unit vector η ^...

(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...

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...

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...

(2003). "Time-series econometrics: Cointegration and autoregressive conditional heteroskedasticity". Advanced Information on the Bank of Sweden Prize in...

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

(see linear regression), this allows the researcher to estimate the conditional expectation (or population average value) of the dependent variable when...

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