Engle RF. Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation. Econometrica 1982; 50: 987--1008.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....scheme when estimating the parameters in the model. Secondly, there is empirical evidence that suggests that some risk factors have signi cant dependence structure over time. Unlike the rst one, this problem can not be solved without introducing more complex models, such as ARCH or GARCH models [18, 9, 44, 33]. If we want to use the model for simulating a sequence of returns this property might become important. However, in our application, simulating value at risk, we are interested in a one step simulation and it is arguably less signi cant for the nal result. Despite its shortcomings, the random ....

R.F Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom in ation. Econometrica, 50(4):987-1007, July 1982.

....the population variance is smaller. The motivations offered for particular TVP estimating methods are invariably related to rational expectations, the mean variance representation of risk and risk Bollerslev [3] identifies the core econometric processes of relevance here to be the ARCH process [4], the GMM process [5] and GARCH [6] 5 aversion or some similar equilibrium notion from economic theory. There are, however, no microeconomic equilibrium models that generate both leptokurtosis and clustered volatility either analytically or by means of simulation. 3.2 Self organised criticality ....

Engle, R.F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation", Econometrica 50, 9871007.

....use linear functions (97) We can indirectly adjust , to get optimal , that change with under the control of . In implementation, the maximization can be made simply by using gradient ascent method. The above method will deteriorate when the variance varies with . One solution is to use the ARCH [26] or GARCH [11] to estimate the time varying variance and then make learning adaptively is a stepsize (98) D. Macroeconomics Modulated Independent State Space Model For a market of risk securities which form a vector in , if we can get a portfolio via the weights of which also form a vector in ....

R. F. Engle, "Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, pp. 987--1007, 1982.

....have a Lagrange multiplier (LM) type interpretation. This class includes the Tsay (1986) test, the RESET tests of Ramsey (1969) and Thursby and Schmidt (1977) the neural network test of Lee, White and Granger (1993) White s (1987) dynamic information test, LM tests against ARCH effects (Engle (1982) and McLeod and Li (1984) the LM tests of Saikkonen and Luukkonen (1988) against bilinear alternatives and exponential autoregressive models, and the LM test of Luukkonen, Saikkonen and Terasvirta (1988) against smooth transition 15 autoregressive models. Two portmanteau tests of linearity are ....

.... However, these tests are usually not very powerful against other departures of the null, while the BDS test appears quite powerful for almost every departure of the null for example, as documented by Brock, Hsieh and LeBaron (1991) the power of the BDS test against ARCH alternative is close to Engle s (1982) LM test. This is true for both nonlinear stochastic processes and nonlinear deterministic, chaotic alternatives. 2.2 Consistent tests of linearity It should be noted that neither the BDS test nor the bispectrum test are consistent tests of nonlinearity, that is, there are known departures from ....

[Article contains additional citation context not shown here]

Engle, R. F. (1982), "Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation," Econometrica 50, 987-1007.

....of such patterns. In this respect, the SV model has been introduced by Clark (1973) Tauchen and Pitts (1983) Taylor (1986 1994) among many other authors. These models appear as an alternative specification to the Autoregressive Conditionally Heteroskedastic (ARCH) model as introduced by Engle (1982) and Bollerslev (1986) The SV models turn out to be more appealing for many reasons: broad general features of the data can be reproduced (persistent volatility, volatility clustering e#ect, leverage e#ect, asymmetries and leptokurtosis) less parameters have to be estimated, and SV models (3.5) ....

Engle, R. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of the U.K. Inflation", Econometrica, 50, 987-1008.

....conditional variance of r M;t as an instrument. This is consistent with the speci#cation used in much of the literature on persistence and asymmetry in variance. 1 Our speci#cations for conditional variance and skewness are GARCH#1,1# in the terminology of the ARCH#GARCH literature introduced by Engle #1982# and Bollerslev #1986#. The initial GARCH speci#cation assumed that returns come from a conditionally normal distribution. However,stock market returns have thicker 3 tails than conditional normal distributions would imply. Bollerslev #1987# assumes that the returns come from a central t ....

Engle, R. F., #Autoregressive Conditional Heteroskedasticity With Estimation Of The Variance Of United Kingdom In#ation." Econometrica, 50 #July 1982#, 987-1008.

....j t Gammaj : Here t = E(L t jF t Gamma1 ) the conditional expected waiting time, where F t is a filtration, potentially containing all information up til time t Gamma 1. The mathematical structure of this model is identical to that of the square of an GARCH model associated with the work of Engle (1982) and Bollerslev (1986) The model has many similarities with earlier work by Wold (1948) and Cox (1972) In practice Engle and Russell (1998) have used an exponential or Weibull distribution on the f t g. Straightforward alternative structures would be to parameterise the log t instead of the ....

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica 50, 987--1007.

....of the conditional heteroskedasticity exhibited by the . nancial time series such as exchange rates and stock prices. One serious drawback of this type of models, however, is their internal inconsistency by aggregation. In fact, the class of strong GARCH processes, as de. ned by Engle [10] and Bollerslev [4] is not closed under contemporaneous aggregation. In general, the sum of two independent strong GARCH processes cannot be described as a strong GARCH whose parameters are functions of the parameters of the two underlying processes. In order to overcome this aggregation ....

....of QMLE under dierent density assumptions, namely Gaussian, Laplace and stable. 2 Problem Identi. cation To describe the problem we consider, suppose that the data consists of observations y t ; t = 1; T generated by a univariate GARCH (1; 1) process, as . rst de. ned by Engle [10] and 2 Bollerslev [4] Let F t (y t1 ; y t2 ; denote the information set at period t and let f t g be a sequence of innovations that is independent and identically distributed (iid) The process fy t g to be estimated can then be described as y t = t t ; 1) 2 t = ....

Engle, R.F., (1982), Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom in#ation, Econometrica, 50(4), 987--1007.

....options and may also a#ect international trade. It is well known that volatility is time varying in high frequency data and that periods of high volatility tend to cluster. To capture this, many authors have used autoregressive conditional heteroskedasticity (ARCH) models, as introduced by Engle (1982) and extended to generalized ARCH (GARCH) in Bollerslev (1986) 1 Such models usually improve the fit a lot compared with a constant variance model and, as Andersen and Bollerslev (1997) claimed, GARCH models provide good volatility forecasts. In this paper we show that GARCH forecasts are, ....

Engle, R.F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation," Econometrica, 50, 987-1008.

....The modeling of the second moments of asset returns has been a major field of study in finance over the last twenty years. Although regularities in the variances of asset returns were noted by Mandelbrot (1963) the explosion in volatility modeling can generally be traced to the work of Engle (1982) and Bollerslev (1986) 1 Research in the area of volatility models has expanded in many directions and has led to a wide variety of modeling techniques, both univariate for individual assets and multivariate for asset portfolios. Most of this research has focused on the in sample fit of ....

Engle, R.F., 1982. "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation," Econometrica, 50, 987-1008.

....Conditional Heterskedasticity (ARCH) models are designed to forecast and model conditional variances. The variance of the dependent variable is modeled as a function of past values of the dependent variable (added by independent, or exogenous variables) ARCH models were introduced by Engle [Engle 1982] and generalized as GARCH by Bollerslev [Bollerslev 1986] These models are widely used in various branches of econometrics, i.e. in financial time series analysis. In an ARCH model, two distinct specifications are considered: One for the 5 Zangari and Venkatamaran enforce this restriction ....

Engle, R.: Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation, Econometrica 50:987-1008, 1982

....returns display pronounced volatility clustering. Only over the last decade have financial economists begun to seriously model these temporal dependencies. While the vast majority of the earlier studies relied on the Autoregressive Conditional Heteroskedastic (ARCH) framework pioneered by Engle (1982), there is now a large and diverse time series literature on volatility modeling. Almost universally, reported results point towards a very high degree of intertemporal volatility persistence; see, e.g. Bollerslev, Chou and Kroner (1992) Bollerslev, Engle and Nelson (1994) Ghysels, Harvey and ....

Engle, R.F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation," Econometrica, 50, 987-1008.

....data is particularly attractive on the basis of both modeling and computational considerations. In terms of modeling, the Gaussian component of the Hermite expansion makes it easy to subsume into the leading term familiar time series models, including vector autoregressive models and ARCH models (Engle, 1982). These models are generally considered to give excellent first approximations in a wide variety of applications. In terms of computation, a Hermite density is easy to evaluate and differentiate. Also, its moments are easy to evaluate because they correspond to higher moments of the normal, which ....

....j where vech(R) denotes a vector of length M(M 1) 2 containing the elements of the upper triangle of R and jx t Gamma1 Gamma j t Gamma2 j denotes a vector containing the absolute values of y t GammaL r Gamma x t GammaLr Gamma1 through y t Gamma1 Gamma x t Gamma2 . The classical ARCH (Engle, 1982) has Sigma x depending on a linear function of squared lagged residuals. The SNP version of ARCH is more akin to the suggestions of Nelson (1991) and Davidian and Carroll (1987) Since the absolute value function is not differentiable, juj is approximated in the formula for R x above by the twice ....

Engle, R. F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica 50, 987--1007.

....data set and with an exchange rate time series and then compare the method with two others (one classical and one Bayesian) that are available in statistical and econometric program packages. 1 Introduction Autoregressive conditional heteroskedastic (ARCH) models were rst introduced by Engle (1982). Applications can be found in several elds of economics and nance and a recent survey can be found in e.g. Gourieroux (1997) The numerical techniques based on the BHHH method of Berndt et al. 1974) for maximum likelihood estimation (MLE) are used for the VAR ARCH models in most current ....

ENGLE, R. F. (1982): Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom in ation. Econometrica, 50(4), 987-1006.

....in the conditional variance De ne a conditional heteroskedasticity model as R t = m(R t ) t ; t i.i.d. N(0; 2 t ) 7) where m(R t ) denotes the regression function, the conditional variance 2 t depends on the information set I t consisting of everything dated t 1 or earlier. Engle (1982) proposed the ARCH(p) skedastic function: 2 t = L) 2 t (8) where (L) is a lag polynomial of order p. Robinson (1991) has considered the general case of conditional heteroskedasticity by resorting to long memory in nite order lag polynomials, and has proposed the following ....

ENGLE, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom In ation. Econometrica, 50, 987-1007.

....Prediction intervals; Foreign exchange rates. I would like to thank Allan Gregory for his insight and guidance. I also thank James MacKinnon for helpful comments. 1 1 Introduction The Autoregresive Conditional Heteroskedasticity (ARCH) class of models was originally introduced by Engle (1982) and has become a core part of empirical nance. The issue of forecasting with these models has been discussed by Koenker and Zhao (1996) Baillie and Bollerslev (1992) Granger, White, and Kamstra (1989) Geweke (1989) Diebold (1988) and Engle and Kraft (1983) However, the procedures developed ....

Engle, R. F., (1982), \Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom In ation,"Econometrica, 50, 987-1007.

....significant non zero off diagonal contribution, the modeling can be improved by transforming the data by the inverse of the noise covariance matrix. ffl ARSCH Models (AutoRegressive Special Conditional Heteroskedasticity) Estimating the noise levels enables us to generalize ARCH and GARCH models [Engle, 1982, Bollerslev, 1986, Bollerslev et al. 1990] since we allow for nonlinearities at every level, we call these models where we input the noise levels averaged over an exponentiallydecaying window in time ARSCHmodels (AutoRegressive Special Conditional Heteroskedasticity) The remainder of this ....

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation. Econometrica, 50:987--1007.

.... the generalized ARCH 1 There are many papers which report stylized facts for exchange rates, including Boothe and Glassman (1987) Diebold (1988) Engle and Hamilton (1990) Hsieh (1989) Kaehler and Marnet (1993) Vlaar and Palm (1993) and Nieuwland, Vershchoor, and Wol (1994) 1 class (Engle (1982), Bollerslev (1986) is based on an ARMA function of past innovations. These models have become the workhorse for parameterizing intertemporal dependence in the conditional variance of speculative returns. 2 Markov switching models for mixture distributions in which draws for component ....

Engle, R. F. (1982): \Autoregressive Conditional Heteroskedasticity with Estimates of the UK in ation," Econometrica, 50, 987-1008.

....support of the Social Sciences and Humanities Research Council of Canada and the second author acknowledges support of the School of Graduate Studies and Research at Queen s University. 1 1 Introduction The Autoregressive Conditional Heteroskedasticity (ARCH) class of models, introduced by Engle (1982), has become a core part of empirical nance. Indeed, citations of ARCH are too numerous to list (see Bollerslev et al. 1992) for an excellent review. These parsimonious models have been successful in capturing the volatility clustering so prevalent in nancial data. Periods of high (low) ....

Engle, R. F., (1982), \Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom In ation,"Econometrica, 50, 987-1007.

....that is orthogonal to the time t information set, the standard EWMA estimator can also be interpreted as an infinite order autoregressive model for the squared return. The standard EWMA estimator is a special case of the generalized autoregressive conditional heteroscedasticity, or GARCH model (Engle, 1982; Bollerslev, 1986) The GARCH(1,1) model for the conditional variance of returns is given by 2 2 2 1 0 2 1 t t t z = 3) where 0 , 1 and 2 are parameters to be estimated. When 0 0 = and 1 2 1 = the GARCH model reduces to the standard EWMA estimator, and is ....

Engle, R., 1982, Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica 50, 987-1007.

....In this subsection we concentrate on the e#ect of autoregressive conditional heteroskedasticity (ARCH) on moment ratio curves. It is well known that even if the innovations to the ARCH process are (conditionally) normal, the unconditional distribution (stationary distribution) is heavy tailed, see Engle (1982), de Haan et al. 1989) and Nelson (1990) The following theorem summarizes what pattern we can expect for the volatility ratio curves in case of ARCH. Theorem 3 (Volatility clusters) Let the return innovations follow a finite variance normal ARCH(p)process # t =h 1 2 t x t ,x t i.i.d. N(0, ....

Engle, R.F. (1982): "Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation," Econometrica, 50, 987-1008.

....skewness and kurtosis of these residuals, we compute a normality test by Doornik and Hansen (1994) which has, under the null hypothesis of normality, an approximate 2 distribution with two degrees of freedom. To check the covariance structure of the residuals, we also calculate the ARCH tests (Engle, 1982) with 5 lags. This test has a 2 distribution with ve degrees of freedom. Although we lack a theoretical justication for the residuals (24) they can at least be used to detect model misspecication. 10 3.5 Implementation issues While the previous sections primarily considered theoretical ....

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inAEation. Econometrica, 50, 9871006.

....ante variance predictions implied by the FIX model (solid line) along with the realized variances (dashed line) The FIX model is defined by equation 3 and its estimates are reported in Table 1. is confined to the ARCH approach to modeling volatility. Since the introduction of the ARCH model by Engle (1982) numerous extensions have been proposed. 12 However only the FIGARCH model developed by Baillie, Bollerslev and Mikkelsen (1996) and the FIEGARCH model formulated Bollerslev and Mikkelsen (1996) explicitly allow for the long memory property of volatility. We shall focus on these two ....

Engle, R. F. 1982. "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance for U.K. Inflation." Econometrica 50:987--1008.

....Thus there is a need to design a test procedure for the parameter constancy in the RCA model with dependent errors. In this article, we intend to develop the LBI test in the RCA model with ARCH errors as the AR ARCH model is widely utilized in actual practice. The ARCH model, introduced by Engle (1982), has been proved very useful for modeling economic data possessing high volatility. Since then, statistical methodology for time series models with ARCH errors has been rapidly developed. For instance, Weiss (1984) considered a class of ARMA models with ARCH errors and studied statistical ....

Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica. 50, 987-1008.

....Black Scholes formula (Merton (1973) However, neither constancy nor a time deterministic behaviour is supported by empirical studies. 1 A type of variance behaviour which has gained widespread acceptance in the literature is Generalized Autoregressive Conditional Heteroskedasticity or GARCH (Engle (1982), Bollerslev (1986) Using a discrete time equilibrium asset pricing framework (Rubinstein (1976) Brennan (1979) Duan (1995) has recently developed a European stock option valuation model when the continuously compounded stock returns follow a GARCH process. Duan s model contains the ....

Engle, Robert, 1982, Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation, Econometrica 52, 289-311.

....constant over time. The evolution of the conditional volatility through time has been successfully modelled as an ARMA process. This type of models is known under the acronym of GARCH models which stands for Generalised Auto Regressive Conditional Heteroskedasticity models and has been proposed by ENGLE (1982) and BOLLERSLEV (1986) HAMAO et al. 1990) use a univariate GARCH model to document the volatility spillovers between New York, Tokyo and London stock exchanges. They find that the links between the volatilities of different markets are significant and that an increase in volatility in one market ....

ENGLE, R. F. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, Econometrica, 50, 987-1007.

....using a smoothing factor of V=0.94. This corresponds to an IGARCH(1,1) model in which the intercept is fixed at zero and the moving average coefficient in the ARIMA representation for the squared returns equals 0.94. The most widespread procedure used by academics is arguably the GARCH model of Engle (1982) and Bollerslev (1986) with the GARCH(1,1) model constituting the leading case. As with the VAR model discussed in the previous section, we will base the GARCH(1,1) model estimates on the 2,449 daily in sample returns from December 1, 1996, through December 1, 1996. Consistent with previous ....

Engle, R.F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation," Econometrica, 50, 987-1008.

....have also received increasing attention in recent years, as the Slutsky Yule theory of linear modeling and forecasting has matured, and that trend will likely continue. Models of volatility dynamics, which permit volatility forecasting, are an important example; the literature began with Engle s (1982) seminal contribution, and recent surveys include Bollerslev, Chou and Kroner (1992) and Bollerslev, Engle, and Nelson (1994) We will, however, avoid discussion of nonlinear methods for the most part, because although they are clearly of value in areas such as finance, they are less useful in ....

Engle, R.F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, 50, 987-1007.

....because the estimated standard errors tend to become more accurate. 18 Note that if one is estimating the reduced form model and if = 0, then the information matrix is block diagonal between the parameters in the mean equations and the parameters in the covariance equations (see Kraft and Engle, [13]) This means that efficient estimates of Pi 1 can be calculated independently of Xi, given only p T consistent estimates of Xi. Similarly, efficient estimates of Xi can be calculated independently of Pi 1 , given only p T consistent estimates of Pi 1 . This suggests the following ....

Kraft, D.F. and R.F. Engle. Autoregressive conditional heteroskedasticityinmultiple time series models. unpublished manuscript, Department of Economics, UC San Diego, 1983. 29

....uncertainty, econometricians have only recently begun developing an analytical framework to deal with uncertainty. A central feature of this framework is the modelling of second and possibly higher moments as well. One of the most prominent tools used to model the second moments is due to Engle [8]. Engle [8] suggested that these unobservable second moments could be modelled by specifying a functional form for the conditional variance and modelling the first and second moments jointly, giving what is called in the literature the Autoregressive Conditional Heteroskedasticity (ARCH) model. Of ....

....econometricians have only recently begun developing an analytical framework to deal with uncertainty. A central feature of this framework is the modelling of second and possibly higher moments as well. One of the most prominent tools used to model the second moments is due to Engle [8] Engle [8] suggested that these unobservable second moments could be modelled by specifying a functional form for the conditional variance and modelling the first and second moments jointly, giving what is called in the literature the Autoregressive Conditional Heteroskedasticity (ARCH) model. Of course, ....

[Article contains additional citation context not shown here]

Engle, R.F. Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (1982): 987-1007.

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Engle RF. Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation. Econometrica 1982; 50: 987--1008.

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Engle R.F. (1982). Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation. Econometrica 50, 987--1008.

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Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica, 50:987--1007. Everitt, B. (1984). An Introduction to Latent Variable Models. Chapman and Hall, London.

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Engle, R.F.. 1982, Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation. Econometrica, 50, 987-1008.

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R. F. Engle, "Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation," Econometrica, vol. 50, no. 4, pp. 987--1007, 1982.

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Engle, R. (1982), \Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Ination,"Econometrica, 50, 987-1008.

No context found.

R.F. Engle. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50:987--1008, 1982.

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Engle, R.F., 1982, "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of the United Kingdom Inflation", Econometrica, 50, 987 - 1007.

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Engle R. F.: Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation, in: Econometrica, Vol. 50, 1987, pp. 987-1007.

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Engle, R.F. (1982) Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987-1006.

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Engle, R. F. (1982), Autoregressive Conditional Heteroskedasticity With Esti- mates of Variance of U. K. Inflation, Econometrica, 50, 987-1008.

No context found.

Engle, R.F., 1982. Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50, 987-1008.

No context found.

Engle, R. 1982. "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflations." Econometrica 50: 987--1008.

No context found.

ENGLE, R.F. (1982): \Autoregressive Conditional Heteroskedasticity with Estimated of the Variance of United Kingdom In ation", Econometrica, 50, 987-1006.

No context found.

Engle, R. (1982): "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation", Econometrica, 50, 987-1008.

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Engle, R. F. (1982): "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, 50, 987-1008.

No context found.

Engle, R. 1982. "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflations." Econometrica 50: 987--1008.

No context found.

Engle, Robert F. (1982), "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. inflation", Econometrica, 50, pp. 987-1007.

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Engle, Robert, 1982, Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Ination, Econometrica 50, 9871007.

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Engle, Robert, 1982, "Autoregressive Conditional Heteroskedasticity," with Estimates of the Variance of U. K. Inflation," Econometrica 50, 987-1008.

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