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V = infer(model,Y)
[V,LogL]
= infer(model,Y)
[V,LogL]
= infer(model,Y,Name,Value)
V = infer(model,Y) infers the conditional variances of a univariate EGARCH model specified in the model object fit to data Y.
[V,LogL] = infer(model,Y) additionally returns the loglikelihood objective function values.
[V,LogL] = infer(model,Y,Name,Value) infers the EGARCH model conditional variances with additional options specified by one or more Name,Value pair arguments.
model 
egarch model object, as created by egarch or estimate. The input model object cannot have any NaN values. 
Y 
numObsbynumPaths matrix of response data whose conditional variances are inferred. Y usually represents an innovation series with mean zero and variances characterized by the EGARCH model specified in model. In this case, Y is a continuation of the residual series E0. Y might also represent a time series of innovations with mean zero plus an offset. The inclusion of an offset is signaled by a nonzero Offset property in model. If Y is a column vector, it represents a single path of the underlying series. If Y is a matrix, Y represents numObs observations of numPaths paths of an underlying time series. infer assumes that observations across any row occur simultaneously. The last observation of any series is the most recent. 
Specify optional commaseparated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.
Notes

[1] Bollerslev, T. "Generalized Autoregressive Conditional Heteroskedasticity." Journal of Econometrics. Vol. 31, 1986, pp. 307–327.
[2] Bollerslev, T. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return." The Review of Economics and Statistics. Vol. 69, 1987, pp. 542–547.
[3] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series Analysis: Forecasting and Control. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.
[4] Enders, W. Applied Econometric Time Series. Hoboken, NJ: John Wiley & Sons, 1995.
[5] Engle, R. F. "Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation." Econometrica. Vol. 50, 1982, pp. 987–1007.
[6] Glosten, L. R., R. Jagannathan, and D. E. Runkle. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks." The Journal of Finance. Vol. 48, No. 5, 1993, pp. 1779–1801.
[7] Hamilton, J. D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994.
[8] Nelson, D. B. "Conditional Heteroskedasticity in Asset Returns: A New Approach." Econometrica. Vol. 59, 1991, pp. 347–370.