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# Econometrics Toolbox Release Notes

## R2014b

New Features, Compatibility Considerations

#### Simulation smoothing for state-space models​

The ssm model object has the method simsmooth for sampling from the posterior distribution of the states using forward filtering, backward sampling.

#### Feasible generalized least squares (FGLS) estimators

The fgls function uses generalized least squares (GLS) to estimate coefficients and standard errors in multiple linear regression models with nonspherical errors by first estimating the covariance of the innovations process.

#### Time-series regression example​

The example, following a series of time series regression examples, illustrates how to estimate multiple linear regression models of time series data in the presence of heteroscedastic or autocorrelated (nonspherical) innovations: Time Series Regression X: Generalized Least Squares and HAC Estimators.

#### Shipped data sets now support tabular arrays

Econometrics Toolbox™ data sets organize data in tabular arrays rather than dataset arrays.

#### Compatibility Considerations

To access or modify a tabular array, you must use table indexing and functions. For details, see Tables.

#### Functions now support tabular arrays

hac, i10test, corrplot, and collintest accept tabular arrays as input arguments. jcitest returns tabular arrays.

#### Compatibility Considerations

To access or modify the output tables of jcitest, you must use table indexing and functions. For details, see Tables.

## R2014a

New Features, Compatibility Considerations

#### Time-invariant and time-varying, linear, Gaussian state-space models

Econometrics Toolbox has a model for performing univariate and multivariate time-series data analysis.

• The ssm model supports time-invariant and time-varying, linear state-space models.

• Specify a state-space model using ssm, and then:

• Estimate its parameters using estimate.

• Implement forward recursion of the state-space model using filter.

• Implement backward recursion of the state-space model using smooth.

• Simulate states and observations using simulate.

• Forecast states and observations using forecast.

#### Kalman filter with missing data

The methods of the state-space model, ssm, use the Kalman filter to estimate the states, and also use this framework to manage missing data.

#### Performance enhancements for ARIMA and GARCH models

The estimate methods of the arima, egarch, garch, gjr, and regARIMA models have been enhanced to converge more quickly, and, therefore, you might experience faster estimation durations.

#### SDE functions moved from Econometrics Toolbox to Financial Toolbox

The following stochastic differential equation (SDE) functions have moved from Econometrics Toolbox to Financial Toolbox™:

#### Data set and example functions moved from Econometrics Toolbox to Financial Toolbox

The following data set and example functions from the matlab/toolbox/econ/econdemos folder have moved to matlab/toolbox/finance/findemos:

• Example_BarrierOption

• Example_BlackScholes

• Example_CEVModel

• Example_CIRModel

• Example_CopulaRNG

• Example_LongstaffSchwartz

• Example_StratifiedRNG

• Data_GlobalIdx2.mat

#### Functionality being removed

Function NameWhat Happens When You Use This Function?Use This Function InsteadCompatibility Considerations
garchcountErrorssum(any(EstParamCov)), where EstParamCov is an estimated parameter covariance matrix of a fitted arima, garch, egarch, or gjr modelN/A
garchdispErrorsprint method of the classes Replace all existing instances of garchdisp with the correct print syntax.
garchfitErrorsestimate method of the classes arima, garch, egarch, or gjrReplace all existing instances of garchfit with the correct estimate syntax.
garchgetErrorsarima, garch, egarch, or gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to retrieve parameter values from the model.
garchinferErrorsinfer method of the classes arima, garch, egarch, or gjrReplace all existing instances of garchinfer with the correct infer syntax.
garchplotErrorsN/AN/A
garchpredErrorsforecast method of the classes arima, garch, egarch, or gjrReplace all existing instances of garchpred with the correct forecast syntax.
garchsetErrorsarima, garch, egarch, or gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to set parameter values for the model.
garchsimErrorssimulate method of the classes arima, garch, egarch, or gjrReplace all existing instances of garchsim with the correct simulate syntax.

## R2013b

New Features, Compatibility Considerations

#### Regression models with ARIMA errors

Econometrics Toolbox has a new model for performing time series regression analysis.

• The regARIMA model supports linear regression models with ARIMA error processes, including AR, MA, ARMA, and seasonal error models.

• Specify a regression model with ARIMA errors using regARIMA, then

• Estimate its parameters using the data and estimate.

• Simulate responses using simulate.

• Forecast responses using forecast.

• Infer residuals using infer.

• Filter innovations using filter.

• Plot an impulse response using impulse.

• Convert it to an ARIMA model using arima.

#### Time series regression example for lag order selection

The example, following a series of time series regression examples, illustrates predictor history selection for multiple linear regression models: Time Series Regression IX: Lag Order Selection.

#### optimoptions support

optimoptions support when using solver optimization options to:

#### Compatibility Considerations

When estimating arima, garch, egarch, or gjr models using estimate, the default solver options now reference an optimoptions object, instead of an optimset structure. If you now use default solver options and operate on them assuming this is an optimset structure, some operations might not work.

optimoptions is the default and recommended method to set solver options, though optimset is also supported.

#### Estimation display options

The options for the Command Window display of arima/estimate, garch/estimate, egarch/estimate, and gjr/estimate is simplified and enhanced. You can easily:

• Display only the maximum likelihood parameter estimates, standard errors, and t statistics. This is the new default.

• Display only iterative optimization information.

• Display only optimization diagnostics.

• Display all of the above.

• Turn off all output.

#### Compatibility Considerations

The new, recommended name-value pair argument that controls the display is Display. However, the software still supports the previous name-value pair argument, print.

#### Functionality being removed

Function NameWhat Happens When You Use This Function?Use This Function InsteadCompatibility Considerations
garchcountWarnsUse sum(any(EstParamCov)), where EstParamCov is an estimated parameter covariance matrix of a fitted arima, garch, egarch, or gjr model.N/A
garchdispWarnsprint method of the classes Replace all existing instances of garchdisp with the correct print syntax.
garchfitWarnsestimate method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchfit with the correct estimate syntax.
garchgetWarnsarima, garch, egarch, and gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to retrieve parameter values from the model.
garchinferWarnsinfer method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchinfer with the correct infer syntax.
garchplotWarnsN/AN/A
garchpredWarnsforecast method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchpred with the correct forecast syntax.
garchsetWarnsarima, garch, egarch, and gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to set parameter values for the model.
garchsimWarnssimulate method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchsim with the correct simulate syntax.

## R2013a

New Features, Compatibility Considerations

#### Heteroscedasticity and autocorrelation consistent (HAC) covariance estimators

The new hac function estimates robust covariances for ordinary least squares coefficients of multiple linear regression models under general forms of heteroscedasticity and autocorrelation.

#### Regression component added to ARIMA models

You can include a regression component to an arima model to measure the linear effects that exogenous covariate series have on a response series. This new functionality also enhances estimate, filter, forecast, infer, and simulate.

#### Compatibility Considerations

This new arima functionality replaces garchfit, garchdisp, garchinfer, garchget, garchset, garchpred, and garchsim. Change all instances of those functions using the new arima syntax.

#### Changes to lmctest

lmctest uses estimate rather than garchfit to calculate the MLEs under the alternative hypothesis.

#### Compatibility Considerations

You might receive slightly different estimates and, in some cases, p-values for the same data under the previous functionality of lmctest.

#### Functionality being removed

Function NameWhat Happens When You Use This Function?Use This Function InsteadCompatibility Considerations
garchcountWarnsUse sum(any(EstParamCov)), where EstParamCov is an estimated parameter covariance matrix of a fitted arima, garch, egarch, or gjr model.N/A
garchdispWarnsprint method of the classes Replace all existing instances of garchdisp with the correct print syntax.
garchfitWarnsestimate method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchfit with the correct estimate syntax.
garchgetWarnsarima, garch, egarch, and gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to retrieve parameter values from the model.
garchinferWarnsinfer method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchinfer with the correct infer syntax.
garchplotWarnsN/AN/A
garchpredWarnsforecast method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchpred with the correct forecast syntax.
garchsetWarnsarima, garch, egarch, and gjrSpecify a model using the appropriate model creator arima, garch, egarch, or gjr. Use Dot Notation to set parameter values for the model.
garchsimWarnssimulate method of the classes arima, garch, egarch, and gjrReplace all existing instances of garchsim with the correct simulate syntax.

## R2012b

New Features

#### Impulse response (dynamic multipliers) for ARIMA models

The arima model object has a new impulse method for generating and plotting impulse response functions for ARIMA models.

#### Filter user-specified disturbances through ARIMA and conditional variance models

There are new methods to filter user-specified disturbances through ARIMA and conditional variance models:

• filter for arima model objects to filter disturbances through an ARIMA process.

• filter for garch model objects to filter disturbances through a GARCH process.

• filter for egarch model objects to filter disturbances through an EGARCH process.

• filter for gjr model objects to filter disturbances through a GJR process.

## R2012a

New Features

#### New Model Objects and Their Functions

Econometrics Toolbox has four new model objects for modeling univariate time series data.

• The arima model object supports ARIMA processes, including AR, MA, ARMA, and seasonal models.

• For modeling conditionally heteroscedastic series, there are new garch, egarch, and gjr model objects, supporting GARCH models and the EGARCH and GJR variants.

Five new functions for each model object simplify the modeling workflow: estimate, infer, forecast, print, and simulate.

#### New Utility Functions

Four new utility functions assist in time series analysis:

• corrplot plots predictor correlations.

• collintest performs Belsley collinearity diagnostics.

• i10test conducts paired integration and stationarity tests.

• recessionplot adds recession bands to time series plots.

#### Demo for Static Time Series Model Specification

A new demo, "Specifying Static Time Series Models," steps through the model specification workflow for static multiple linear regression models.

Steps include:

• Detecting multicollinearity

• Identifying influential observations

• Testing for spurious regression due to integrated data

• Selecting predictor subsets using stepwise regression and lasso

• Conducting residual diagnostics

• Forecasting

The demo uses many tools from Econometrics Toolbox, and introduces new utility functions useful for model specification.

To run the demo in the Command Window, use the command showdemo Demo_StaticModels.

#### New Data Sets

Econometrics Toolbox includes two new data sets:

• Data_CreditDefaults. Historical data on investment-grade corporate bond defaults and four predictors, 1984–2004. Data are those used in: Loeffler, G., and P. N. Posch. Credit Risk Modeling Using Excel and VBA. West Sussex, England: Wiley Finance, 2007.

• Data_Recessions. U.S. recession start and end dates from 1857 to 2011. Source: National Bureau of Economic Research. "U.S. Business Cycle Expansions and Contractions." http://www.nber.org/cycles.html.

## R2011b

New Features, Compatibility Considerations

#### Warning and Error ID Changes

Many warning and error IDs have changed from their previous versions. These warnings or errors typically appear during a function call.

#### Compatibility Considerations

If you use warning or error IDs, you might need to change the strings you use. For example, if you turned off a warning for a certain ID, the warning might now appear under a different ID. If you use a try/catch statement in your code, replace the old identifier with the new identifier. There is no definitive list of the differences, or of the IDs that changed.

## R2011a

New Features

#### New Cointegration Functionality

Econometrics Toolbox now offers functions for cointegration testing and modeling. The egcitest function uses Engle-Granger methods to test for individual cointegrating relationships, and estimates their parameters. The jcitest function uses Johansen methods to test for multiple cointegrating relationships, and estimates parameters in corresponding vector error-correction models. The jcontest function tests linear restrictions on both error-correction speeds and the space of cointegrating vectors, and estimates restricted model parameters.

#### Convert Vector Autoregressive Models to and from Vector Error-Correction Models

The functions vectovar and vartovec allow you to convert between vector autoregressive (VAR) models and vector error-correction (VEC) models.

#### Data Sets for Calibrating Economic Models

Econometrics Toolbox includes three new data sets:

• Data_Canada. Mackinnon's data on inflation and interest rates in Canada, 1954–1994. Data are those used in: MacKinnon, J. G. "Numerical Distribution Functions for Unit Root and Cointegration Tests." Journal of Applied Econometrics. v. 11, 1996, pp. 601–618.

• Data_JDanish, Data_JAustralian. Johansen's data on money and income in Denmark, 1974–1987, and Australia/U.S. purchasing power and interest parity, 1972–1991. Data are those used in: Johansen, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford: Oxford University Press, 1995.

## R2010b

New Features, Compatibility Considerations

#### Functions Being Removed

Function NameWhat Happens When You Use This Function?Use This Function InsteadCompatibility Considerations
dfARDTestErroradftestThe new function syntax differs. Replace all existing instances of dfARDTest with the correct adftest syntax.
dfARTestErroradftestThe new function syntax differs. Replace all existing instances of dfARTest with the correct adftest syntax.
dfTSTestErroradftestThe new function syntax differs. Replace all existing instances of dfTSTest with the correct adftest syntax.
ppARDTestErrorpptestThe new function syntax differs. Replace all existing instances of ppARDTest with the correct pptest syntax.
ppARTestErrorpptestThe new function syntax differs. Replace all existing instances of ppARTest with the correct pptest syntax.
ppTSTestErrorpptestThe new function syntax differs. Replace all existing instances of ppTSTest with the correct pptest syntax.

#### Additional Syntax Options for archtest and lbqtest

The functions archtest and lbqtest now take name-value pair arguments as inputs. The old syntax of individual arguments will continue to work but will not be documented.

#### New Data Set for Calibrating Economic Models

The economic data from the paper by Nielsen and Risager, "Stock Returns and Bond Yields in Denmark, 1922–99," (Department of Economics, Copenhagen Business School; Working paper 3-2001, 2001) is now included with Econometrics Toolbox in the file Data_Danish.

## R2010a

New Features, Compatibility Considerations

#### Functions Being Removed

Function NameWhat Happens When You Use This Function?Use This Function InsteadCompatibility Considerations
dfARDTestErroradftestThe new function syntax differs. Replace all existing instances of dfARDTest with the correct adftest syntax.
dfARTestErroradftestThe new function syntax differs. Replace all existing instances of dfARTest with the correct adftest syntax.
dfTSTestErroradftestThe new function syntax differs. Replace all existing instances of dfTSTest with the correct adftest syntax.
ppARDTestErrorpptestThe new function syntax differs. Replace all existing instances of ppARDTest with the correct pptest syntax.
ppARTestErrorpptestThe new function syntax differs. Replace all existing instances of ppARTest with the correct pptest syntax.
ppTSTestErrorpptestThe new function syntax differs. Replace all existing instances of ppTSTest with the correct pptest syntax.

#### Demo Showing Multivariate Modeling of the U.S. Economy

A new demo, "Modeling the United States Economy," develops a small macroeconomic model. This model is used to examine the impact of various shocks on the United States economy, particularly around the period of the 2008 fiscal crisis. It uses the multiple time series tools from the Econometrics Toolbox.

To run the demo in the command window, use the command echodemo Demo_USEconModel.

#### Lag Operator Polynomial Objects

The new LagOp polynomial class provides methods to create and manipulate lag operator polynomials and filter time series data, as well as methods to perform polynomial algebra including addition, subtraction, multiplication, and division.

#### Leybourne-McCabe Test for Stationarity

The new Leybourne-McCabe test function lmctest assesses the null hypothesis that a univariate time series y is a trend-stationary AR(p) process against the alternative that y is a nonstationary ARIMA(p,1,1) process.

#### Historical Data Sets for Calibrating Economic Models

The new data set Data_SchwertMacro contains original data from G. William Schwert's article "Effects of Model Specification on Tests for Unit Roots in Macroeconomic Data," (Journal of Monetary Economics, Vol. 20, 1987, pp. 73–103.). These data are a benchmark for unit root tests. The new data set Data_SchwertStock contains indices of U.S. stock prices as published in G. William Schwert's article "Indexes of U.S. Stock Prices from 1802 to 1987," (The Journal of Business,Vol. 63, 1990, pp. 399–42.). The new data set Data_USEconModelcontains the macroeconomic series for the new demo Demo_USEconModel.

#### New Organization and Naming Standard for Data Sets

Econometrics Toolbox has a new set of naming conventions for data sets. Data set names are prefixed by Data_.

For full information on the available data sets, demos, and examples, see Data Sets, Demos, and Example Functions or type help econ/econdemos at the command line. For more information on Dataset Array objects, see dataset in the Statistics Toolbox™ documentation.

#### Compatibility Considerations

Replace any instances of load Old_Data with load and the new file name.

#### New Naming Convention for Demos and Example Functions

All demos and examples in the Econometrics Toolbox have been moved to the folder econ/econdemos and renamed according to the following convention:

• Demos are named Demo_DemoName

• Examples are named Example_ExampleName

#### Compatibility Considerations

Replace any instances of example functions with their new names. For full information on the available, demos, and examples, see Data Sets, Demos, and Example Functions or type help econ/econdemos at the command line.

## R2009b

New Features, Compatibility Considerations

#### Unit Root Tests

There are now four classes of unit root tests. More information on the tests is available in the Unit Root Nonstationarity section of the User's Guide.

### Dickey-Fuller and Phillips-Perron Tests

Dickey-Fuller and Phillips-Perron tests now have single interfaces, with new capabilities for multiple testing. Both adftest and pptest test a unit root null hypothesis against autoregressive, autoregressive with drift, or trend-stationary alternatives.

### KPSS Test

The new kpsstest function tests a null hypothesis of (trend) stationarity against nonstationary unit root alternatives.

### Variance Ratio Test

The new vratiotest function tests a null hypothesis of a random walk against alternatives with innovations that are not independent and identically distributed.

#### Compatibility Considerations

The ardtest function replaces the dfARDTest, dfARTest, and dfTSTest functions. The pptest function replaces the ppARDTest, ppARTest, and ppTSTest functions. The new function syntax differs from the functions they replace.

#### Financial Toolbox Required

Econometrics Toolbox requires Financial Toolbox™ as of this version.

#### Nelson-Plosser Data

The Nelson and Plosser [50] data set is now available. To access the data, enter load Data_NelsonPlosser at the MATLAB® command line.

## R2009a

New Features, Compatibility Considerations

#### Hypothesis Tests

There are two new hypothesis tests for model misspecification:

Furthermore, the likelihood ratio test, lratiotest, has been enhanced to be able to "test up" as well as "test down" when performing multiple model comparisons. It now accepts vectors of model parameters for restricted log likelihoods, for unrestricted log likelihoods, or for both.

There is a new demo about these tests; see New Demo.

#### Compatibility Considerations

lratiotest error messages and message IDs differ from previous versions.

#### Structural VAR, VARX, and VARMAX models

Econometrics Toolbox multiple time series functions now include structural multiple time series. Structural models have the general form

${A}_{0}{Y}_{t}=a+{X}_{t}b+\sum _{i=1}^{p}{A}_{i}{Y}_{t-i}+\sum _{j=1}^{q}{B}_{j}{W}_{t-j}+{B}_{0}{W}_{t}.$

Previously, Econometrics Toolbox multiple time series functions addressed models of the form

${Y}_{t}=a+{X}_{t}b+\sum _{i=1}^{p}{A}_{i}{Y}_{t-i}+\sum _{j=1}^{q}{B}_{i}{W}_{t-j}+{W}_{t}.$

The mathematical difference is the inclusion of A0 and B0 matrices. These matrices allow practitioners to specify structural dependencies between variables. For more information, see the Multivariate Time Series Models chapter of the Econometrics Toolbox User's Guide.

#### Compatibility Considerations

Objects created with the Econometrics Toolbox V1.0 vgxset function, and saved in MAT files, do not work with Econometrics Toolbox V1.1 functions. Recreate the objects with the Econometrics Toolbox V1.1 vgxset function.

#### New Demo

There is a new demo on hypothesis tests. Run the demo at the MATLAB command line by entering showdemo classicalTestsDemo.

## R2008b

New Features

#### Multivariate VAR, VARX, and VARMA Models

A new suite of functions, listed in the following table, adds support for multivariate VAR, VARX, and VARMA models.

FunctionDescription
vgxar

Convert VARMA specification into a pure vector autoregressive (VAR) model

vgxcount

Count restricted and unrestricted parameters in VAR or VARX models

vgxdisp

Display VGX model parameters and standard errors in different formats

vgxget

Get multivariate time-series specification parameters

vgxinfer

Infer innovations of a VGX process

vgxloglik

Compute conditional log-likelihoods of VGX process

vgxma

Convert VARMA specification into a pure vector moving average (VMA) model

vgxplot

Plot multivariate time series process

vgxpred

Generate transient response of VGX process during a specified forecast period

vgxproc

Generate a VGX process from an innovations process

vgxqual

Determine if a VGX process is stable and invertible

vgxset

Set or modify multivariate time-series specification parameters

vgxsim

Simulate VGX processes

vgxvarx

Solve VAR or VARX model using maximum likelihood estimation

#### Heston Stochastic Volatility Models

The new heston function adds support for Heston stochastic volatility models to the SDE engine.

## R2008a

New Features

#### Monte Carlo Simulation of Stochastic Differential Equations

The GARCH Toolbox™ software now allows you to model dependent financial and economic variables, such as interest rates and equity prices, via Monte Carlo simulation of multivariate diffusion processes. For more information, see Stochastic Differential Equations in the GARCH Toolbox documentation.

## R2007b

New Features

#### Changes to garchsim

The garchsim function previously allowed you to specify the State argument as either a scalar or a time series matrix of standardized, independent, identically distributed disturbances to drive the output Innovations in a time series process. The State argument must now be a time series matrix. See the State input argument on the garchsim reference page for more information.

## R2007a

No New Features or Changes

## R2006b

New Features

#### Data Preprocessing

A new Hodrick-Prescott filter, hpfilter, separates time series into trend and cyclical components

#### Demos

A new demo uses the hpfilter function to reproduce the results in Hodrick and Prescott's original paper on U.S. business cycles

## R2006a

New Features

#### User's Guide

A new chapter in the GARCH Toolbox User's Guide explains how to conduct Dickey-Fuller and Phillips-Perron unit root tests with the new statistical functions in the toolbox.

#### Statistical Functions

Version 2.2 of the GARCH Toolbox software has six new functions. All of them support the ability to conduct univariate unit root tests on time series data. Three functions support augmented Dickey-Fuller unit root tests. The remaining three support Phillips-Perron unit root tests.

### Dickey-Fuller Unit Root Tests

Function

Purpose

dfARDTest

Augmented Dickey-Fuller unit root test based on AR model with drift.

dfARTest

Augmented Dickey-Fuller unit root test based on zero drift AR model.

dfTSTest

Augmented Dickey-Fuller unit root test based on trend stationary AR model.

### Phillips-Perron Unit Root Tests

Function

Purpose

ppARDTest

Phillips-Perron unit root test based on AR(1) model with drift.

ppARTest

Phillips-Perron unit root test based on zero drift AR(1) model.

ppTSTest

Phillips-Perron unit root test based on trend stationary AR(1) model.

## R14SP3

New Features, Compatibility Considerations

#### Changes to garchsim

A change introduced in V2.1 of the GARCH Toolbox software concerns user-specified noise processes. The garchsim function now allows you to provide a time series matrix of standardized, i.i.d. disturbances to drive the output Innovations in a time series process. In previous versions, you could only provide a state that was used to generate a random noise process. See the State input argument on the garchsim reference page for more information.

#### Compatibility Considerations

garchsim argument Is renamed. In V2.1, the garchsim argument Seed is renamed to State for consistency with the MATLAB rand and randn functions. The name change, in itself, introduces no backward incompatibilities. The following topic explains a related change.

garchsim defaults to current random number generator state. In V2.0.1 of the GARCH Toolbox software, the garchsim function used the initial random number generator state, 0, if you did not specify a value for the Seed argument. The Seed argument corresponded to the rand and randn state value.

In V2.1, if you do not specify a value for the State (formerly Seed) argument, garchsim uses the current state of rand and randn, rather than the initial state. Use the commands s = rand('state') and s = randn('state') to determine the current state of these random number generators. For more information, see the rand and randn reference pages.

## Compatibility Summary

ReleaseFeatures or Changes with Compatibility Considerations
R2014b
R2014a
R2013b
R2013a
R2012bNone
R2012aNone
R2011bWarning and Error ID Changes
R2011aNone
R2010bFunctions Being Removed
R2010a
R2009bUnit Root Tests
R2009a
R2008bNone
R2008aNone
R2007bNone
R2007aNone
R2006bNone
R2006aNone
R14SP3Changes to garchsim