Model and analyze financial and economic systems using statistical methods. Econometrics Toolbox provides functions for modeling and analyzing time series data. It offers a wide range of diagnostic tests for model selection, including tests for impulse analysis, unit roots and stationarity, cointegration, and structural change. You can estimate, simulate, and forecast economic systems using a variety of models, including, regression, ARIMA, state space, GARCH, multivariate VAR and VEC, and switching models representing dynamic shifts in data. The toolbox also provides Bayesian and Markov-based tools for developing time-varying models that learn from new data.
Author(s): The MathWorks, Inc.
Edition: R2020a
Publisher: The MathWorks, Inc.
Year: 2020
Language: English
Pages: 3156
City: Natick, MA
Tags: Matlab, Econometrics, Toolbox, MathWorks
Getting Started
Econometrics Toolbox Product Description
Econometric Modeling
Model Selection
Econometrics Toolbox Features
Econometrics Toolbox Model Objects, Properties, and Object Functions
Model Objects
Model Properties
Specify Models
Retrieve Model Properties
Modify Model Properties
Object Functions
Stochastic Process Characteristics
What Is a Stochastic Process?
Stationary Processes
Linear Time Series Model
Unit Root Process
Lag Operator Notation
Characteristic Equation
Bibliography
Data Preprocessing
Data Transformations
Why Transform?
Common Data Transformations
Trend-Stationary vs. Difference-Stationary Processes
Nonstationary Processes
Trend Stationary
Difference Stationary
Specify Lag Operator Polynomials
Lag Operator Polynomial of Coefficients
Difference Lag Operator Polynomials
Nonseasonal Differencing
Nonseasonal and Seasonal Differencing
Time Series Decomposition
Moving Average Filter
Moving Average Trend Estimation
Parametric Trend Estimation
Hodrick-Prescott Filter
Using the Hodrick-Prescott Filter to Reproduce Their Original Result
Seasonal Filters
What Is a Seasonal Filter?
Stable Seasonal Filter
Sn × m seasonal filter
Seasonal Adjustment
What Is Seasonal Adjustment?
Deseasonalized Series
Seasonal Adjustment Process
Seasonal Adjustment Using a Stable Seasonal Filter
Seasonal Adjustment Using S(n,m) Seasonal Filters
Model Selection
Box-Jenkins Methodology
Box-Jenkins Model Selection
Autocorrelation and Partial Autocorrelation
What Are Autocorrelation and Partial Autocorrelation?
Theoretical ACF and PACF
Sample ACF and PACF
Ljung-Box Q-Test
Detect Autocorrelation
Compute Sample ACF and PACF
Conduct the Ljung-Box Q-Test
Engle’s ARCH Test
Detect ARCH Effects
Test Autocorrelation of Squared Residuals
Conduct Engle's ARCH Test
Unit Root Nonstationarity
What Is a Unit Root Test?
Modeling Unit Root Processes
Available Tests
Testing for Unit Roots
Unit Root Tests
Test Simulated Data for a Unit Root
Test Time Series Data for Unit Root
Test Stock Data for a Random Walk
Assess Stationarity of a Time Series
Information Criteria
Model Comparison Tests
Available Tests
Likelihood Ratio Test
Lagrange Multiplier Test
Wald Test
Covariance Matrix Estimation
Conduct Lagrange Multiplier Test
Conduct Wald Test
Compare GARCH Models Using Likelihood Ratio Test
Check Fit of Multiplicative ARIMA Model
Goodness of Fit
Residual Diagnostics
Check Residuals for Normality
Check Residuals for Autocorrelation
Check Residuals for Conditional Heteroscedasticity
Assess Predictive Performance
Nonspherical Models
What Are Nonspherical Models?
Plot a Confidence Band Using HAC Estimates
Change the Bandwidth of a HAC Estimator
Check Model Assumptions for Chow Test
Power of the Chow Test
Econometric Modeler
Econometric Modeler App Overview
Prepare Data for Econometric Modeler App
Import Time Series Variables
Perform Exploratory Data Analysis
Fitting Models to Data
Conducting Goodness-of-Fit Checks
Finding Model with Best In-Sample Fit
Export Session Results
Specifying Lag Operator Polynomials Interactively
Specify Lag Structure Using Lag Order Tab
Specify Lag Structure Using Lag Vector Tab
Prepare Time Series Data for Econometric Modeler App
Prepare Table of Multivariate Data for Import
Prepare Numeric Vector for Import
Import Time Series Data into Econometric Modeler App
Import Data from MATLAB Workspace
Import Data from MAT-File
Plot Time Series Data Using Econometric Modeler App
Plot Univariate Time Series Data
Plot Multivariate Time Series and Correlations
Detect Serial Correlation Using Econometric Modeler App
Plot ACF and PACF
Conduct Ljung-Box Q-Test for Significant Autocorrelation
Detect ARCH Effects Using Econometric Modeler App
Inspect Correlograms of Squared Residuals for ARCH Effects
Conduct Ljung-Box Q-Test on Squared Residuals
Conduct Engle's ARCH Test
Assess Stationarity of Time Series Using Econometric Modeler
Test Assuming Unit Root Null Model
Test Assuming Stationary Null Model
Test Assuming Random Walk Null Model
Assess Collinearity Among Multiple Series Using Econometric Modeler App
Transform Time Series Using Econometric Modeler App
Apply Log Transformation to Data
Stabilize Time Series Using Nonseasonal Differencing
Convert Prices to Returns
Remove Seasonal Trend from Time Series Using Seasonal Difference
Remove Deterministic Trend from Time Series
Implement Box-Jenkins Model Selection and Estimation Using Econometric Modeler App
Select ARCH Lags for GARCH Model Using Econometric Modeler App
Estimate Multiplicative ARIMA Model Using Econometric Modeler App
Perform ARIMA Model Residual Diagnostics Using Econometric Modeler App
Specify t Innovation Distribution Using Econometric Modeler App
Compare Predictive Performance After Creating Models Using Econometric Modeler App
Estimate ARIMAX Model Using Econometric Modeler App
Estimate Regression Model with ARMA Errors Using Econometric Modeler App
Compare Conditional Variance Model Fit Statistics Using Econometric Modeler App
Perform GARCH Model Residual Diagnostics Using Econometric Modeler App
Share Results of Econometric Modeler App Session
Time Series Regression Models
Time Series Regression Models
Regression Models with Time Series Errors
What Are Regression Models with Time Series Errors?
Conventions
Create Regression Models with ARIMA Errors
Default Regression Model with ARIMA Errors Specifications
Specify regARIMA Models Using Name-Value Pair Arguments
Specify Linear Regression Models Using Econometric Modeler App
Specify the Default Regression Model with ARIMA Errors
Modify regARIMA Model Properties
Modify Properties Using Dot Notation
Nonmodifiable Properties
Create Regression Models with AR Errors
Default Regression Model with AR Errors
AR Error Model Without an Intercept
AR Error Model with Nonconsecutive Lags
Known Parameter Values for a Regression Model with AR Errors
Regression Model with AR Errors and t Innovations
Create Regression Models with MA Errors
Default Regression Model with MA Errors
MA Error Model Without an Intercept
MA Error Model with Nonconsecutive Lags
Known Parameter Values for a Regression Model with MA Errors
Regression Model with MA Errors and t Innovations
Create Regression Models with ARMA Errors
Default Regression Model with ARMA Errors
ARMA Error Model Without an Intercept
ARMA Error Model with Nonconsecutive Lags
Known Parameter Values for a Regression Model with ARMA Errors
Regression Model with ARMA Errors and t Innovations
Specify Regression Model with ARMA Errors Using Econometric Modeler App
Create Regression Models with ARIMA Errors
Default Regression Model with ARIMA Errors
ARIMA Error Model Without an Intercept
ARIMA Error Model with Nonconsecutive Lags
Known Parameter Values for a Regression Model with ARIMA Errors
Regression Model with ARIMA Errors and t Innovations
Create Regression Models with SARIMA Errors
SARMA Error Model Without an Intercept
Known Parameter Values for a Regression Model with SARIMA Errors
Regression Model with SARIMA Errors and t Innovations
Specify Regression Model with SARIMA Errors
Specify ARIMA Error Model Innovation Distribution
About the Innovation Process
Innovation Distribution Options
Specify Innovation Distribution
Impulse Response of Regression Models with ARIMA Errors
Plot Impulse Response of Regression Model with ARIMA Errors
Regression Model with AR Errors
Regression Model with MA Errors
Regression Model with ARMA Errors
Regression Model with ARIMA Errors
Maximum Likelihood Estimation of regARIMA Models
Innovation Distribution
Loglikelihood Functions
regARIMA Model Estimation Using Equality Constraints
Presample Values for regARIMA Model Estimation
Initial Values for regARIMA Model Estimation
Optimization Settings for regARIMA Model Estimation
Optimization Options
Constraints on Regression Models with ARIMA Errors
Estimate a Regression Model with ARIMA Errors
Estimate a Regression Model with Multiplicative ARIMA Errors
Select Regression Model with ARIMA Errors
Choose Lags for ARMA Error Model
Intercept Identifiability in Regression Models with ARIMA Errors
Intercept Identifiability
Intercept Identifiability Illustration
Alternative ARIMA Model Representations
regARIMA to ARIMAX Model Conversion
Illustrate regARIMA to ARIMAX Model Conversion
Simulate Regression Models with ARMA Errors
Simulate an AR Error Model
Simulate an MA Error Model
Simulate an ARMA Error Model
Simulate Regression Models with Nonstationary Errors
Simulate a Regression Model with Nonstationary Errors
Simulate a Regression Model with Nonstationary Exponential Errors
Simulate Regression Models with Multiplicative Seasonal Errors
Simulate a Regression Model with Stationary Multiplicative Seasonal Errors
Untitled
Monte Carlo Simulation of Regression Models with ARIMA Errors
What Is Monte Carlo Simulation?
Generate Monte Carlo Sample Paths
Monte Carlo Error
Presample Data for regARIMA Model Simulation
Transient Effects in regARIMA Model Simulations
What Are Transient Effects?
Illustration of Transient Effects on Regression
Forecast a Regression Model with ARIMA Errors
Forecast a Regression Model with Multiplicative Seasonal ARIMA Errors
Verify Predictive Ability Robustness of a regARIMA Model
MMSE Forecasting Regression Models with ARIMA Errors
What Are MMSE Forecasts?
How forecast Generates MMSE Forecasts
Forecast Error
Monte Carlo Forecasting of regARIMA Models
Monte Carlo Forecasts
Advantage of Monte Carlo Forecasts
Bayesian Linear Regression
Bayesian Linear Regression
Classical Versus Bayesian Analyses
Main Bayesian Analysis Components
Posterior Estimation and Inference
Implement Bayesian Linear Regression
Workflow for Standard Bayesian Linear Regression Models
Workflow for Bayesian Predictor Selection
Specify Gradient for HMC Sampler
Posterior Estimation and Simulation Diagnostics
Diagnose MCMC Samples
Perform Sensitivity Analysis
Tune Slice Sampler For Posterior Estimation
Compare Robust Regression Techniques
Bayesian Lasso Regression
Bayesian Stochastic Search Variable Selection
Replacing Removed Syntaxes of estimate
Replace Removed Syntax When Estimating Analytical Marginal Posterior
Replace Removed Syntax When Estimating Numerical Marginal Posterior
Replace Removed Syntax When Estimating Conditional Posterior
Conditional Mean Models
Conditional Mean Models
Unconditional vs. Conditional Mean
Static vs. Dynamic Conditional Mean Models
Conditional Mean Models for Stationary Processes
Specify Conditional Mean Models
Default ARIMA Model
Specify Nonseasonal Models Using Name-Value Pairs
Specify Multiplicative Models Using Name-Value Pairs
Specify Conditional Mean Model Using Econometric Modeler App
Autoregressive Model
AR(p) Model
Stationarity of the AR Model
AR Model Specifications
Default AR Model
AR Model with No Constant Term
AR Model with Nonconsecutive Lags
ARMA Model with Known Parameter Values
AR Model with t Innovation Distribution
Specify AR Model Using Econometric Modeler App
Moving Average Model
MA(q) Model
Invertibility of the MA Model
MA Model Specifications
Default MA Model
MA Model with No Constant Term
MA Model with Nonconsecutive Lags
MA Model with Known Parameter Values
MA Model with t Innovation Distribution
Specify MA Model Using Econometric Modeler App
Autoregressive Moving Average Model
ARMA(p,q) Model
Stationarity and Invertibility of the ARMA Model
ARMA Model Specifications
Default ARMA Model
ARMA Model with No Constant Term
ARMA Model with Known Parameter Values
Specify ARMA Model Using Econometric Modeler App
ARIMA Model
ARIMA Model Specifications
Default ARIMA Model
ARIMA Model with Known Parameter Values
Specify ARIMA Model Using Econometric Modeler App
Multiplicative ARIMA Model
Multiplicative ARIMA Model Specifications
Seasonal ARIMA Model with No Constant Term
Seasonal ARIMA Model with Known Parameter Values
Specify Multiplicative ARIMA Model Using Econometric Modeler App
Specify Multiplicative ARIMA Model
ARIMA Model Including Exogenous Covariates
ARIMAX(p,D,q) Model
Conventions and Extensions of the ARIMAX Model
ARIMAX Model Specifications
Create ARIMAX Model Using Name-Value Pairs
Specify ARMAX Model Using Dot Notation
Specify ARIMAX or SARIMAX Model Using Econometric Modeler App
Modify Properties of Conditional Mean Model Objects
Dot Notation
Nonmodifiable Properties
Specify Conditional Mean Model Innovation Distribution
About the Innovation Process
Choices for the Variance Model
Choices for the Innovation Distribution
Specify the Innovation Distribution
Modify the Innovation Distribution
Specify Conditional Mean and Variance Models
Impulse Response Function
Plot the Impulse Response Function
Moving Average Model
Autoregressive Model
ARMA Model
Box-Jenkins Differencing vs. ARIMA Estimation
Maximum Likelihood Estimation for Conditional Mean Models
Innovation Distribution
Loglikelihood Functions
Conditional Mean Model Estimation with Equality Constraints
Presample Data for Conditional Mean Model Estimation
Initial Values for Conditional Mean Model Estimation
Optimization Settings for Conditional Mean Model Estimation
Optimization Options
Conditional Mean Model Constraints
Estimate Multiplicative ARIMA Model
Model Seasonal Lag Effects Using Indicator Variables
Forecast IGD Rate from ARX Model
Estimate Conditional Mean and Variance Model
Choose ARMA Lags Using BIC
Infer Residuals for Diagnostic Checking
Monte Carlo Simulation of Conditional Mean Models
What Is Monte Carlo Simulation?
Generate Monte Carlo Sample Paths
Monte Carlo Error
Presample Data for Conditional Mean Model Simulation
Transient Effects in Conditional Mean Model Simulations
Simulate Stationary Processes
Simulate AR Process
Simulate MA Process
Simulate Trend-Stationary and Difference-Stationary Processes
Simulate Multiplicative ARIMA Models
Simulate Conditional Mean and Variance Models
Monte Carlo Forecasting of Conditional Mean Models
Monte Carlo Forecasts
Advantage of Monte Carlo Forecasting
MMSE Forecasting of Conditional Mean Models
What Are MMSE Forecasts?
How forecast Generates MMSE Forecasts
Forecast Error
Convergence of AR Forecasts
Forecast Multiplicative ARIMA Model
Specify Presample and Forecast Period Data To Forecast ARIMAX Model
Forecast Conditional Mean and Variance Model
Model and Simulate Electricity Spot Prices Using the Skew-Normal Distribution
Conditional Variance Models
Conditional Variance Models
General Conditional Variance Model Definition
GARCH Model
EGARCH Model
GJR Model
Specify GARCH Models
Default GARCH Model
Specify Default GARCH Model
Using Name-Value Pair Arguments
Specify GARCH Model Using Econometric Modeler App
Specify GARCH Model with Mean Offset
Specify GARCH Model with Known Parameter Values
Specify GARCH Model with t Innovation Distribution
Specify GARCH Model with Nonconsecutive Lags
Specify EGARCH Models
Default EGARCH Model
Specify Default EGARCH Model
Using Name-Value Pair Arguments
Specify EGARCH Model Using Econometric Modeler App
Specify EGARCH Model with Mean Offset
Specify EGARCH Model with Nonconsecutive Lags
Specify EGARCH Model with Known Parameter Values
Specify EGARCH Model with t Innovation Distribution
Specify GJR Models
Default GJR Model
Specify Default GJR Model
Using Name-Value Pair Arguments
Specify GJR Model Using Econometric Modeler App
Specify GJR Model with Mean Offset
Specify GJR Model with Nonconsecutive Lags
Specify GJR Model with Known Parameter Values
Specify GJR Model with t Innovation Distribution
Modify Properties of Conditional Variance Models
Dot Notation
Nonmodifiable Properties
Specify the Conditional Variance Model Innovation Distribution
Specify Conditional Variance Model For Exchange Rates
Maximum Likelihood Estimation for Conditional Variance Models
Innovation Distribution
Loglikelihood Functions
Conditional Variance Model Estimation with Equality Constraints
Presample Data for Conditional Variance Model Estimation
Initial Values for Conditional Variance Model Estimation
Optimization Settings for Conditional Variance Model Estimation
Optimization Options
Conditional Variance Model Constraints
Infer Conditional Variances and Residuals
Likelihood Ratio Test for Conditional Variance Models
Compare Conditional Variance Models Using Information Criteria
Monte Carlo Simulation of Conditional Variance Models
What Is Monte Carlo Simulation?
Generate Monte Carlo Sample Paths
Monte Carlo Error
Presample Data for Conditional Variance Model Simulation
Simulate GARCH Models
Assess EGARCH Forecast Bias Using Simulations
Simulate Conditional Variance Model
Monte Carlo Forecasting of Conditional Variance Models
Monte Carlo Forecasts
Advantage of Monte Carlo Forecasting
MMSE Forecasting of Conditional Variance Models
What Are MMSE Forecasts?
EGARCH MMSE Forecasts
How forecast Generates MMSE Forecasts
Forecast GJR Models
Forecast a Conditional Variance Model
Converting from GARCH Functions to Model Objects
Multivariate Time Series Models
Vector Autoregression (VAR) Models
Types of Stationary Multivariate Time Series Models
Lag Operator Representation
Stable and Invertible Models
Models with Regression Component
VAR Model Workflow
Multivariate Time Series Data Formats
Multivariate Time Series Data
Load Multivariate Economic Data
Multivariate Data Format
Preprocess Data
Time Base Partitions for Estimation
Partition Multivariate Time Series Data for Estimation
Vector Autoregression (VAR) Model Creation
Create VAR Model
Fully Specified Model Object
Model Template for Unrestricted Estimation
Partially Specified Model Object for Restricted Estimation
Display and Change Model Objects
Select Appropriate Lag Order
Create and Adjust VAR Model Using Shorthand Syntax
Create and Adjust VAR Model Using Longhand Syntax
VAR Model Estimation
Preparing VAR Models for Fitting
Fitting Models to Data
Examining the Stability of a Fitted Model
Convert VARMA Model to VAR Model
Fit VAR Model of CPI and Unemployment Rate
Fit VAR Model to Simulated Data
VAR Model Forecasting, Simulation, and Analysis
VAR Model Forecasting
Data Scaling
Calculating Impulse Responses
Generate VAR Model Impulse Responses
Compare Generalized and Orthogonalized Impulse Response Functions
Forecast VAR Model
Forecast VAR Model Using Monte Carlo Simulation
Forecast VAR Model Conditional Responses
Implement Seemingly Unrelated Regression
Estimate Capital Asset Pricing Model Using SUR
Simulate Responses of Estimated VARX Model
Simulate VAR Model Conditional Responses
Simulate Responses Using filter
VAR Model Case Study
Convert from vgx Functions to Model Objects
Cointegration and Error Correction Analysis
Integration and Cointegration
Cointegration and Error Correction
The Role of Deterministic Terms
Cointegration Modeling
Determine Cointegration Rank of VEC Model
Identifying Single Cointegrating Relations
The Engle-Granger Test for Cointegration
Limitations of the Engle-Granger Test
Test for Cointegration Using the Engle-Granger Test
Estimate VEC Model Parameters Using egcitest
VEC Model Monte Carlo Forecasts
Generate VEC Model Impulse Responses
Identifying Multiple Cointegrating Relations
Test for Cointegration Using the Johansen Test
Estimate VEC Model Parameters Using jcitest
Compare Approaches to Cointegration Analysis
Testing Cointegrating Vectors and Adjustment Speeds
Test Cointegrating Vectors
Test Adjustment Speeds
Structural Change Models
Discrete-Time Markov Chains
What Are Discrete-Time Markov Chains?
Discrete-Time Markov Chain Theory
Markov Chain Modeling
Discrete-Time Markov Chain Object Framework Overview
Markov Chain Analysis Workflow
Create and Modify Markov Chain Model Objects
Create Markov Chain from Stochastic Transition Matrix
Create Markov Chain from Random Transition Matrix
Specify Structure for Random Markov Chain
Work with State Transitions
Visualize Markov Chain Structure and Evolution
Determine Asymptotic Behavior of Markov Chain
Identify Classes in Markov Chain
Compare Markov Chain Mixing Times
Simulate Random Walks Through Markov Chain
Compute State Distribution of Markov Chain at Each Time Step
State-Space Models
What Are State-Space Models?
Definitions
State-Space Model Creation
What Is the Kalman Filter?
Standard Kalman Filter
State Forecasts
Filtered States
Smoothed States
Smoothed State Disturbances
Forecasted Observations
Smoothed Observation Innovations
Kalman Gain
Backward Recursion of the Kalman Filter
Diffuse Kalman Filter
Explicitly Create State-Space Model Containing Known Parameter Values
Create State-Space Model with Unknown Parameters
Explicitly Create State-Space Model Containing Unknown Parameters
Implicitly Create Time-Invariant State-Space Model
Create State-Space Model Containing ARMA State
Implicitly Create State-Space Model Containing Regression Component
Implicitly Create Diffuse State-Space Model Containing Regression Component
Implicitly Create Time-Varying State-Space Model
Implicitly Create Time-Varying Diffuse State-Space Model
Create State-Space Model with Random State Coefficient
Estimate Time-Invariant State-Space Model
Estimate Time-Varying State-Space Model
Estimate Time-Varying Diffuse State-Space Model
Estimate State-Space Model Containing Regression Component
Filter States of State-Space Model
Filter Time-Varying State-Space Model
Filter Time-Varying Diffuse State-Space Model
Filter States of State-Space Model Containing Regression Component
Smooth States of State-Space Model
Smooth Time-Varying State-Space Model
Smooth Time-Varying Diffuse State-Space Model
Smooth States of State-Space Model Containing Regression Component
Simulate States and Observations of Time-Invariant State-Space Model
Simulate Time-Varying State-Space Model
Simulate States of Time-Varying State-Space Model Using Simulation Smoother
Estimate Random Parameter of State-Space Model
Forecast State-Space Model Using Monte-Carlo Methods
Forecast State-Space Model Observations
Forecast Observations of State-Space Model Containing Regression Component
Forecast Time-Varying State-Space Model
Forecast State-Space Model Containing Regime Change in the Forecast Horizon
Forecast Time-Varying Diffuse State-Space Model
Compare Simulation Smoother to Smoothed States
Rolling-Window Analysis of Time-Series Models
Rolling-Window Analysis for Parameter Stability
Rolling Window Analysis for Predictive Performance
Assess State-Space Model Stability Using Rolling Window Analysis
Assess Model Stability Using Rolling Window Analysis
Assess Stability of Implicitly Created State-Space Model
Choose State-Space Model Specification Using Backtesting
Functions
adftest
aicbic
archtest
arima
regARIMA.arima
arma2ar
arma2ma
armafevd
armairf
asymptotics
autocorr
bayeslm
bayesvarm
chowtest
classify
collintest
conjugateblm
conjugatebvarm
customblm
cusumtest
corrplot
crosscorr
diffuseblm
diffusebvarm
distplot
dssm.disp
ssm.disp
dssm
dtmc
Econometric Modeler
egarch
egcitest
eigplot
empiricalblm
empiricalbvarm
arima.estimate
estimate
estimate
estimate
estimate
dssm.estimate
estimate
regARIMA.estimate
ssm.estimate
estimate
estimate
fevd
fevd
fgls
arima.filter
filter
dssm.filter
LagOp.filter
filter
regARIMA.filter
ssm.filter
filter
filter
arima.forecast
forecast
forecast
forecast
dssm.forecast
forecast
regARIMA.forecast
ssm.forecast
forecast
forecast
garch
gctest
gctest
gjr
graphplot
hac
hitprob
hittime
hpfilter
i10test
arima.impulse
regARIMA.impulse
infer
arima.infer
regARIMA.infer
infer
infer
irf
irf
LagOp.isEqLagOp
isergodic
LagOp.isNonZero
isreducible
LagOp.isStable
jcitest
jcontest
kpsstest
lagmatrix
LagOp
lassoblm
lazy
lbqtest
lmctest
lmtest
lratiotest
mcmix
LagOp.minus
mixconjugateblm
mixsemiconjugateblm
LagOp.mldivide
LagOp.mrdivide
msVAR
LagOp.mtimes
normalbvarm
parcorr
plot
LagOp.plus
pptest
price2ret
print
arima.print
regARIMA.print
recessionplot
recreg
redistribute
dssm.refine
ssm.refine
regARIMA
LagOp.reflect
ret2price
sampleroptions
semiconjugateblm
semiconjugatebvarm
simplot
ssm.simsmooth
simsmooth
arima.simulate
simulate
simulate
simulate
simulate
simulate
regARIMA.simulate
ssm.simulate
simulate
simulate
dssm.smooth
smooth
ssm.smooth
ssm
subchain
arima.summarize
summarize
summarize
regARIMA.summarize
summarize
summarize
summarize
summarize
LagOp.toCellArray
var2vec
varm
varm
vec2var
vecm
vecm
vratiotest
waldtest
Appendices
Data Sets and Examples
Glossary
