A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

- ! -

!-step ahead forecast

- A -

Access data in files

Adaptive learning

Belief parameters prior file

Belief system file

DSGE model solution eigenvalues

Import prior data

Monte Carlo filtering

Naive belief system

No smoothed states for belief Kalman filter

Perceived law of motion

PLM

Posterior mode estimation

Posterior sampling

Prior sampling

Save inverse Hessian

Select model solver

Set forward looking variables

Use fixed beliefs for projections

Adjust prediction paths

AiM

AiM model file

Location

View

AiM parser

Anderson-Moore algorithm

Annualization

- B -

Base color

Base output directory

Bayesian VAR

Conditional predictive distributions

Cross-equation tightness

Diffuse covariance matrix prior

Diffuse prior for parameters on lags

Endogenous variables

Exogenous variables

Forecasting data

Gibbs sampling

Harmonic lag decay

Inverted Wishart distribution

Lag length

Lag order selection

Marginal likelihood estimation

Minnesota prior

Modesty statistics

Normal conditional on covariance matrix

Overall tightness

Posterior density

Posterior mode

Posterior mode results

Posterior sampling results

Prediction event

Predictive distributions

Prior density

Prior mean for parameters on lags

Prior type for covariance parameters

Prior type for parameters on lags

Raw posterior draws

Risk analysis

Sequential posterior mean

Sequential posterior median

Stationarity

Steady state parameter prior file

Steady state parameter prior mean

Steady state parameter prior standard deviation

Wishart degrees of freedom

Wishart location parameters

Belief coefficient estimation (adaptive learning)

Belief system file

beta

Burn-in period

- C -

Cauchy

Chandrasekhar recursions

Check optimum

Check only for transformed parameters

Finite difference inverse Hessian

Grid width

Number of grid points

Plots

Run after posterior mode estimation

Check optimum (adaptive learning)

Plots

chi-square

Clear log

Close DSGE Model File

Coherence

Observed variables

Observed variables (DSGE-VAR)

State variables

Cointegration

Compile

Conditional correlations

DSGE model

DSGE-VAR

Conditional forecasting

Distribution of shocks

Distribution of state variables

Reorder conditioning shocks

Reorder direct method shocks for state variables

Select direct method shocks for observed variables

Select direct method shocks for state variables

Select state variables

Select subset method shocks for observed variables

Select subset method shocks for state variables

Select variables

Time-varying number of conditioning state variables

Time-varying number of conditioning variables

Values for shocks

Waggoner-Zha

Conditional variance decomposition

Confidence band colors

Confidence bands

Confidence region

Equal talks

Highest probability density (unimodal distribution)

Configure shocks

Shock alias

Shock color

Constrained filters

Estimation

Select constraints

Control System Toolbox

Controllability

Convergence

Bayesian VAR

DSGE model

Correlation

Conditional

Conditional (DSGE-VAR)

Decomposition

Decomposition (DSGE-VAR)

Measurement errors

Observed variables

Observed variables (DSGE-VAR)

State shocks

State variables

Correlation decompositions

DSGE model

DSGE-VAR

Correlations

Structural shcoks (DSGE-VAR)

Counterfactual

Observed variable scenario

Parameter scenario

State variable scenario

Covariance matrix prior

Coverage probability

End

Increment

Start

Credible region

Equal talks

Highest probability density (unimodal distribution)

Cross-equation tightness

csminwel

CUSUM

- D -

Data construction file

Actuals

Annualization

Bayesian VAR variables

Conditional forecasting data

Levels

Optional fields

Percentiles

Required fields

Sample frequency

Sample settings

Summary information

Transformation

Transformation functions

Variable names

Zero lower bound

Data input

Data simulation

DSGE

DSGE-VAR

Diff files

Diffuse initialization

Dirichlet

Doubling algorithm

Convergence criterion

Iterations

Tolerance level

Download YADA

Driver Tools

Run driver functions

Run matlab script file

Select driver functions

DSGE model

Calibrated parameters

Controllability

Deterministic variables

Estimated parameters

examples

Exogenous variables

External solver

Indeterminacy

Initialized parameters

Klein's solab solver

Measurement equations

Model description

Observability

Observed variables

Parameter values

Prior distribution

Save inverse Hessian

Selecting solver

Setup

Sims' gensys solver

Solvers

State-space representation

Structural form

Unique and stable solution

Update parameters

VAR representation

DSGE Model File

Close

Driver tools

Model sequence

Open

Reload

Reopen

Run driver functions

Save

Select driver functions

DSGE model files

DSGE model solution eigenvalues

DSGE Model Tolerance

DSGE-VAR

Acceptance ratio

Check posterior mode

Chib and Jeliazkov

Coherence

Compare estimated parameters

Compare marginal likelihood

Conditional correlations

Conditional predictive distributions

Convergence

Correlation decompositions

CUSUM

DSGE model parameters

Eigenvalues

Estimate structural shocks

Geweke

Identification

Impulse responses

Joint Posterior Model

Kurtosis

Lag order

Lag order selection

Laplace posterior densities

Log-likelihood function

MANOVA

Marginal Posterior Model

Marginal predictive moments

Model sequence

Modesty statistics

Modified harmonic mean

Moving CUSUM

MPSRF

Multiple chain convergence statistic

Multivariate analysis of variance

Multivariate potential scale reduction factor

Observed variable correlations

Observed variable decompositions

Optimization error summary

Parameter change

Population moments

Posterior densities

Posterior mode results

Posterior mode summary

Posterior sampling DSGE parameters

Posterior sampling summary

Posterior sampling VAR parameters

Prediction event

Predictive distributions

Predictive likelihood

Prior densities

Prior sampling VAR parameters

Proposal density

Raw posterior draws

Risk analysis

Sample moments

Scatter-plot posterior draws

Separated partial means test

Sequential marginal likelihood

Sequential posterior mean

Sequential posterior median

Sims, Waggoner and Zha

Simulate data

Single chain convergence statistic - acceptance ratio

Single chain convergence statistic - CUSUM

Single chain convergence statistic - posterior mean

Single chain convergence statistic - posterior median

Single chain convergence statistic - separated partial means test

Single chain convergence statistic - SMC adaption

Skewness

SMC adaption

Spectral decompositions

Stationarity

Structural shock correlations

Structural shocks

VAR equations

VAR parameters

Variance decompositions

DSGE-VECM

Dynare

Model file

Parser

Path

Steady-state parameters

View dynare model file

Dynare model file

- E -

Economic shocks

Eigenvalues

Adaptive learning solution

DSGE Model

DSGE-VAR model

VAR Model

Equal tails

Erlang

Estimation

Constrained measurement errors

Constrained state shocks

Constrained state variables

DSGE-VAR structural shocks

Joint Posterior mode

Marginal Posterior mode

Measurement errors

Posterior mean

Posterior median

Posterior mode

Posterior mode under adaptive learning

Recursive smooth estimation

Simulation smoother

State shocks

State variables

System prior mode

System prior mode under adaptive learning

Estimation (adaptive learning)

Actual law of motion

ALM

Belief coefficients

Forward looking variables

Information matrix

Measurement errors

Perceived law of motion

Persistence

PLM

PLM coefficients

Recursive smooth estimation

Simulation smoother

State shocks

State variables

Estimation log

Clear log window button

Estimation Sample

Exit

exponential

Extending YADA

Extra csminwel runs

- F -

F

Filter tunes

Estimation

Select constraints

Finite difference inverse Hessian

Step length

First difference variable

Fisher's information matrix

fminunc

Forecast error decomposition

Forecast error variance decompositions

Conditional

DSGE-VAR

Levels data

Original data

Riccati equation solver

State, shock, measurement error, parameter uncertainty

Unconditional

Forecast horizon

Maximum length

Forecasting

Backcasting

Bayesian VAR

Conditional

Continuous ranked probability score

CRPS

DSGE-VAR

Energy score

ES

Marginal predictive moments

Nowcasting

PIT

Predictive likelihood

Probability Integral Transform

Ragged edge data

Unconditional

Zero lower bound

Forecasting (adaptive learning)

Continuous ranked probability score

CRPS

Energy score

PIT

Probability integral transform

Unconditional

Frequency domain

Coherence - Observed variables

Coherence - State variables

Coherence (DSGE-VAR)

Information matrix

Spectral decomposition, DSGE model

Spectral decomposition, DSGE-VAR model

- G -

gamma

gensys

GNU General Public License

Groups

Observed variables

Shocks

Gumbel

- H -

Harmonic lag decay

Highest probability density

Hyperparameter

- I -

IF

Import prior data

Import YADA settings

Impulse responses

Annual data

DSGE-VAR

Levels data

Number of responses

Original data

State variables

Impulse responses (adaptive learning)

Annual data

Levels data

Original data

State variables

Inefficiency factor

Information matrix

Initial state covariance matrix

Analytical method (vectorization)

Constant time identity

Diffuse method

Numerical method (doubling algorithm)

Initial state values

Initialize parameters

Parameter functions

Required input

Required ouptut

Inverse Hessian estimation

Finite difference

Initialization

My estimate

Optimzation routine output

Parameter covariance matrix

Quadratic approximation to log posterior

Transform conditional standard deviations for modiefied Hessian to marginal

inverted gamma

Inverted Wishart

Degrees of freedom

ML estimate of A

Variance tightness hyperparameter

- J -

Jacobian

Jacobian (adaptive learning)

- K -

Kalman filter

Chandrasekhar recursions

Diffuse initialization

Initial covariance matrix

Initial mean

Initial values

Square root filter

Standard filter

Training sample

Univariate filter

Kernel density estimation

Bi-weight

Bump killing bandwidth

Epanechnikov

Gaussian

Laplace

Logistic

Normal

Posterior density

Predictive density

Predictive density (adaptive learning)

Prior density

Rectangular

Sheather-Jones bandwidth

Silverman-type

Sköld-Roberts correction

Triangular

Tri-weight

Kolmogorov-Smirnov tests

Kurtosis

- L -

Lag length

Lag order

left truncated normal

Levels variable

License

logistic

Log-likelihood function

Log-likelihood function (adaptive learning)

Log-linearized model

log-normal

- M -

Macinstosh OS X

MANOVA

Manual

Marginal likelihood

Adaptive learning results

Chib and Jeliazkov

Compare

DSGE-VAR

Geweke's modified harmonic mean

Results

Sims, Waggoner and Zha's modified harmonic mean

Markov Chain Monte Carlo

Matlab path

Matlab shortcuts

Matlab toolboxes

Control system toolbox

Optimization toolbox

Maximization

csminwel

Extra csminwel runs

fminunc

gmhmaxlik

Maximum number of iterations

newrat

Tolerance level

Measurement equations

Required input

Required output

Measurement error estimation

Measurement error estimation (adaptive learning)

MitISEM algorithm

Candidate density

Coefficient of variation

Maximum number of mixture components

Mixture of Student-t

Tolerance level

Model name

Model Sequence

Continuous ranked probability score

Marginal likelihood

Marginal predictive moments

Posterior mode

Posterior sampling

Predictive likelihood

Probability integral transforms

Run model sequence

Select models

Model setup

Modesty analysis

Bayesian VAR

DSGE model

DSGE-VAR

Modified harmonic mean

Geweke

Sims, Waggoner and Zha

Monte Carlo filtering

Monte Carlo filtering (adaptive learning)

Moving CUSUM

MPSRF

MS-Windows

Multiple chain convergence statistic

Multivariate analysis of variance

Multivariate potential scale reduction factor

- N -

Naive belief system

No smoothed states for belief Kalman filter

normal

- O -

Observability

Observation weight decompositions

Observation weights

Observed variable decomposition

Bars

DSGE Model

DSGE-VAR

Paths

Scatter-plot

Observed variable decomposition (adaptive learning)

Bars

DSGE Model

Scatter-plot

Observed variable groups

Observed variable scenario

Constrained shock estimates

Method

Optimal shock selection

Reorder shock

Run scenario

Select shocks

Select variables

Time varying number of scenario variables

User shock selection

Observed variable scenarios (adaptive learning)

DSGE model

Open DSGE Model File

Open Text File

Open YADA

Operating system

Optimization error

Optimization toolbox

Output directory

Overall tightness

- P -

Parallel Computing Toolbox

Close parallel pool

Open parallel pool

Parameter change

Observed variables

Observed variables (DSGE-VAR)

State variables

Parameter covariance matrix

Parameter data

Parameters to initialize

Parameters to update

Parameter transformations

Original parameters

Transformed parameters

Parameter values

Adaptive learning

Pareto

Path

Perceived law of motion

Percentiles

Persistence (adaptive learning)

PLM

PLM coefficient estimation

Plotting

Annualized observed variables

Conditioning variable assumptions

Observed variables

State variable assumptions

Transformed observed variables

Poor man's invertibility condition

Population moments

Conditional correlations

Conditional correlations (DSGE-VAR)

Correlation decompositions

Observed variables

Observed variables (DSGE-VAR)

State variables

Posterior correlations

Posterior correlations (adaptive learning)

Posterior densities

Bivariate

Diffusion process

DSGE model

Fast Fourier Transform

Marginal

Posterior densities (adaptive learning)

Bivariate

Diffusion process

Fast Fourier Transform

Marginal

Posterior density

Bayesian VAR

DSGE model parameters of DSGE-VAR

DSGE-VAR Laplace approximation

DSGE-VAR normal approximation

Laplace approximation

Normal approximation

VAR parameters of DSGE-VAR

Posterior density (adaptive learning)

Laplace approximation

Normal approximation

Posterior mode

Adaptive learning results

Adaptive learning summary

Bayesian VAR

Check optimum

Convergence problem

DSGE model

Estimation

Iterated estimates

Joint distribution

Joint of DSGE-VAR

Marginal distribution

Marginal distribution under adaptive learning

Marginal of DSGE-VAR

Results

Summary

Surface

Posterior mode (adaptive learning)

Check optimum

DSGE model

Estimation

Iterated estimates

Surface

Posterior predictive checks

One-step-ahead forecast error covariances

Posterior sampling

Adaptive learning

Bayesian VAR

DSGE model

DSGE model with adaptive learning

DSGE parameters of DSGE-VAR model

Fixed block RWM

Fixed blocking RWM

Gibbs sampling

Importance sampling

Importance sampling based on the MitISEM algorithm

Initialization

IS

IS based on the MitISEM algorithm

Number of burn-in draws

Number of draws

Number of parallel chain

Number of saves

Random blocking RWM

Random walk Metropolis

Random walk Metropolis algorithm

Sequential Monte Carlo with data tempering

Sequential Monte Carlo with likelihood tempering

Slice sampler

SMC with data tempering

SMC with likelihood tempering

VAR parameters of DSGE-VAR

Prediction

Bayesian VAR

Conditional

DSGE-VAR

Unconditional

Prediction (adaptive learning)

Unconditional

Prediction event

Bayesian VAR

DSGE model

DSGE-VAR

Prediction event (adaptive learning)

DSGE model

Predictive likelihood

DSGE model

DSGE-VAR

Print Setup

Prior correlations

Prior Data

Excel spreadsheet

Lotus 1-2-3 spreadsheet

System prior

Prior data headers

Initial value

Lower bound

Model parameter

Prior parameter 1

Prior parameter 2

Prior parameter 3

Prior type

Status

Upper bound

Prior densities

DSGE model

Prior density

Bayesian VAR

Bivariate density

DSGE model parameters

Fast Fourier Transform

Graph density

Grid density estimate

Kernel density estimate

Linear Diffusion

Marginal prior density

Numerical value

VAR parameters of DSGE-VAR

Prior density (adaptive learning)

Numerical value

Prior distribution

Adaptive learning parameters

Prior distribution specification file

Prior Distributions

beta

Cauchy

chi-square

Dirichlet

Erlang

exponential

F

gamma

Graph distribution

Gumbel

inverted gamma

left truncated normal

logistic

log-normal

normal

Pareto

Snedecor

Student-t

Summary information

Summary information under adaptive learning

System prior

Type I generalized logistic

uniform

Weibull

Prior predictive checks

One-step-ahead forecast error covariances

Prior Sampling

DSGE model parameters

Number of default draws

Number of draws

VAR parameters of DSGE-VAR

Prior Sampling (adaptive learning)

Adaptive learning

DSGE model with adaptive learning

Progress dialog

Show dialog

Show time

Proposal density

Normal

Student-t

- Q -

QR factorization with column pivoting

Quantiles

Quit

- R -

Random number initialization

Fixed state

Random state

Random walk Metropolis algorithm

Fixed blocking

Normal proposal density

Random blocking

Student-t proposal density

Raw posterior draws

Log posterior kernel

Normalized weights (SMC)

Original parameters

Transformed parameters

Raw posterior draws (adaptive learning)

Log posterior kernel

Normalized weights (SMC)

Original parameters

Transformed parameters

Relative numerical efficiency

Reload DSGE Model File

Reopen DSGE Model File

Reserved parameter names

Results

Adaptive learning

Retreive data

Riccati equation

Maximum number of iterations

Tolerance level

Risk analysis

Bayesian VAR

DSGE model

DSGE-VAR

Risk analysis (adaptive learning)

DSGE model

RNE

Run AiM Parser

- S -

Sample moments

Conditional correlations

Conditional correlations (DSGE-VAR)

Observed variables

Observed variables (DSGE-VAR)

State variables

Save DSGE Model File

Save results

Scale factor

Scatter-plot

DSGE-VAR original parameters

DSGE-VAR transformed parameters

Original parameters

Transformed parameters

Scatter-plot (adaptive learning)

Original parameters

Transformed parameters

Select belief system

Select conditioning variables

Select constraints for state variables

Select direct method conditioning shocks

Observed variables

State variables

Select model solver

Select scenario shocks

Select scenario variables

Select state scenario shocks

Select state scenario variables

Select state variable assumptions

Select subset method conditioning shocks

Observed variables

State variables

Selected Sample

Sequential estimation

StartIteration

StepLength

Sequential marginal likelihood

Bayesian VAR

Chib and Jeliazkov

DSGE-VAR model

Geweke

Laplace approximation

Modified harmonic mean

Sims, Waggoner and Zha

Sequential marginal likelihood (adaptive learning)

Chib and Jeliazkov

Geweke

Laplace approximation

Modified harmonic mean

Sims, Waggoner and Zha

Sequential Monte Carlo

Bending parameter

Data tempering

Initial scale factor value

Likelihood tempering

Mixing weight

Multinomial resampling

Number of fixed parameter blocks

Number of Metropolis-Hastings steps

Number of particles (draws)

Number of tempering stages

Recursion threshold

Resampling algorithm

Resampling threshold

Residual resampling

Stratified resampling

Systematic resampling

Target acceptance rate

Tempering schedule

Set economic shocks

Set forward variables

Set state equations

Set state shocks

Set state variables

Set structural shocks

Shock alias

Shock color

Shock groups

Shortcuts editor

Signal extraction error

Simulating data

DSGE

DSGE-VAR

Number of parameter values

Number of paths per parameter value

Single chain convergence statistic

Acceptance ratio

CUSUM

Separated partial means test

Sequential posterior mean

Sequential posterior median

SMC adaption

Singular value decomposition

Skewness

Slice sampler

SMC adaption

Snedecor

solab

Spectral decomposition

DSGE model

DSGE-VAR model

Spectral density

DSGE model

DSGE-VAR model

Square root filter

State shock estimation

State shock estimation (adaptive learning)

State variable decomposition

State variable decomposition (adaptive learning)

State variable scenario

Constrained shock estimates

Method

Optimal shock selection

Reorder shock

Run scenarios

Select shocks

Select variables

Time varying number of state scenario variables

User shock selection

State variable scenario (adaptive learning)

DSGE model

State-space representation

Save

View

Stationarity

Steady state parameter prior file

Structural equations

Structural form

Structural shocks

Student-t

Subsets of parameter draws

Equal distance

Method selection

Number of draws for prediction

Percentage use for impulse responses

Random draws

System prior correlations

System prior density

System prior density file

System requirement

Matlab

System requirements

- T -

Toolbar

About

Close Model

Configure Shocks

Estimate Posterior Mode

Help

License

Open Graphics

Open Model

Posterior Sampling

Quit

Reload Model

Run AiM parser

Save Model

Set Shock Groups

Set State Equations

Set State Shocks

Set State Variables

Toolbox

Control system toolbox

Optimization toolbox

Training sample

Transformation functions

Annualization (annual)

Export (export)

First order Taylor expansion of annualization function (annualizepartial)

First order Taylor expansion of general function (partial)

General (fcn)

Inversion (invert)

Linear combinations of observed variables

Tuned filters

Estimation

Select constraints

- U -

uniform

Unit roots

Defined

Specified

Undefined

Univariate filter

UNIX

Update parameters

Parameter functions

Required input

Required output

Use fixed beliefs for projections

- V -

Variables

DSGE model

VAR model

Variance decompositions

Conditional

DSGE-VAR

Levels data

Original data

State variables

Unconditional

Variance tightness hyperparameter

Version

1.00

1.10

1.20

1.30

1.40

1.50

1.60

1.80

1.90

2.00

2.10

2.20

2.30

2.40

2.50

2.60

2.70

2.80

2.90

3.00

3.10

3.20

3.30

3.40

3.50

3.60

3.70

3.80

3.90

4.00

4.10

4.20

4.30

4.40

4.50

4.60

4.70

4.80

4.90

5.00

5.10

5.20

5.30

5.40

5.50

- W -

Web site

Weibull

- Y -

YADA homepage

- Z -

Zero lower bound

Data

Policy rate variable