YADA Help
Contents
| Index
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