Ox Class Reference

Database class

Database function members

Database data members

PcFiml class

PcFiml function data members

PcNaiveDgp class

PcNaiveDgp function members

PcNaiveDgp data members

Sample class

Sample function members

Sample data members

Simulation class

Simulation function members

Simulation data members

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Database class

The Database class stores a matrix of data, together with the sample period (the class derives from the Sample class), and the names of the variables. Functions to create a database from disk files (ASCII, GiveWin and spreadsheet formats) are provided. A sample period for selection can be set, variables are selected by name, optionally with a lag length. This selection can then be extracted from the database. The selected sample is always adjusted so as not to include missing values (-9999.99). To use the class, link in the database code and include database.h.

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Database function members

Database::Append(const mNew, const asNew, const iT1);

mNew

in: T x k matrix with the new variables

asNew

in: array with k variable names of the new variables

iT1

in: starting observation index in database

Appends the k new variables to the database, storing the observations and variable names. The first observation has database index iT1 (use 0 if the variables start at the same sample point as the database), the last is the end of the database sample, or the end of mNew, whichever comes first.

Create(const iFreq, const iYear1, const iPeriod1,
    const iYear2, const iPeriod2);

iFreq

in: int, frequency

iYear1

in: int, start year

iPeriod1

in: int, start period

iYear2

in: int, end year

iPeriod2

in: int, end period

Creates a database. Use this when the database is not to be loaded from disk. The Append member function allows adding data to the database.

Database::Database();

Constructor.

Database::DeSelect();

Clears the current selection.

Database::Deterministic(const fCseason);

Appends constant, trend and seasonals to the database. These variables are named Constant, Trend and Season. Season_1, ..., Season_x, where x is the frequency. Season has a 1 in quarter 1 (for quarterly data), and zeros elsewhere, Season_1 has a 1 in quarter 2, etc. If fCseason is TRUE, the seasonals are centred (with quarterly observations, for quarter 1: 0.75, -0.25, -0.25, -0.25, ...), in which case the names are CSeason, CSeason_1, ..., CSeason_x.

Database::FindSelection(const sVar, const iLag);

sVar

in: string, variable name

iLag

in: int, lag length

Returns the selection index of the specified variable with the specified lag, or -1 if it is not selected.

Database::GetAll();

Returns the whole database matrix.

Database::GetGroup(const iGroup);
Database::GetGroupLag(const iGroup, const iLag1, const iLag2);

iGroup

in: int, group number

iLag1

in: int, first lag

iLag2

in: int, last lag

GetGroup returns a T x n matrix with all selected variables of group iGroup.

GetGroupLag returns only those with the specified lag length. If no database sample has been selected yet, the return value is a 0.

Database::GetGroupLag(const iGroup, const iLag1,
    const iLag2, aasNames);

iGroup

in: int, group number

iLag1

in: int, first lag

iLag2

in: int, last lag

aasNames

in: array

out: will hold an array of strings with the names of the variables with specified group and lag

GetGroupLag gets the names of all selected variables of group iGroup which have a lag in iLag1 ... iLag2. The selection sample period must have been set.

Database::GetMaxLag();
Database::GetMaxGroupLag(iGroup);

GetMaxLag returns the highest lag in all selected variables. GetMaxGroupLag returns the highest lag in selected variables of the specified group iGroup.

Database::GetSelStart();
Database::GetSelEnd();

GetSelStart returns the database index of the first observation of the selected sample. GetSelEnd returns the database index of the last observation of the selected sample.

Database::GetSelSample();

GetSelSample returns a string with the selected sample text, e.g. "1980 (1) - 1984 (2)".

Database::GetVar(const sName);

GetVar returns the specified variable, or 0 if the variable cannot be found.

Database::Info();

Prints information on the contents of the database.

Database::LoadDht(const sFilename, const iYear1,
    const iPeriod1, const iFreq);
Database::LoadFmtVar(const sFilename);
Database::LoadIn7(const sFilename);
Database::LoadObs(const sFilename, const cVar,const cObs,
    const iYear1, const iPeriod1, const iFreq,
    const fOffendMis);
Database::LoadVar(const sFilename, const cVar,const cObs,
    const iYear1, const iPeriod1, const iFreq,
    const fOffendMis);
Database::LoadWks(const sFilename);
Database::LoadXls(const sFilename);

sFilename

in: string, filename

cVar

in: int, number of variables

cObs

in: int, number of observations

iYear1

in: int, start year

iPeriod1

in: int, start period

iFreq

in: int, frequency

fOffendMis

in: int, TRUE:offending text treated as missing value FALSE: offending text skipped

LoadDht creates the database and loads the specified Gauss data file from disk.

LoadIn7 creates the database and loads the specified GiveWin file (which is the same as a PcGive 7 data file) from disk.

LoadFmtVar creates the database and loads the ASCII file with formatting information from disk. In GiveWin this is called `Data with load info'. Such a file is human-readable, with the data ordered by variable, and each variable preceded by a line of the type:

> name year1 period1 year2 period2 frequency

LoadObs and LoadVar create the database and load the specified human-readable data file from disk. The data is ordered by observation (LoadObs), or by variable. Since there is no information on the sample or the variable names in these files, the sample must be provided as function arguments. The variable names are set to Var1, Var2, etc., use Rename to rename the variables.

LoadWks and LoadXLS create the database and load the specified spreadsheet file from disk. A .wks or .wk1 file is a Lotus file, an .xls file is an Excel worksheet (up to version 4).

Database class can read the following types of Excel file:

• Excel 2.1;
• Excel 3.0;
• Excel 4.0 (called `normal' inside Excel 4).

When saving an Excel file, it is written in Excel 2.1 format.

Database::Remove(const sName);

Removes the named variable from the database.

Database::Rename(const sNewName, const sOldName);

sNewName

in: new name

sOldName

in: name of database variable to rename

Renames a database variable.

Database::Renew(const mNew, const sName, const iT1);

mNew

in: T x 1 matrix

sName

in: variable names

iT1

in: first observation

Renews the observations on the named variable. The first new observation has database index iT1, the last is the end of the database sample, or the end of mNew, whichever comes first.

Database::SaveIn7(const sFilename);
Database::SaveFmtVar(const sFilename);
Database::SaveObs(const sFilename);
Database::SaveVar(const sFilename);
Database::SaveWks(const sFilename);
Database::SaveXls(const sFilename);

sFilename

in: string, filename

SaveIn7 saves the database as a GiveWin file.

SaveFmtVar saves the database as a formatted ASCII file. Also see under LoadFmtVar.

SaveObs and SaveVar save the database as a human-readable data file, ordered by observation, or by variable. Also see under LoadObs, LoadVar.

SaveWks and SaveXls save the database as a Lotus or Excel spreadsheet file. Also see under LoadWks, LoadXls.

Database::Select(const iGroup, const aSel);

Selects variables by name and with specified lags, and assigns the iGroup number to the selection. The aSel argument is an array consisting of sequences of three values: name, start lag, end lag. After a sample period is set, the selection can be extracted from the database.

Database::SetSample(const iYear1, const iPeriod1,
    const iYear2, const iPeriod2);

iYear1

in: int, start year of selection, use -1 for earliest year and period

iPeriod1

in: int, start period of selection

iYear2

in: int, end year of selection, use -1 for latest year and period

iPeriod2

in: int, end period of selection

Selects a sample for the variables previously selected with the Select function. The actually selected sample will be the largest starting from the specified starting date (but not exceeding the specified end date) without any missing values. Use DeSelect to deselect the current sample and variables.

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Database data members

m_mData

data matrix (T x k)

m_asNames

variable names (array with k strings)

m_mVarsel

variable selection (1 x s matrix with selection) the selection consists of indices in m_mData and m_asNames

m_mLagsel

lag length of each entry in m_mVarsel (1 x s matrix)

m_mSelgroup

group number of each entry in m_mVarsel (1 x s matrix)

m_iT1sel

row index in m_mData of first selected observation (int)

m_iT2sel

row index in m_mData of last selected observation (int)

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PcFiml class

The PcFiml class provides part of the advanced computations available in the menu driven computer program PcFiml. The class is derived from the Database class, and provides model formulation using variable names. The class allows for estimating a Vector Autoregression (VAR), cointegration analysis (`Johansen procedure'), and multivariate regression model (such as an unrestricted reduced form, URF), as well as a simultaneous equations model (2SLS, 3SLS, FIML). No identities equations are currently possible. Mis-specification tests include: vector autoregression, vector normality, vector heteroscedasticity, vector portmanteau, as well as a Chow test.

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PcFiml function data members

ArTest(const iAr1, const iAr2);

System vector AR test for lags iAr1... iAr2.

Chow(const iYear, const iPeriod);

Forecast Chow tests for break on or after iYear (iPeriod).

Cointegration();

Estimate cointegrating space.

EgeArTest(const iAr1, const iAr2);

Model vector AR test for lags iAr1...iAr2.

Estimate();

Estimate the system (NB: use ->SetSample() first).

EstimateAcc();

SVD based estimation of the system (NB: use ->SetSample() first).

Fiml();

Do FIML estimation.

GetOmega();

Returns n x n matrix of URF/RRF residual variance V'V/(T-k).

GetPi();

Returns n x k matrix of URF/RRF coefficients.

GetResiduals();

Returns T x n matrix V of URF/RRF residuals.

GetResult();

Returns results from FIML estimation (return code from MaxBFGS).

GetVarPi();

Returns n x k matrix with variances of RRF/URF coefficients.

GetVarTheta();

System: returns full nk x nk variance-covariance matrix of URF coefficients;

Model: returns full np x np variance-covariance matrix of model coefficients.

HeteroTest(const fStand, const fCross);

Vector heteroscedasticity test.

NormalityTest();

Vector normality test.

Output(const fSys, const fCoint);

Print System and/or Cointegration results.

PcFiml();

Constructor.

Portmanteau(const iLag);

Vector portmanteau test up to lag iLag.

SetEquation(const sEquation, const aModel);

Delete or add variable from model.

SetPrint(fPrint);

Toggles print switch.

SetPrintUrf(fPrintUrf);

Toggles URF print switch.

ThreeSLS();

Do 3SLS estimation.

TwoSLS();

Do 2SLS estimation.

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PcNaiveDgp class

The PcNaiveDgp class is a data generation process (DGP), designed for use in dynamic econometric Monte Carlo experiments. The class is used through the header file pcnaive.h. The design is an n-variate version DGP:

y_t = A₀y_t + A₁y_t-1 + A₂z_t + a₃ + u_t,

u_t = B₀u_t-1 + e_t + B₁e_t-1,

z_t = C₀z_t-1 + c₁ + c₂t + v_t.

The vectors y_t,u_t,e_t are n x 1, so that the coefficient matrices A₀,A₁,B₀,B₁ are n x n, and a₃ is n x 1. The z_t vector is q x 1, making a₂ n x q, C₀ q x q, and c₁,c₂ q x 1. The zs can be kept fixed between experiments, or regenerated for the experiment. A distribution for e_t and v_t can be specified.

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PcNaiveDgp function members

PcNaiveDgp::Asymp();

Prints an asymptotic analysis (companion matrix with eigenvalues) of the current DGP.

PcNaiveDgp::DiscardZ();

Discards the current z_t; the next call to Generate() will generate new observations on z_t.

PcNaiveDgp::Generate(const cT);

Returns generated Y = (y₀ ... y_T)', as a T x n matrix.

Generates cT observation of the current DGP.

PcNaiveDgp::GetU();
PcNaiveDgp::GetY();
PcNaiveDgp::GetZ();

GetU returns current U as a T x n matrix.

GetY returns current Y as a T x n matrix (as does Generate).

GetZ returns current Z as a T x q matrix.

PcNaiveDgp::PcNaiveDgp(const cY, const cZ);

cY

in: int, n, dimension of y_t

cZ

in: int, q, dimension of z_t

Constructor.

PcNaiveDgp::Print();

Prints the setup of the current DGP.

PcNaiveDgp::SetDistribution(const iEqn, const iDist,
    mPar1, mPar2);

iEqn

in: one of: U_DGP, Z_DGP

iDist

in: one of: NO_DIST, NORMAL, MVNORMAL, LOGNORMAL, T_DIST, F_DIST, EXPONENTIAL, MVNARCH, MVNHETERO

mPar1

in: first parameter of distribution, MVNARCH, MVNHETERO: n x n for y_t, u_t; q x q for z_t others: n x 1 for y_t, u_t; q x 1 for z_t

mPar2

in: second parameter of distribution, MVNORMAL, MVNARCH, MVNHETERO: n x n for y_t, u_t; q x q for z_t others: n x 1 for y_t, u_t; q x 1 for z_t

Specifies the distribution for the u, or z equations given above. The first argument indicates the equation, the second the distribution. The last two arguments parameterize the distribution.

PcNaiveDgp::SetFixedZ(const fSetting);

Specifies whether z_t is fixed (fSetting equals TRUE) or not. Fixed z_t is only generated once, until a call to DiscardZ or SetFixedZ is made.

PcNaiveDgp::SetMixing(const iEqn, const iDist,
    mPar1, mPar2, const iT1, const iT2,
    const mMixParIn, const mMixParOut);

Not yet implemented

PcNaiveDgp::SetUParameter(const mLagAr, const mLagMa);

mLagAr

in: n x n matrix B₀

mLagMa

in: n x n matrix B₁

Sets the parameters for the e_t equation.

PcNaiveDgp::SetYParameter(const mLevel, const mLag,
    const mZpar, const vConst);

mLevel

in: n x n matrix A₀ must have zeros on the diagonal

mLag

in: n x n matrix A₁

mZpar

in: n x q matrix A₂

vConst

in: n x 1 matrix a₃

Sets the parameters for the y_t equation.

PcNaiveDgp::SetZParameter(const mLag, const vConst,
    const vTrend);

mLag

in: q x q matrix C₀

vConst

in: q x 1 matrix c₁

vTrend

in: q x 1 matrix c₂

Sets the parameters for the z_t equation.

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PcNaiveDgp data members

m_cY

n, dimension of y_t,

m_cZ

q, dimension of z_t,

m_mZ

generated Z, T x q,

m_mY

generated Y, T x n,

m_mU

generated U, T x n,

m_mUlag

B₀', error AR parameters, n x n,

m_mElag

B₁', error MA parameters, n x n,

m_iEdist

error distribution,

m_mEdistPar1

error distribution parameter 1,

m_mEdistPar2

error distribution parameter 2,

m_iEmix

error mixing distribution,

m_mEmixPar1

mixing distribution parameters, 1 x n,

m_mEmixPar2

mixing distribution parameters, 1 x n,

m_iEmixT1

first observation over which to mix,

m_iEmixT2

last observation over which to mix,

m_mEmixParIn

error mixing parameter inside sample, 1 x n,

m_mEmixParOut

error mixing parameter outside sample, 1 x n,

m_mZlag

C₀', coefficients on z_t-1, q x q,

m_mZconst

c₁', coefficients on constant, 1 x q,

m_mZtrend

c₂', coefficients on trend, 1 x q,

m_iZdist

z distribution,

m_mZdistPar1

z distribution parameters 1,

m_mZdistPar2

z distribution parameter 2,

m_iZmix

z mixing distribution,

m_mZmixPar1

z mixing distribution parameters, 1 x q,

m_mZmixPar2

z mixing distribution parameters, 1 x q,

m_iZmixT1

first observation over which to mix z ,

m_iZmixT2

last observation over which to mix z ,

m_mZmixParIn

z mixing parameter inside sample, 1 x n,

m_mZmixParOut

z mixing parameter outside sample, 1 x n,

m_fFixedZ

TRUE for fixed z_t,

m_mYlevel

A₀ structural coefficients on y_t, n x n,

m_mYlag

A₁ structural coefficients on y_t-1, n x n,

m_mYparZ

A₂ structural coefficients on z_t, n x q,

m_mYconst

a₃ structural coefficients on constant, n x 1,

m_mY0inv

D^-1, D= (I-A₀)',

m_mYlagRf

A₁'D^-1 reduced form coefficients on y_t-1,

m_mYparZRf

A₂'D^-1 reduced form coefficients on z_t,

m_mYconstRf

a₃'D^-1 reduced form coefficients on constant.

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Sample class

The Sample class stores a time interval, and the frequency, e.g. 1980 (1) -- 1990 (1), with frequency 4 (i.e. quarterly observations). The Sample class is only used to derive from. It requires linking in the database code, and inclusion of database.h.

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Sample function members

Sample::GetYear1();

The year of the first observation.

Sample::GetPeriod1();

The period of the first observation.

Sample::GetYear2();

The year of the last observation.

Sample::GetPeriod2();

The period of the last observation.

Sample::GetSize();

The number of observations in the sample.

Sample::GetIndex(const iYear, const iPeriod);

The index of the specified time point.

Sample::ObsYear(iObs);

The year of the observation index.

Sample::ObsPeriod(iObs);

The period of the observation index.

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Sample data members

m_iFreq

data frequency (int)

m_iYear1

year of first observation (int)

m_iPeriod1

period of first observation (int)

m_iYear2

year of last observation (int)

m_iPeriod2

period of last observation (int)

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Simulation class

The Simulation class can be used to set up Monte Carlo experiments. Derive your own simulation experimentation class from this, overriding the virtual functions. Simulation will handle the replications and storage, and print the final results. The type of data it can handle are coefficients, test statistics and p-values of test statistics. The class is used through the header file simula.h.

A table is printed at the end, which gives the results, for coefficients:

• mean: mean over all replications (MC mean),
• std.dev: standard deviation around MC mean (MCSD),
• mean bias: MC mean - true value,
• se mean bias: MCSD / sqrt(M),
• rmse: standard deviation around true value, sqrt(MCSD^2 + mean bias^2),

where M is the number of replications and rmse is the root of the mean squared error.

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Simulation function members

virtual Simulation::Generate(const iRep, const cT,
    const mxT);

iRep

in: int, index of current replication (0 is first)

cT

in: int, sample size to be used for replication

mxT

in: int, maximum sample size to be used for replication (this is only relevant when using common random numbers)

The functions should return 1 if successful, 0 if the replications failed.

If the call to the Generate function fails, it is retried until a successful return (so always returning 0, or not returning a value could result in an infinite loop). The number of rejected replications is reported in the output.

The derived class must override the virtual function. It is called for every replication, and must perform the actual replication. The results from this replication are obtained by the simula class by calling GetCoefficients, GetPvalues and GetTestStatistics.

virtual Simulation::GetCoefficients();
virtual Simulation::GetPvalues();
virtual Simulation::GetTestStatistics();

The functions return 0 if the information is not generated, otherwise:

GetCoefficients

s_c x 1 matrix with the observed coefficients GetPvalues

s_p x 1 matrix with observed p-values of the tests GetTestStatistics

s_t x 1 matrix with the observed test statistics

Virtual functions which the derived class must override.

Simulation::SetCoefNames(const asNames)
Simulation::SetTestNames(const asNames)

asNames

in: array with names. For SetCoefNames: array with s_c names; for SetTestNames: array with s_t names.

Installs the names of tests statistics and coefficients, to make the report more readable.

Simulation::Simulate()

This is the core function. It runs the Monte Carlo experiment, and prints the results.

Simulation::Simulation(const mT, const mxT,
    const cRep, const fCommon, const dSeed,
    const mPvalue, const mTrueParam);

mT

in: 1 x r matrix of sample sizes

mxT

in: int, maximum sample size

cRep

in: int, number of replications

fCommon

in: 1: reset seed for each experiment; else 0

dSeed

in: double, resets seed to dSeed if fCommon == TRUE

mPvalue

in: 1 x s_p matrix with p-values to test at, only used if GetPvalues returns p-values

mTrueParam

in: 1 x s_c matrix with true parameters, only used if GetCoefficients returns coefficients

Constructor function.

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Simulation data members

m_mT

sample size matrix

m_mxT

maximum sample size

m_cRep

number of replications

m_cRejected

number of replications rejected

m_mPvalue

row vector with p-values for tests

m_asPvalue

array with names of p-values

m_asTest

array with names of tests

m_asCoef

array with names of coefficients

m_mTrueParam

actual parameter values for coeffs

m_fCommon

TRUE if same seed for each experiment

m_dSeed

reset to dSeed if fCommon==TRUE

m_mCoefMean

mean of coefficients

m_mCoefMeanSq

mean of squares of coefficients

m_mReject

rejection frequencies

m_mTestVal

sorted test values for last sample size

m_mCritVal

critical values corresponding to mPvalue

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Ox version 1.10. This file last changed 18-Jul-1996.