# fgls

Feasible generalized least squares

## Syntax

## Description

`[`

returns vectors of coefficient estimates `coeff`

,`se`

,`EstCoeffCov`

] = fgls(`X`

,`y`

)`coeff`

and corresponding
standard errors `se`

, and the estimated coefficient covariance matrix
`EstCoeffCov`

from applying feasible generalized least squares (FGLS) to the multiple linear regression
model `y`

= `X`

*β* +
*ε*. `y`

is a vector of response data and
`X`

is a matrix of predictor data.

`[`

applies FGLS to the variables in the table or timetable `CoeffTbl`

,`CovTbl`

] = fgls(`Tbl`

)`Tbl`

, and
returns FGLS coefficient estimates and standard errors in the table
`CoeffTbl`

and FGLS estimated coefficient covariance matrix
`EstCoeffCov`

.

The response variable in the regression is the last table variable, and all other
variables are the predictor variables. To select a different response variable for the
regression, use the `ResponseVariable`

name-value argument. To select
different predictor variables, use the `PredictorNames`

name-value
argument.

`[___] = fgls(___,`

uses additional options specified by one or more name-value arguments, using any input-argument combination in the previous syntaxes. `Name=Value`

)`fgls`

returns the output-argument combination for the corresponding input arguments.

For example,
`fgls(Tbl,ResponseVariable="GDP",InnovMdl="H4",Plot="all")`

provides
coefficient, standard error, and residual mean-squared error (MSE) plots of iterations of
FGLS for a regression model with White’s robust innovations covariance, and the table
variable `GDP`

is the response while all other variables are
predictors.

`[___] = fgls(`

plots on the axes specified in `ax`

,___,Plot=plot)`ax`

instead of the axes of new figures
when `plot`

is not `"off"`

. `ax`

can
precede any of the input argument combinations in the previous syntaxes.

`[___,`

returns handles to plotted graphics objects when `iterPlots`

] = fgls(___,Plot=plot)`plot`

is not
`"off"`

. Use elements of `iterPlots`

to modify
properties of the plots after you create them.

## Examples

### Estimate FGLS Coefficients and Uncertainty Measures

Suppose the sensitivity of the US consumer price index (CPI) to changes in the paid compensation of employees (COE) is of interest.

Load the US macroeconomic data set, which contains the matrix of data `Data`

. The COE and CPI series are columns 1 and 2.

load Data_USEconModel.mat COE = Data(:,1); CPI = Data(:,2); dates = datetime(dates,ConvertFrom="datenum");

Plot the series.

tiledlayout(2,1) nexttile plot(dates,CPI); title("\bf Consumer Price Index, Q1 in 1947 to Q1 in 2009"); axis tight nexttile plot(dates,COE); title("\bf Compensation Paid to Employees, Q1 in 1947 to Q1 in 2009"); axis tight

The series are nonstationary. Stabilize them by computing their returns.

rCPI = price2ret(CPI); rCOE = price2ret(COE);

Regress `rCPI`

onto `rCOE`

including an intercept to obtain ordinary least squares (OLS) estimates, standard errors, and the estimated coefficient covariance. Generate a lagged residual plot.

Mdl = fitlm(rCOE,rCPI); clmCoeff = Mdl.Coefficients.Estimate

`clmCoeff = `*2×1*
0.0033
0.3513

clmSE = Mdl.Coefficients.SE

`clmSE = `*2×1*
0.0010
0.0490

CLMEstCoeffCov = Mdl.CoefficientCovariance

`CLMEstCoeffCov = `*2×2*
0.0000 -0.0000
-0.0000 0.0024

```
figure
plotResiduals(Mdl,"lagged")
```

The residual plot exhibits an upward trend, which suggests that the innovations comprise an autoregressive process. This violates one of the classical linear model assumptions. Consequently, hypothesis tests based on the regression coefficients are incorrect, even asymptotically.

Estimate the regression coefficients, standard errors, and coefficient covariances using FGLS. By default, `fgls`

includes an intercept in the regression model and imposes an AR(1) model on the innovations.

[coeff,se,EstCoeffCov] = fgls(rCPI,rCOE)

`coeff = `*2×1*
0.0148
0.1961

`se = `*2×1*
0.0012
0.0685

`EstCoeffCov = `*2×2*
0.0000 -0.0000
-0.0000 0.0047

Row 1 of the outputs corresponds to the intercept and row 2 corresponds to the coefficient of `rCOE`

.

If the COE series is exogenous with respect to the CPI, then the FGLS estimates `coeff`

are consistent and asymptotically more efficient than the OLS estimates.

### Estimate FGLS Coefficients and Uncertainty Measures on Table Data

Load the US macroeconomic data set, which contains the timetable of data `DataTable`

.

`load Data_USEconModel`

Stabilize all series by computing their returns.

RDT = price2ret(DataTable);

`RDT`

is a timetable of returns of all variables in `DataTable`

. The `price2ret`

function conserves variable names.

Estimate the regression coefficients, standard errors, and the coefficient covariance matrix using FGLS. Specify the response and predictor variable names.

[CoeffTbl,CoeffCovTbl] = fgls(RDT,ResponseVariable="CPIAUCSL",PredictorVariables="COE")

`CoeffTbl=`*2×2 table*
Coeff SE
__________ _________
Const 6.2416e-05 1.336e-05
COE 0.20562 0.055615

`CoeffCovTbl=`*2×2 table*
Const COE
___________ ___________
Const 1.7848e-10 -5.6329e-07
COE -5.6329e-07 0.003093

When you supply a table or timetable of data, `fgls`

returns tables of estimates.

### Specify AR Lags For FGLS Estimation

Suppose the sensitivity of the US consumer price index (CPI) to changes in the paid compensation of employees (COE) is of interest. This example expands on the analysis outlined in the example Estimate FGLS Coefficients and Uncertainty Measures.

Load the US macroeconomic data set.

`load Data_USEconModel`

The series are nonstationary. Stabilize them by applying the log, and then the first difference.

LDT = price2ret(Data); rCOE = LDT(:,1); rCPI = LDT(:,2);

Regress `rCPI`

onto `rCOE`

, which includes an intercept to obtain OLS estimates. Plot correlograms for the residuals.

Mdl = fitlm(rCOE,rCPI); u = Mdl.Residuals.Raw; figure; subplot(2,1,1) autocorr(u); subplot(2,1,2); parcorr(u);

The correlograms suggest that the innovations have significant AR effects. According to Box-Jenkins Methodology, the innovations seem to comprise an AR(3) series.

Estimate the regression coefficients using FGLS. By default, `fgls`

assumes that the innovations are autoregressive. Specify that the innovations are AR(3) by using the `ARLags`

name-value argument, and print the final estimates to the command window by using the `Display`

name-value argument.

`fgls(rCPI,rCOE,ARLags=3,Display="final");`

OLS Estimates: | Coeff SE ------------------------ Const | 0.0122 0.0009 x1 | 0.4915 0.0686 FGLS Estimates: | Coeff SE ------------------------ Const | 0.0148 0.0012 x1 | 0.1972 0.0684

If the COE rate series is exogenous with respect to the CPI rate, the FGLS estimates are consistent and asymptotically more efficient than the OLS estimates.

### Account for Residual Heteroscedasticity Using FGLS Estimation

Model the nominal GNP `GNPN`

growth rate accounting for the effects of the growth rates of the consumer price index `CPI`

, real wages `WR`

, and the money stock `MS`

. Account for classical linear model departures.

Load the Nelson-Plosser data set, which contains the data in the table `DataTable`

. Remove all observations containing at least one missing value.

```
load Data_NelsonPlosser
DT = rmmissing(DataTable);
T = height(DT);
```

Plot the series.

predNames = ["CPI" "WR" "MS"]; tiledlayout(2,2) for j = ["GNPN" predNames] nexttile plot(DT{:,j}); xticklabels(DT.Dates) title(j); axis tight end

All series appear nonstationary.

For each series, compute the returns.

RetDT = price2ret(DT);

`RetTT`

is a timetable of the returns of the variables in `TT`

. The variables names are conserved.

Regress the `GNPN`

rate onto the `CPI`

, `WR`

, and `MS`

rates. Examine a scatter plot and correlograms of the residuals.

Mdl = fitlm(RetDT,ResponseVar="GNPN",PredictorVar=predNames); figure plotResiduals(Mdl,"caseorder"); axis tight

figure tiledlayout(2,1) nexttile autocorr(Mdl.Residuals.Raw); nexttile parcorr(Mdl.Residuals.Raw);

The residuals appear to flare in, which is indicative of heteroscedasticity. The correlograms suggest that there is no autocorrelation.

Estimate FGLS coefficients by accounting for the heteroscedasticity of the residuals. Specify that the estimated innovation covariance is diagonal with the squared residuals as weights (that is, White's robust estimator `H0`

).

fgls(RetDT,ResponseVariable="GNPN",PredictorVariables=predNames, ... InnovMdl="HC0",Display="final");

OLS Estimates: | Coeff SE ------------------------- Const | -0.0076 0.0085 CPI | 0.9037 0.1544 WR | 0.9036 0.1906 MS | 0.4285 0.1379 FGLS Estimates: | Coeff SE ------------------------- Const | -0.0102 0.0017 CPI | 0.8853 0.0169 WR | 0.8897 0.0294 MS | 0.4874 0.0291

### Estimate FGLS Coefficients of Models Containing ARMA Errors

Create this regression model with ARMA(1,2) errors, where $${\epsilon}_{t}$$ is Gaussian with mean 0 and variance 1.

$$\begin{array}{c}{y}_{t}=1+{x}_{t}\left[\begin{array}{c}2\\ 3\end{array}\right]+{u}_{t}\\ {u}_{t}=0.6{u}_{t-1}+{\epsilon}_{t}-0.3{\epsilon}_{t-1}+0.1{\epsilon}_{t-1}.\end{array}$$

```
beta = [2 3];
phi = 0.2;
theta = [-0.3 0.1];
Mdl = regARIMA(AR=phi,MA=theta,Intercept=1, ...
Beta=beta,Variance=1);
```

`Mdl`

is a `regARIMA`

model. You can access its properties using dot notation.

Simulate 500 periods of 2-D standard Gaussian values for $${x}_{t}$$, and then simulate responses using `Mdl`

.

```
numObs = 500;
rng(1); % For reproducibility
X = randn(numObs,2);
y = simulate(Mdl,numObs,X=X);
```

`fgls`

supports AR(*p*) innovation models. You can convert an ARMA model polynomial to an infinite-lag AR model polynomial using `arma2ar`

. By default, `arma2ar`

returns the coefficients for the first 10 terms. After the conversion, determine how many lags of the resulting AR model are practically significant by checking the length of the returned vector of coefficients. Choose the number of terms that exceed 0.00001.

```
format long
arParams = arma2ar(phi,theta)
```

`arParams = `*1×3*
-0.100000000000000 0.070000000000000 0.031000000000000

```
arLags = sum(abs(arParams) > 0.00001);
format short
```

Some of the parameters have small magnitude. You might want to reduce the number of lags to include in the innovations model for `fgls`

.

Estimate the coefficients and their standard errors using FGLS and the simulated data. Specify that the innovations comprise an AR(`arLags`

) process.

`[coeff,~,EstCoeffCov] = fgls(X,y,InnovMdl="AR",ARLags=arLags)`

`coeff = `*3×1*
1.0372
2.0366
2.9918

`EstCoeffCov = `*3×3*
0.0026 -0.0000 0.0001
-0.0000 0.0022 0.0000
0.0001 0.0000 0.0024

The estimated coefficients are close to their true values.

### Plot Iterations of FGLS Estimation

This example expands on the analysis in Estimate FGLS Coefficients of Models Containing ARMA Errors. Create this regression model with ARMA(1,4) errors, where $${\epsilon}_{t}$$ is Gaussian with mean 0 and variance 1.

$$\begin{array}{c}{y}_{t}=1+{x}_{t}\left[\begin{array}{c}1.5\\ 2\end{array}\right]+{u}_{t}\\ {u}_{t}=0.9{u}_{t-1}+{\epsilon}_{t}-0.4{\epsilon}_{t-1}+0.2{\epsilon}_{t-4}.\end{array}$$

beta = [1.5 2]; phi = 0.9; theta = [-0.4 0.2]; Mdl = regARIMA(AR=phi,MA=theta,MALags=[1 4],Intercept=1,Beta=beta,Variance=1);

Suppose the distribution of the predictors is

$${x}_{t}\sim N\left(\left[\begin{array}{c}-1\\ 1\end{array}\right],\left[\begin{array}{cc}0.25& 0\\ 0& 1\end{array}\right]\right).$$

Simulate 30 periods from ${\mathit{x}}_{\mathit{t}}$, and then simulate 30 corresponding responses from the regression model with ARMA errors `Mdl`

.

```
numObs = 30;
rng(1); % For reproducibility
muX = [-1 1];
sigX = [0.5 1];
X = randn(numObs,numel(beta)).*sigX + muX;
y = simulate(Mdl,numObs,X=X);
```

Convert the ARMA model polynomial to an infinite-lag AR model polynomial using `arma2ar`

. By default, `arma2ar`

returns the coefficients for the first 10 terms. Find the number of terms that exceed 0.00001.

arParams = arma2ar(phi,theta); arLags = sum(abs(arParams) > 1e-5);

Estimate the regression coefficients by using eight iterations of FGLS, and specify the number of lags in the AR innovation model (`arLags`

). Also, specify to plot the coefficient estimates and their standard errors for each iteration, and to display the final estimates and the OLS estimates in tabular form.

[coeff,~,EstCoeffCov] = fgls(X,y,InnovMdl="AR",ARLags=arLags, ... NumIter=8,Plot=["coeff" "se"],Display="final");

OLS Estimates: | Coeff SE ------------------------ Const | 1.7619 0.4514 x1 | 1.9637 0.3480 x2 | 1.7242 0.2152 FGLS Estimates: | Coeff SE ------------------------ Const | 1.0845 0.6972 x1 | 1.7020 0.2919 x2 | 2.0825 0.1603

The algorithm seems to converge after the four iterations. The FGLS estimates are closer to the true values than the OLS estimates.

Properties of iterative FGLS estimates in finite samples are difficult to establish. For asymptotic properties, one iteration of FGLS is sufficient, but `fgls`

supports iterative FGLS for experimentation.

If the estimates or standard errors show instability after successive iterations, then the estimated innovations covariance might be ill conditioned. Consider scaling the residuals by using the `ResCond`

name-value argument to improve the conditioning of the estimated innovations covariance.

## Input Arguments

`X`

— Predictor data *X*

numeric matrix

Predictor data *X* for the multiple linear regression model,
specified as a `numObs`

-by-`numPreds`

numeric
matrix.

Each row represents one of the `numObs`

observations and each column
represents one of the `numPreds`

predictor variables.

**Data Types: **`double`

`y`

— Response data *y*

numeric vector

Response data *y* for the multiple linear
regression model, specified as a
`numObs`

-by-1 numeric vector.
Rows of `y`

and
`X`

correspond.

**Data Types: **`double`

`Tbl`

— Combined predictor and response data

table | timetable

Combined predictor and response data for the multiple linear regression model,
specified as a table or timetable with `numObs`

rows. Each row of
`Tbl`

is an observation.

The test regresses the response variable, which is the last variable in
`Tbl`

, on the predictor variables, which are all other variables
in `Tbl`

. To select a different response variable for the regression,
use the `ResponseVariable`

name-value argument. To select different
predictor variables, use the `PredictorNames`

name-value argument to
select `numPreds`

predictors.

`ax`

— Axes on which to plot

vector of `Axes`

objects

Axes on which to plot, specified as a vector of `Axes`

objects with
length equal to the number of plots specified by the `Plot`

name-value argument.

By default, `fgls`

creates a separate figure for each
plot.

**Note**

`NaN`

s in `X`

,
`y`

, or `Tbl`

indicate missing values, and
`fgls`

removes observations containing at least one
`NaN`

. That is, to remove `NaN`

s in `X`

or `y`

, `fgls`

merges the variables ```
[X
y]
```

, and then it uses list-wise deletion to remove any row that contains at least
one `NaN`

. `fgls`

also removes any row of
`Tbl`

containing at least one `NaN`

. Removing
`NaN`

s in the data reduces the sample size and can create irregular time
series.

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **`fgls(Tbl,ResponseVariable="GDP",InnovMdl="H4",Plot="all")`

provides coefficient, standard error, and residual mean squared error (RMSE) plots
of iterations of FGLS for a regression model with White’s robust innovations
covariance, and the table variable `GDP`

is the response while all
other variables are predictors.

`VarNames`

— Variable names to use in display

string vector | character vector | cell vector of strings | cell vector of character vectors

Variable names used in the display, specified as a string vector or
cell vector of strings of a length `numCoeffs`

:

If

`Intercept=true`

,`VarNames(1)`

is the name of the intercept (for example`'Const'`

) and`VarNames(`

specifies the name to use for variable+ 1)`j`

`X(:,`

or)`j`

`PredictorVariables(`

.)`j`

If

`Intercept=false`

,`VarNames(`

specifies the name to use for variable)`j`

`X(:,`

or)`j`

`PredictorVariables(`

.)`j`

The defaults is one of the following alternatives prepended by
`'Const'`

when an intercept is present in the
model:

**Example: **```
VarNames=["Const" "AGE"
"BBD"]
```

**Data Types: **`char`

| `cell`

| `string`

`Intercept`

— Flag to include intercept

`true`

(default) | `false`

Flag to include a model intercept, specified as a value in this table.

Value | Description |
---|---|

`true` | `fgls` includes an intercept term in the regression model. `numCoeffs` = `numPreds` + 1. |

`false` | `fgls` does not include an intercept when fitting the regression model. `numCoeffs` = `numPreds` . |

**Example: **`Intercept=false`

**Data Types: **`logical`

`InnovMdl`

— Model for innovations covariance estimate

`"AR"`

(default) | `"CLM"`

| `"HC0"`

| `"HC1"`

| `"HC2"`

| `"HC3"`

| `"HC4"`

| character vector

Model for the innovations covariance estimate, specified as a model name in the following table.

Set `InnovMdl`

to specify the structure of the
innovations covariance estimator $$\widehat{\Omega}$$.

For diagonal innovations covariance models (i.e., models with heteroscedasticity), $$\widehat{\Omega}=diag(\omega ),$$ where

*ω*= {*ω*;_{i}*i*= 1,...,*T*} is a vector of innovation variance estimates for the observations, and*T*=`numObs`

.`fgls`

estimates the data-driven vector*ω*using the corresponding model residuals (*ε*), their leverages $${h}_{i}={x}_{i}{({X}^{\prime}X)}^{-1}{x}_{i}^{\prime},$$ and the degrees of freedom*dfe*.Model Name Weight Reference `"CLM"`

$${\omega}_{i}=\frac{1}{dfe}{\displaystyle \sum}_{i=1}^{T}{\epsilon}_{i}^{2}$$

[4] `"HC0"`

$${\omega}_{i}={\epsilon}_{i}^{2}$$

[6] `"HC1"`

$${\omega}_{i}=\frac{T}{dfe}{\epsilon}_{i}^{2}$$

[5] `"HC2"`

$${\omega}_{i}=\frac{{\epsilon}_{i}^{2}}{1-{h}_{i}}$$

[5] `"HC3"`

$${\omega}_{i}=\frac{{\epsilon}_{i}^{2}}{{(1-{h}_{i})}^{2}}$$

[5] `"HC4"`

$${\omega}_{i}=\frac{{\epsilon}_{i}^{2}}{{(1-{h}_{i})}^{{d}_{i}}}$$

where $${d}_{i}=\mathrm{min}\left(4,\frac{{h}_{i}}{\overline{h}}\right)$$

[1] For full innovation covariance models (in other words, models having heteroscedasticity and autocorrelation), specify

`"AR"`

.`fgls`

imposes an AR(*p*) model on the innovations, and constructs $$\widehat{\Omega}$$ using the number of lags,*p*, specified by the name-value argument`arLags`

and the Yule-Walker equations.

If the `NumIter`

name-value argument is
`1`

and you specify the
`InnovCov0`

name-value argument,
`fgls`

ignores
`InnovMdl`

.

**Example: **`InnovMdl=HC0`

**Data Types: **`char`

| `string`

`ARLags`

— Number of lags

`1`

(default) | positive integer

Number of lags to include in the autoregressive (AR) innovations model, specified as a positive integer.

If the `InnovMdl`

name-value argument is not
`"AR"`

(that is, for diagonal models),
`fgls`

ignores
`ARLags`

.

For general ARMA innovations models, convert the innovations model to the equivalent AR form by performing one of the following actions.

Construct the ARMA innovations model lag operator polynomial using

`LagOp`

. Then, divide the AR polynomial by the MA polynomial using, for example,`mrdivide`

. The result is the infinite-order, AR representation of the ARMA model.Use

`arma2ar`

, which returns the coefficients of the infinite-order, AR representation of the ARMA model.

**Example: **`ARLags=4`

**Data Types: **`double`

`InnovCov0`

— Initial innovations covariance

`[]`

(default) | positive vector | positive definite matrix | positive semidefinite matrix

Initial innovations covariance, specified as a positive vector, positive semidefinite matrix, or a positive definite matrix.

`InnovCov0`

replaces the data-driven estimate of the innovations covariance ($$\widehat{\Omega}$$) in the first iteration of GLS.

For diagonal innovations covariance models (that is, models with heteroscedasticity), specify a

`numObs`

-by-1 vector.`InnovCov0(`

is the variance of innovation)`j`

.`j`

For full innovation covariance models (that is, models having heteroscedasticity and autocorrelation), specify a

`numObs`

-by-`numObs`

matrix.`InnovCov0(`

is the covariance of innovations,`j`

)`k`

and`j`

.`k`

By default, `fgls`

uses a data-driven $$\widehat{\Omega}$$ (see the `InnovMdl`

name-value
argument).

**Data Types: **`double`

`NumIter`

— Number of iterations

`1`

(default) | positive integer

Number of iterations to implement for the FGLS algorithm, specified as a positive integer.

`fgls`

estimates the innovations covariance $$\widehat{\Omega}$$ at each iteration from the residual series according to the
innovations covariance model `InnovMdl`

. Then, the software
computes the GLS estimates of the model coefficients.

**Example: **`NumIter=10`

**Data Types: **`double`

`ResCond`

— Flag to scale residuals

`false`

(default) | `true`

Flag to scale the residuals at each iteration of FGLS, specified as a value in this table.

Value | Description |
---|---|

`true` | `fgls` scales the residuals
at each iteration. |

`false` | `fgls` does not scale the
residuals at each iteration. |

**Tip**

The setting `ResCond=true`

can improve the
conditioning of the estimation of the innovations covariance $$\widehat{\Omega}$$.

**Data Types: **`logical`

`Display`

— Command window display control

`"off"`

(default) | `"final"`

| `"iter"`

| character vector

Command window display control, specified as a value in this table.

Value | Description |
---|---|

`"final"` | `fgls` displays the
final estimates. |

`"iter"` | `fgls` displays the
estimates after each iteration. |

`"off"` | `fgls` suppresses
command window display. |

`fgls`

shows estimation
results in tabular form.

**Example: **`Display="iter"`

**Data Types: **`char`

| `string`

`Plot`

— Control for plotting results

`"off"`

(default) | `"all"`

| `"coeff"`

| `"mse"`

| `"se"`

| character vector | string vector | cell array of character vectors

Control for plotting results after each iteration, specified as a value in the following table, or a string vector or cell array of character vectors of such values.

To examine the convergence of the FGLS algorithm, specify plotting the estimates for each iteration.

Value | Description |
---|---|

`"all"` | `fgls` plots the
estimated coefficients, their standard errors, and the residual mean-squared
error (MSE) on separate plots. |

`"coeff"` | `fgls` plots the
estimated coefficients. |

`"mse"` | `fgls` plots the
MSEs. |

`"off"` | `fgls` does not plot
the results. |

`"se"` | `fgls` plots the
estimated coefficient standard errors. |

**Example: **`Plot="all"`

**Example: **`Plot=["coeff" "se"]`

separately plots
iterative coefficient estimates and their standard
errors.

**Data Types: **`char`

| `string`

| `cell`

`ResponseVariable`

— Variable in `Tbl`

to use for response

first variable in `Tbl`

(default) | string vector | cell vector of character vectors | vector of integers | logical vector

Variable in `Tbl`

to use for response, specified as a string vector or cell vector of character vectors containing variable names in `Tbl.Properties.VariableNames`

, or an integer or logical vector representing the indices of names. The selected variables must be numeric.

`fgls`

uses the same specified response variable for all tests.

**Example: **`ResponseVariable="GDP"`

**Example: **`ResponseVariable=[true false false false]`

or
`ResponseVariable=1`

selects the first table variable as the
response.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

`PredictorVariables`

— Variables in `Tbl`

to use for the predictors

string vector | cell vector of character vectors | vector of integers | logical vector

Variables in `Tbl`

to use for the predictors, specified as a string vector or cell vector of character vectors containing variable names in `Tbl.Properties.VariableNames`

, or an integer or logical vector representing the indices of names. The selected variables must be numeric.

`fgls`

uses the same specified predictors for all tests.

By default, `fgls`

uses all variables in `Tbl`

that are not specified by the `ResponseVariable`

name-value
argument.

**Example: **`PredictorVariables=["UN" "CPI"]`

**Example: **`PredictorVariables=[false true true false]`

or
`DataVariables=[2 3]`

selects the second and third table
variables.

**Data Types: **`double`

| `logical`

| `char`

| `cell`

| `string`

## Output Arguments

`coeff`

— FGLS coefficient estimates

numeric vector

FGLS coefficient estimates, returned as a `numCoeffs`

-by-1 numeric
vector. `fgls`

returns `coeff`

when you
supply the inputs `X`

and `y`

.

Rows of `coeff`

correspond to the predictor matrix columns, with
the first row corresponding to the intercept when `Intercept=true`

.
For example, in a model with an intercept, the value of $${\widehat{\beta}}_{1}$$ (corresponding to the predictor
*x*_{1}) is in position 2 of
`coeff`

.

`se`

— Coefficient standard error estimates

numeric vector

Coefficient standard error estimates, returned as a
`numCoeffs`

-by-1 numeric. The elements of `se`

are
`sqrt(diag(EstCoeffCov))`

. `fgls`

returns
`se`

when you supply the inputs `X`

and
`y`

.

Rows of `se`

correspond to the predictor matrix columns, with the
first row corresponding to the intercept when `Intercept=true`

. For
example, in a model with an intercept, the estimated standard error of $${\widehat{\beta}}_{1}$$ (corresponding to the predictor
*x*_{1}) is in position 2 of
`se`

, and is the square root of the value in position (2,2) of
`EstCoeffCov`

.

`EstCoeffCov`

— Coefficient covariance matrix estimate

numeric matrix

Coefficient covariance matrix estimate, returned as a
`numCoeffs`

-by-`numCoeffs`

numeric matrix.
`fgls`

returns `EstCoeffCov`

when you
supply the inputs `X`

and `y`

.

Rows and columns of `EstCoeffCov`

correspond to the predictor
matrix columns, with the first row and column corresponding to the intercept when
`Intercept=true`

. For example, in a model with an intercept, the
estimated covariance of $${\widehat{\beta}}_{1}$$ (corresponding to the predictor
*x*_{1}) and $${\widehat{\beta}}_{2}$$ (corresponding to the predictor
*x*_{2}) are in positions (2,3) and (3,2) of
`EstCoeffCov`

, respectively.

`CoeffTbl`

— FGLS coefficient estimates and standard errors

table

FGLS coefficient estimates and standard errors, returned as a
`numCoeffs`

-by-2 table. `fgls`

returns
`CoeffTbl`

when you supply the input
`Tbl`

.

For * j* = 1,…,

`numCoeffs`

, row
*of*

`j`

`CoeffTbl`

contains estimates of
coefficient *in the regression model and it has label*

`j`

`VarNames(``j`

)

. The first variable
`Coeff`

contains the coefficient estimates `coeff`

and the second variable `SE`

contains the standard errors
`se`

.`CovTbl`

— Coefficient covariance matrix estimate

table

Coefficient covariance matrix estimate, returned as a
`numCoeffs`

-by-`numCoeffs`

table containing the
coefficient covariance matrix estimate `EstCoeffCov`

.
`fgls`

returns `CovTbl`

when you supply
the input `Tbl`

.

For each pair (* i*,

*),*

`j`

`CovTbl(``i`

,`j`

)

contains the covariance estimate of coefficients *and*

`i`

*in the regression model. The label of row and variable*

`j`

*is*

`j`

`VarNames(``j`

)

,
*= 1,…,*

`j`

`numCoeffs`

.`iterPlots`

— Handles to plotted graphics objects

structure array of graphics objects

Handles to plotted graphics objects, returned as a structure array of graphics
objects. `iterPlots`

contains unique plot identifiers, which you can
use to query or modify properties of the plot.

`iterPlots`

is not available if the value of the
`Plot`

name-value argument is `"off"`

.

## More About

### Feasible Generalized Least Squares

*Feasible generalized least squares* (FGLS) estimates the coefficients of a multiple linear regression model and their covariance matrix in the presence of nonspherical innovations with an unknown covariance matrix.

Let *y _{t}* =

*X*

_{t}*β*+

*ε*be a multiple linear regression model, where the innovations process

_{t}*ε*is Gaussian with mean 0, but with true, nonspherical covariance matrix

_{t}*Ω*(for example, the innovations are heteroscedastic or autocorrelated). Also, suppose that the sample size is

*T*and there are

*p*predictors (including an intercept). Then, the FGLS estimator of

*β*is

$${\widehat{\beta}}_{FGLS}={\left({X}^{\top}{\widehat{\Omega}}^{-1}X\right)}^{-1}{X}^{\top}{\widehat{\Omega}}^{-1}y,$$

where $$\widehat{\Omega}$$ is an innovations covariance estimate based on a model (e.g., innovations process forms an AR(1) model). The estimated coefficient covariance matrix is

$${\widehat{\Sigma}}_{FGLS}={\widehat{\sigma}}_{FGLS}^{2}{\left({X}^{\top}{\widehat{\Omega}}^{-1}X\right)}^{-1},$$

where

$${\widehat{\sigma}}_{FGLS}^{2}=\frac{{y}^{\top}\left[{\widehat{\Omega}}^{-1}-{\widehat{\Omega}}^{-1}X{\left({X}^{\top}{\widehat{\Omega}}^{-1}X\right)}^{-1}{X}^{\top}{\widehat{\Omega}}^{-1}\right]y}{T-p}.$$

FGLS estimates are computed as follows:

OLS is applied to the data, and then residuals $$\left({\widehat{\epsilon}}_{t}\right)$$ are computed.

$$\widehat{\Omega}$$ is estimated based on a model for the innovations covariance.

$${\widehat{\beta}}_{FGLS}$$ is estimated, along with its covariance matrix $${\widehat{\Sigma}}_{FGLS}.$$

Optional: This process can be iterated by performing the following steps until $${\widehat{\beta}}_{FGLS}$$ converges.

Compute the residuals of the fitted model using the FGLS estimates.

Apply steps 2–3.

If $$\widehat{\Omega}$$ is a consistent estimator of $$\Omega $$ and the predictors that comprise *X* are exogenous, then FGLS estimators are consistent and efficient.

Asymptotic distributions of FGLS estimators are unchanged by repeated iteration. However, iterations might change finite sample distributions.

### Generalized Least Squares

*Generalized least squares* (GLS) estimates the coefficients of a multiple linear regression model and their covariance matrix in the presence of nonspherical innovations with known covariance matrix.

The setup and process for obtaining GLS estimates is the same as in FGLS, but replace $$\widehat{\Omega}$$ with the known innovations covariance matrix $$\Omega $$.

In the presence of nonspherical innovations, and with known innovations covariance, GLS estimators are unbiased, efficient, and consistent, and hypothesis tests based on the estimates are valid.

### Weighted Least Squares

*Weighted least squares* (WLS) estimates the
coefficients of a multiple linear regression model and their covariance matrix in the
presence of uncorrelated but heteroscedastic innovations with known, diagonal covariance
matrix.

The setup and process to obtain WLS estimates is the same as in FGLS, but replace $$\widehat{\Omega}$$ with the known, diagonal matrix of weights. Typically, the diagonal elements are the inverse of the variances of the innovations.

In the presence of heteroscedastic innovations, and when the variances of the innovations are known, WLS estimators are unbiased, efficient, and consistent, and hypothesis tests based on the estimates are valid.

## Tips

To obtain standard generalized least squares (GLS) estimates:

To obtain weighted least squares (WLS) estimates, set the

`InnovCov0`

name-value argument to a vector of inverse weights (e.g., innovations variance estimates).In specific models and with repeated iterations, scale differences in the residuals might produce a badly conditioned estimated innovations covariance and induce numerical instability. Conditioning improves when you set

`ResCond=true`

.

## Algorithms

In the presence of nonspherical innovations, GLS produces efficient estimates relative to OLS and consistent coefficient covariances, conditional on the innovations covariance. The degree to which

`fgls`

maintains these properties depends on the accuracy of both the model and estimation of the innovations covariance.Rather than estimate FGLS estimates the usual way,

`fgls`

uses methods that are faster and more stable, and are applicable to rank-deficient cases.Traditional FGLS methods, such as the Cochrane-Orcutt procedure, use low-order, autoregressive models. These methods, however, estimate parameters in the innovations covariance matrix using OLS, where

`fgls`

uses maximum likelihood estimation (MLE) [2].

## References

[1] Cribari-Neto, F. "Asymptotic Inference Under Heteroskedasticity of Unknown Form." *Computational Statistics & Data Analysis*. Vol. 45, 2004, pp. 215–233.

[2] Hamilton, James D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[3] Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lϋtkepohl, and T. C. Lee. *The Theory and Practice of Econometrics*. New York, NY: John Wiley & Sons, Inc., 1985.

[4] Kutner, M. H., C. J. Nachtsheim, J. Neter, and W. Li. *Applied Linear Statistical Models*. 5th ed. New York: McGraw-Hill/Irwin, 2005.

[5] MacKinnon, J. G., and H. White. "Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties." *Journal of Econometrics*. Vol. 29, 1985, pp. 305–325.

[6] White, H. "A Heteroskedasticity-Consistent Covariance Matrix and a Direct Test for Heteroskedasticity." *Econometrica*. Vol. 48, 1980, pp. 817–838.

## Version History

**Introduced in R2014b**

### R2022a: `fgls`

returns estimates in tables when you supply a table of data

If you supply a table of time series data `Tbl`

,
`fgls`

returns the following outputs:

In the first position,

`fgls`

returns the table`CoeffTbl`

containing variables for coefficient estimates`Coeff`

and standard errors`SE`

with rows corresponding to, and labeled as,`VarNames`

.In the second position,

`fgls`

returns a table containing the estimated coefficient covariances`CovTbl`

with rows and variables corresponding to, and labeled as,`VarNames`

.

Before R2022a, `fgls`

returned the numeric outputs in separate
positions of the output when you supplied a table of input data.

Starting in R2022a, if you supply a table of input data, update your code to return all outputs in the first through third output positions.

[CoeffTbl,CovTbl,iterPlots] = fgls(Tbl,Name=Value)

If you request more outputs, `fgls`

issues an error.

Also, access results by using table indexing. For more details, see Access Data in Tables.

## See Also

### Functions

### Objects

### Topics

- Classical Model Misspecification Tests
- Time Series Regression I: Linear Models
- Time Series Regression VI: Residual Diagnostics
- Time Series Regression X: Generalized Least Squares and HAC Estimators
- Autocorrelation and Partial Autocorrelation
- Engle’s ARCH Test
- Nonspherical Models
- Time Series Regression Models

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