Load the Data

Load the Canadian electric power consumption data set from the World Bank. The response is Canada's electrical energy consumption in kWh (DataTable.consump), the predictor is Canada's GDP in year 2000 USD (DataTable.gdp), and the data set also contains the GDP deflator (DataTable.gdp_deflator). Because DataTable is a timetable, DataTable.Time is the sample year.

load Data_PowerConsumption

Define the Model

Model the behavior of the annual difference in electrical energy consumption with respect to real GDP as a linear model:

consumpDiff = DataTable.consump - lagmatrix(DataTable.consump,1); ...
    % Annual difference in consumption
T = size(consumpDiff,1);
consumpDiff = consumpDiff(2:end)/1.0e+10; 
    % Scale for numerical stability
rGDP = DataTable.gdp./(DataTable.gdp_deflator);    % Deflate GDP
rGDP = rGDP(2:end)/1.0e+10;   % Scale for numerical stability
rGDPdes = [ones(T-1,1) rGDP]; % Design matrix

Mdl = fitlm(rGDP,consumpDiff);
coeff = Mdl.Coefficients(:,1);
EstParamCov = Mdl.CoefficientCovariance;
resid = Mdl.Residuals.Raw;

Plot the Data

Plot the difference in energy consumption, consumpDiff versus the real GDP, to check for possible heteroscedasticity.

figure
plot(rGDP,consumpDiff,'.')
title 'Annual Difference in Energy Consumption vs real GDP - Canada';
xlabel 'real GDP (year 2000 USD)';
ylabel 'Annual Difference in Energy Consumption (kWh)';

The figure indicates that heteroscedasticity might be present in the annual difference in energy consumption. As real GDP increases, the annual difference in energy consumption seems to be less variable.

Plot the residuals.

Plot the residuals from Mdl against the fitted values and year to assess heteroscedasticity and autocorrelation.

figure
subplot(2,1,1)
hold on
plot(Mdl.Fitted,resid,'.')
plot([min(Mdl.Fitted) max(Mdl.Fitted)],[0 0],'k-')
title 'Residual Plots';
xlabel 'Fitted Consumption';
ylabel 'Residuals';
axis tight
hold off
subplot(2,2,3)
autocorr(resid)
h1 = gca;
h1.FontSize = 8;
subplot(2,2,4)
parcorr(resid)
h2 = gca;
h2.FontSize = 8;

The residual plot reveals decreasing residual variance with increasing fitted consumption. The autocorrelation function shows that autocorrelation might be present in the first few lagged residuals.

Test for heteroscedasticity and autocorrelation.

Test for conditional heteroscedasticity using Engle's ARCH test. Test for autocorrelation using the Ljung-Box Q test. Test for overall correlation using the Durbin-Watson test.

[~,englePValue] = archtest(resid);
englePValue

englePValue = 0.1463

[~,lbqPValue] = lbqtest(resid,'lags',1:3);...
    % Significance of first three lags
lbqPValue

lbqPValue = 1×3

    0.0905    0.1966    0.0522

[dwPValue] = dwtest(Mdl);
dwPValue

dwPValue = 0.0024

The p value of Engle's ARCH test suggests significant conditional heteroscedasticity at 15% significance level. The p value for the Ljung-Box Q test suggests significant autocorrelation with the first and third lagged residuals at 10% significance level. The p value for the Durbin-Watson test suggests that there is strong evidence for overall residual autocorrelation. The results of the tests suggest that the standard linear model conditions of homoscedasticity and uncorrelated errors are violated, and inferences based on the OLS coefficient covariance matrix are suspect.

One way to proceed with inference (such as constructing a confidence band) is to correct the OLS coefficient covariance matrix by estimating the Newey-West coefficient covariance.

Estimate the Newey-West coefficient covariance.

Correct the OLS coefficient covariance matrix by estimating the Newey-West coefficient covariance using hac. Compute the maximum lag to be weighted for the standard Newey-West estimate, maxLag (Newey and West, 1994). Use hac to estimate the standard Newey-West coefficient covariance.

maxLag = floor(4*(T/100)^(2/9));
[NWEstParamCov,~,NWCoeff] = hac(Mdl,'type','hac',...
    'bandwidth',maxLag + 1);

Estimator type: HAC
Estimation method: BT
Bandwidth: 4.0000
Whitening order: 0
Effective sample size: 49
Small sample correction: on

Coefficient Covariances:

       |  Const      x1   
--------------------------
 Const |  0.3720  -0.2990 
 x1    | -0.2990   0.2454 

The Newey-West standard error for the coefficient of rGDP, labeled $$x_1$$ in the table, is less than the usual OLS standard error. This suggests that, in this data set, correcting for residual heteroscedasticity and autocorrelation increases the precision in measuring the linear effect of real GDP on energy consumption.

Calculate the Working-Hotelling confidence bands.

Compute the 95% Working-Hotelling confidence band for each covariance estimate using nlpredci (Kutner et al., 2005).

modelfun = @(b,x)(b(1)*x(:,1)+b(2)*x(:,2));
    % Define the linear model
[beta,nlresid,~,EstParamCov] = nlinfit(rGDPdes,...
    consumpDiff,modelfun,[1,1]); % estimate the model
[fity,fitcb] = nlpredci(modelfun,rGDPdes,beta,nlresid,...
    'Covar',EstParamCov,'SimOpt','on');
    % Margin of errors
conbandnl = [fity - fitcb fity + fitcb]; 
    % Confidence bands
[fity,NWfitcb] = nlpredci(modelfun,rGDPdes,...
    beta,nlresid,'Covar',NWEstParamCov,'SimOpt','on');
    % Corrected margin of error
NWconbandnl = [fity - NWfitcb fity + NWfitcb];
    % Corrected confidence bands

Plot the Working-Hotelling confidence bands.

Plot the Working-Hotelling confidence bands on the same axes twice: one plot displaying electrical energy consumption with respect to real GDP, and the other displaying the electrical energy consumption time series.

figure
hold on
l1 = plot(rGDP,consumpDiff,'k.');
l2 = plot(rGDP,fity,'b-','LineWidth',2);
l3 = plot(rGDP,conbandnl,'r-');
l4 = plot(rGDP,NWconbandnl,'g--');
title 'Data with 95% Working-Hotelling Conf. Bands';
xlabel 'real GDP (year 2000 USD)';
ylabel 'Consumption (kWh)';
axis([0.7 1.4 -2 2.5])
legend([l1 l2 l3(1) l4(1)],'Data','Fitted','95% Conf. Band',...
    'Newey-West 95% Conf. Band','Location','SouthEast')
hold off

figure
year = DataTable.Time(2:end);
hold on
l1 = plot(year,consumpDiff);
l2 = plot(year,fity,'k-','LineWidth',2);
l3 = plot(year,conbandnl,'r-');
l4 = plot(year,NWconbandnl,'g--');
title 'Consumption with 95% Working-Hotelling Conf. Bands';
xlabel 'Year';
ylabel 'Consumption (kWh)';
legend([l1 l2 l3(1) l4(1)],'Consumption','Fitted',...
    '95% Conf. Band','Newey-West 95% Conf. Band',...
    'Location','SouthWest')
hold off

The plots show that the Newey-West estimator accounts for the heteroscedasticity in that the confidence band is wide in areas of high volatility, and thin in areas of low volatility. The OLS coefficient covariance estimator ignores this pattern of volatility.

References: