chowtest
Chow test for structural change
Syntax
Description
returns a vector of test decisions from conducting Chow
tests on the multiple linear regression model h
= chowtest(X
,y
,bp
)y
=
X
β + ε at specified
break points. y
is a vector of response data and
X
is a matrix of predictor data. Each element of
bp
results in a separate test.
returns the table StatTbl
= chowtest(Tbl
,bp
)StatTbl
containing variables for the test
results, statistics, and settings from conducting Chow tests on the variables of the
table or timetable Tbl
. Each row of
StatTbl
contains the results of the corresponding
test.
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
namevalue argument.
To select different predictor variables, use the PredictorNames
namevalue argument.
___ = chowtest(___,
specifies options using one or more namevalue arguments in
addition to any of the input argument combinations in previous syntaxes.
Name=Value
)chowtest
returns the output argument combination for the
corresponding input arguments.
In addition to bp
, some options control the number of tests
to conduct. For example,
chowtest(Tbl,ResponseVariable="GDP",Test=["breakpoint"
"forecast"],Intercept=false)
conducts two tests for the presence of a
structural break in the coefficients of the regression model of
GDP
on all other variables of the table
Tbl
without an intercept term. The first test assesses
coefficient equality constraints directly, and the second test assesses forecast
performance.
Examples
Conduct Chow Test for Structural Change
Conduct Chow tests to assess whether there are structural changes in the equation for food demand around World War II. Input the predictor series as a matrix and input the response series as a vector.
Load the US food consumption data set Data_Consumption.mat
, which contains annual measurements from 1927 through 1962 with missing data due to the war in the matrix Data
.
load Data_Consumption
Suppose that you want to develop a model for consumption as determined by food prices and disposable income, and assess its stability through the economic shock through the war.
Plot the series.
P = Data(:,1); % Food price index I = Data(:,2); % Disposable income index Q = Data(:,3); % Food consumption index figure; plot(dates,[P I Q]) axis tight grid on xlabel("Year") ylabel("Index") legend(["Price" "Income" "Consumption"],Location="southeast")
Measurements are missing from 1942 through 1947, which correspond to World War II.
Stabilize each series by applying the log transformation.
LP = log(P); LI = log(I); LQ = log(Q);
Assume that log consumption is a linear function of the logs of food price and income.
$${\text{LQ}}_{t}={\beta}_{0}+{\beta}_{1}{\text{LI}}_{t}+{\beta}_{2}\text{LP}+{\epsilon}_{t}.$$
$${\epsilon}_{t}$$ is a Gaussian random variable with mean 0 and standard deviation $${\sigma}^{2}$$.
Identify the indices before World War II. Plot log consumption with respect to the logs of food price and income.
preWarIdx = (dates <= 1941); figure scatter3(LP(preWarIdx),LI(preWarIdx),LQ(preWarIdx),[],"ro"); hold on scatter3(LP(~preWarIdx),LI(~preWarIdx),LQ(~preWarIdx),[],"b*"); legend(["Prewar observations" "Postwar observations"], ... Location="best") xlabel("Log Price") ylabel("Log Income") zlabel("Log Consumption") % Obtain better view h = gca; h.CameraPosition = [4.3 12.2 5.3];
Data relationships appear to be affected by the war.
Conduct two break point Chow tests at 5% level of significance. For the first test, set the break point at 1941. Set the break point of the other test at 1948.
bp = find(preWarIdx,1,"last");
X = [LP LI];
y = LQ;
h1941 = chowtest(X,y,bp)
h1941 = logical
1
h1948 = chowtest(X,y,bp + 1)
h1948 = logical
0
h1941 = 1
indicates that there is enough evidence to reject the null hypothesis that the coefficients are stable when the break points occur before the war. However, h1948 = 0
indicates that there is not enough evidence to reject coefficient stability if the break point is after the war. This result suggests that the data at 1948 is influential.
Alternatively, you can supply a vector of break points to conduct three Chow tests.
h = chowtest(X,y,[bp bp+1]);
RESULTS SUMMARY *************** Test 1 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: All Statistic: 5.5400 Critical value: 3.0088 P value: 0.0049 Significance level: 0.0500 Decision: Reject coefficient stability *************** Test 2 Sample size: 30 Breakpoint: 16 Test type: breakpoint Coefficients tested: All Statistic: 1.2942 Critical value: 3.0088 P value: 0.2992 Significance level: 0.0500 Decision: Fail to reject coefficient stability
By default, chowtest
displays a summary of the test results for each test when you conduct more than one test.
Return Test pValues and Decision Statistics
Load the US food consumption data set Data_Consumption.mat
. Consider a model for log food consumption as determined by log food prices and log disposable income.
load Data_Consumption
X = log(Data(:,1:2));
y = log(Data(:,3));
Conduct two break point Chow tests at 5% level of significance. For the first test, set the break point at 1941. Set the break point of the other test at 1948. Return the test decision, $\mathit{p}$Value, test statistic, and test critical value.
bp = find(dates <= 1941,1,"last");
[h,pValue,stat,cValue] = chowtest(X,y,bp)
h = logical
1
pValue = 0.0049
stat = 5.5400
cValue = 3.0088
pValue
< 0.01, which suggests that the evidence to reject the null hypothesis that all coefficients in the regression models, determined by the break point at 1941, are equal.
Conduct Chow Test for Structural Change on Table Variables
Conduct Chow tests to assess whether there are structural changes in the equation for food demand around World War II, where the time series are variables in a table.
Load the US food consumption data set Data_Consumption.mat
, which contains annual measurements from 1927 through 1962 with missing data due to the war in the table DataTable
. Convert the table to a timetable, and remove rows containing missing values.
load Data_Consumption
dates = datetime(dates,12,31);
TT = table2timetable(DataTable,RowTimes=dates);
TT.Row = [];
TT = rmmissing(TT);
Apply the log transform to all variables in the table.
LogTT = varfun(@log,TT); LogTT.Properties.VariableNames
ans = 1x3 cell
{'log_P'} {'log_I'} {'log_Q'}
Conduct two break point Chow tests at 5% level of significance. For the first test, set the break point at the end of 1941. Set the break point of the other test at the end of 1948.
bp1941 = find(LogTT.Time >= datetime(1941,12,31),1); bp1948 = find(LogTT.Time >= datetime(1948,12,31),1); bp = [bp1941 bp1948]; StatTbl = chowtest(LogTT,bp)
RESULTS SUMMARY *************** Test 1 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: All Statistic: 5.5400 Critical value: 3.0088 P value: 0.0049 Significance level: 0.0500 Decision: Reject coefficient stability *************** Test 2 Sample size: 30 Breakpoint: 16 Test type: breakpoint Coefficients tested: All Statistic: 1.2942 Critical value: 3.0088 P value: 0.2992 Significance level: 0.0500 Decision: Fail to reject coefficient stability
StatTbl=2×8 table
h pValue stat cValue Break Point Alpha Intercept Test
_____ _________ ______ ______ ___________ _____ _________ ______________
Test 1 true 0.0049125 5.54 3.0088 15 0.05 true {'breakpoint'}
Test 2 false 0.29918 1.2942 3.0088 16 0.05 true {'breakpoint'}
StatTbl
contains the decision statistics and options for each test (row).
By default, chowtest
selects the last table variable as the response, and selects all other variables as predictors. You can select a different variable by using the ResponseVariable
namevalue argument. You can choose a different set of predictor variables by using the PredictorVariables
namevalue argument.
Test Model of Real U.S. GNP for Structural Change
Apply the Chow test to assess the stability of an explanatory model of US real gross national product (RGNP) using the end of World War II as a break point.
Load the NelsonPlosser data set Data_NelsonPlosser.mat
, which contains the table of data DataTable
.
load Data_NelsonPlosser
The time series in the data set contain annual, macroeconomic measurements from 1860 to 1970. For more details, a list of variables, and descriptions, enter Description
in the command line.
Convert the table to a timetable. Focus the sample to measurements from the end of 1915 through the end of 1970.
dates = datetime(dates,12,31);
span = isbetween(dates,datetime(1915,12,31),datetime(1970,12,31),"closed");
TT = table2timetable(DataTable,RowTimes=dates);
TT.Dates = [];
TT = TT(span,:);
Consider a predictive model of the US RGNP GNPR
given measurements of the industrial production index IPI
, total employment E
, and real wages WR
.
Plot the series in the model.
prednames = ["IPI" "E" "WR"]; tiledlayout(2,2) for j = ["GNPR" prednames] nexttile plot(TT.Time,TT{:,j}) ylabel(j) end
To address exponential growth, apply the log transform to the series.
LogTT = varfun(@log,TT);
LogTT
is a timetable containing the transformed variables in TT
, but with names prepended with log_
.
Select the index corresponding to the end of World War II, September 2, 1945.
bp = find(LogTT.Time > datetime(1945,9,2),1);
Assume that an appropriate multiple regression model to describe real GNP is
$$\mathrm{log}({\text{GNPR}}_{t})={\beta}_{0}+{\beta}_{1}\mathrm{log}({\text{IPI}}_{t})+{\beta}_{2}\mathrm{log}({\text{E}}_{t})+{\beta}_{3}\mathrm{log}({\text{WR}}_{t}).$$
Conduct a break point test to assess whether all regression coefficients are stable. Use the end of WWII as a break point. Print a test summary to the command line.
lprednames = "log_" + prednames; StatTbl = chowtest(LogTT,bp,ResponseVariable="log_GNPR", ... PredictorVariables=lprednames,Display="summary")
RESULTS SUMMARY *************** Test 1 Sample size: 56 Breakpoint: 31 Test type: breakpoint Coefficients tested: All Statistic: 4.0978 Critical value: 2.5652 P value: 0.0062 Significance level: 0.0500 Decision: Reject coefficient stability
StatTbl=1×8 table
h pValue stat cValue Break Point Alpha Intercept Test
_____ _________ ______ ______ ___________ _____ _________ ______________
Test 1 true 0.0061633 4.0978 2.5652 31 0.05 true {'breakpoint'}
StatTbl
contains decision statistics and test options for the test. StatTbl.h = 1
and StatTbl.pValue < 0.01
indicate string evidence to reject the null hypothesis that the regression coefficients before and after WWII are equivalent.
Assess Stability of Subsets of Regression Coefficients
Conduct a Chow test to assess the stability of a subset of regression coefficients. This example expands on Conduct Chow Test for Structural Change.
Load the US food consumption data set. Convert the table to a timetable, and remove rows containing missing values.
load Data_Consumption.mat
dates = datetime(dates,12,31);
TT = table2timetable(DataTable,RowTimes=dates);
TT.Row = [];
TT = rmmissing(TT);
Apply the log transformation to each series.
LogTT = varfun(@log,DataTable);
Identify the indices before World War II.
preWarIdx = dates <= datetime(1941,12,31);
Consider two regression models: one is log consumption onto log food price, and the other is log consumption onto log income. Plot scatter plots and regression lines for both models.
figure plot(LogTT.log_P(preWarIdx),LogTT.log_Q(preWarIdx),"bo", ... LogTT.log_P(~preWarIdx),LogTT.log_Q(~preWarIdx),"r*"); axis tight grid on lsline xlabel("Log Price") ylabel("Log Consumption") legend("Prewar observations","Postwar observations")
figure plot(LogTT.log_I(preWarIdx),LogTT.log_Q(preWarIdx),"bo", ... LogTT.log_I(~preWarIdx),LogTT.log_Q(~preWarIdx),"r*"); axis tight grid on lsline xlabel("Log Income") ylabel("Log Consumption") legend("Prewar observations","Postwar observations", ... Location="northwest")
A clear break in food price elasticity exists between subsamples before and after the war. However, income elasticity does not appear to have such a break.
Conduct two Chow tests to determine whether there is statistical evidence to reject model continuity for both regression models. Because there are more observations in the complementary subsample than coefficients, conduct a break point test. Consider the elasticities in the test only. That is, specify false
for the intercept (first coefficient) and true
for elasticity (second coefficient).
bp = find(preWarIdx,1,"last"); % Index for 1941 chowtest(LogTT,bp,Coeffs=[false true],Display="summary", ... ResponseVariable="log_Q",PredictorVariables="log_P");
RESULTS SUMMARY *************** Test 1 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: 0 1 Statistic: 7.3947 Critical value: 4.2252 P value: 0.0115 Significance level: 0.0500 Decision: Reject coefficient stability
chowtest(LogTT,bp,Coeffs=[false true],Display="summary", ... ResponseVariable="log_Q",PredictorVariables="log_I");
RESULTS SUMMARY *************** Test 1 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: 0 1 Statistic: 0.1289 Critical value: 4.2252 P value: 0.7225 Significance level: 0.0500 Decision: Fail to reject coefficient stability
The first test rejects the null hypothesis that price elasticities are equivalent across subsamples at 5% level of significance. The second test fails to reject the null hypothesis that income elasticities are equivalent across subsamples.
Consider a regression model of log consumption onto the logs of price and income. Conduct two break point tests: one that compares price elasticity across subsamples only, and another that compares income elasticity only.
Coeffs = [false true false; false false true]; chowtest(LogTT,bp,Coeffs=Coeffs,Display="summary", ... ResponseVariable="log_Q",PredictorVariables=["log_P" "log_I"]);
RESULTS SUMMARY *************** Test 1 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: 0 1 0 Statistic: 0.0001 Critical value: 4.2597 P value: 0.9920 Significance level: 0.0500 Decision: Fail to reject coefficient stability *************** Test 2 Sample size: 30 Breakpoint: 15 Test type: breakpoint Coefficients tested: 0 0 1 Statistic: 2.8151 Critical value: 4.2597 P value: 0.1064 Significance level: 0.0500 Decision: Fail to reject coefficient stability
For both tests, there is not enough evidence to reject model stability at 5% level.
Model Structural Change
Simulate data for a linear model including a structural break in the intercept and one of the predictor coefficients. Then, choose specific coefficients to test for equality across a break point using the Chow test. Adjust parameters to assess the sensitivity of the Chow test.
Specify four predictors, 50 observations, and a break point at period 44 for the simulated linear model.
numPreds = 4;
numObs = 50;
bp = 44;
rng(1); % For reproducibility
Form the predictor data by specifying means for the predictors, and then adding random, standard Gaussian noise to each of the means.
mu = [0 1 2 3]; X = repmat(mu,numObs,1) + randn(numObs,numPreds);
Include a column of ones to the predictor data.
X = [ones(numObs,1) X];
Specify the true values of the regression coefficients and that the intercept and the coefficient of the second predictor jump by 10%.
beta1 = [1 2 3 4 5]'; % Initial subsample coefficients beta2 = beta1 + [beta1(1)*0.1 0 beta1(3)*0.1 0 0 ]'; % Complementary subsample coefficients X1 = X(1:bp,:); % Initial subsample predictors X2 = X(bp+1:end,:); % Complementary subsample predictors
Specify a 2by5 logical matrix that indicates to test first the intercept and second regression coefficient, and then test all other coefficients.
test1 = [true false true false false]; Coeffs = [test1; ~test1]
Coeffs = 2x5 logical array
1 0 1 0 0
0 1 0 1 1
The null hypothesis for the first test (Coeffs(1,:)
) is equality of the intercepts and the coefficients of the second predictor across subsamples. The null hypothesis for the second test (Coeffs(2,:)
) is equality of the first, third, and fourth predictors across subsamples.
Simulate data for the linear model
$$\text{y}=\left[\begin{array}{cc}X1& 0\\ 0& X2\end{array}\right]\left[\begin{array}{c}\text{beta1}\\ \text{beta2}\end{array}\right]+\text{innov}.$$
Create innov
as a vector of random Gaussian variates with mean zero and standard deviation 0.2.
sigma = 0.2;
innov = sigma*randn(numObs,1);
y = [X1 zeros(bp,size(X2,2)); ...
zeros(numObsbp,size(X1,2)) X2]*[beta1; beta2]+innov;
Conduct the two break point tests indicated in Coeffs
. Because there is an intercept in the predictor matrix X
already, specify to suppress its inclusion in the linear model that chowtest
fits.
chowtest(X,y,bp,Intercept=false,Coeffs=Coeffs, ... Display="summary");
RESULTS SUMMARY *************** Test 1 Sample size: 50 Breakpoint: 44 Test type: breakpoint Coefficients tested: 1 0 1 0 0 Statistic: 5.7102 Critical value: 3.2317 P value: 0.0066 Significance level: 0.0500 Decision: Reject coefficient stability *************** Test 2 Sample size: 50 Breakpoint: 44 Test type: breakpoint Coefficients tested: 0 1 0 1 1 Statistic: 0.2497 Critical value: 2.8387 P value: 0.8611 Significance level: 0.0500 Decision: Fail to reject coefficient stability
At the default significance level:
The Chow test correctly rejects the null hypothesis that no structural breaks exist at period
bp
for the intercept and the second coefficient.The Chow test correctly failed to reject the null hypothesis for the other coefficients.
Compare the break point test results to the results of the forecast test.
chowtest(X,y,bp,Intercept=false,Coeffs=Coeffs, ... Test="forecast",Display="summary");
RESULTS SUMMARY *************** Test 1 Sample size: 50 Breakpoint: 44 Test type: forecast Coefficients tested: 1 0 1 0 0 Statistic: 3.7637 Critical value: 2.8451 P value: 0.0182 Significance level: 0.0500 Decision: Reject coefficient stability *************** Test 2 Sample size: 50 Breakpoint: 44 Test type: forecast Coefficients tested: 0 1 0 1 1 Statistic: 0.2135 Critical value: 2.6123 P value: 0.9293 Significance level: 0.0500 Decision: Fail to reject coefficient stability
In this case, the inferences from the tests are equivalent to those for the break point test.
Input Arguments
X
— Predictor data X
numeric matrix
Predictor data X for the multiple linear regression model,
specified as a numObs
bynumPreds
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
by1 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
namevalue argument. To select different
predictor variables, use the PredictorNames
namevalue argument to
select numPreds
predictors.
bp
— Break points
positive integer  vector of positive integers
Break points for the tests, specified as a positive integer or a vector of positive integers.
Each break point is an index of a specific observation (row) in the data, after
chowtest
removes missing
(NaN
) values. The element bp(j)
specifies to split the data into the initial and complementary samples
indexed by 1:bp(j)
and (bp(j) +
1):numObs
, respectively.
Data Types: double
Note
NaN
s inX
,y
, orTbl
indicate missing values, andchowtest
removes observations containing at least oneNaN
. That is, to removeNaN
s inX
ory
,chowtest
merges the variables[X y]
, and then it uses listwise deletion to remove any row that contains at least oneNaN
.chowtest
also removes any row ofTbl
containing at least oneNaN
. RemovingNaN
s in the data reduces the sample size and can create irregular time series.If
bp
is a scalar, then the number of tests,numTests
, is the common dimension of values in namevalue argument. In this case,chowtest
uses the samebp
in each test. Otherwise, the length ofbp
determinesnumTests
, andchowtest
runs separate tests for each value inbp
.
NameValue Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Namevalue 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: chowtest(Tbl,DataVariable="GDP",Test=["breakpoint"
"forecast"],Intercept=true)
conducts two tests for the presence of a
structural break in the coefficients, including an intercept, of the regression
model of GDP
on all other variables of the table
Tbl
. The first test assesses coefficient equality constraints
directly, and the second test assesses forecast performance.
Intercept
— Flag to include intercept
true
(default)  false
 logical vector
Flag to include an intercept when chowtest
fits the regression
model, specified as a value in this table or a length numTests
vector
of such values.
Value  Description 

true  chowtest includes an intercept when
fitting the regression model. numCoeffs =
numPreds + 1. 
false  chowtest does not include an intercept
when fitting the regression model. numCoeffs =
numPreds . 
chowtest
conducts a separate test for each value
in Intercept
.
Example: Intercept=false
excludes an intercept from the model for
each test.
Data Types: logical
Test
— Chow test type
"breakpoint"
(default)  "forecast"
 character vector  string vector  cell vector of character vectors
Chow test type, specified as a test name, or a string vector or cell vector of test names. This table contains the supported test names.
Test Name  Description 

"breakpoint" (default) 

"forecast" 

For details on the value of
numCoeffs
, see the Intercept
and
Coeffs
namevalue arguments.
chowtest
conducts a separate test for each
test name in Test
.
Example: Test=["breakpoint" "forecast"]
conducts two
Chow tests. The first test directly assesses coefficient equality
constraints, and the second test assess forecast
performance.
Data Types: char
 string
 cell
Coeffs
— Flags indicating which elements of β to test for equality
logical vector  logical array
Flags indicating which elements of β to test for equality, specified as a
logical vector or array. Vector values must be of length
numCoeffs
. Array values must be of size
numTests
bynumCoeffs
.
If the value of Intercept
contains mixed logical values:
numCoeffs
isnumPreds
+ 1chowtest
ignores values in the first column ofCoeffs
for models without an intercept.
For example, suppose the regression model has three
predictors (numPreds
is 3) in a linear model, and you
want to conduct two Chow tests (numTests
is 2). Each
test includes all regression parameters in the linear model. Also, you
want chowtest
to fit an
intercept in the linear model for the first test only. Therefore,
Intercept
must be the logical array [1
0]
. Because there is at least one model for which
chowtest
fits an intercept,
Coeffs
must be a 2by4 logical array
(numTests
is 2 and numCoeffs
is numPreds
+ 1). The elements of
Coeffs(:,1)
correspond to whether to
test the intercept irrespective of its presence
in the model. Therefore, one way to specify Coeffs
is
true(2,4)
. For the second test,
chowtest
does not fit an intercept, and so
it ignores the value true
in
Coeffs(2,1)
. Because
chowtest
ignores
Coeffs(2,1)
, Coeffs
=
[true(1,4); false true(1,3)]
yields the same
result.
The default is true(numTests,numCoeffs)
,
which tests all of β for all tests.
Example: Coeffs=[false true true; true true true]
conducts two Chow tests on
a model containing three coefficients. The first test assesses the
second and third coefficients, and the second test assesses all
coefficients.
Alpha
— Nominal significance level
0.05 (default)  numeric scalar  numeric vector
Nominal significance level for the hypothesis test, specified as a numeric scalar in the interval (0,1) or a numeric vector of such values.
chowtest
conducts a separate test for each value in Alpha
.
Example: Alpha=[0.01 0.05]
uses a level of significance of 0.01
for the first test, and then uses a level of significance of 0.05
for the second test.
Data Types: double
Display
— Flag for command window display of results
"off"
 "summary"
Flag for a command window display of results, specified as a value in this table.
Value  Description  Default Value When 

"off"  chowtest does not display results in the
command window.  numTests = 1 
"summary"  For each test, chowtest displays results
in the command window.  numTests > 1 
The value of Display
applies to all tests.
Example: Display="off"
Data Types: char
 string
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.
chowtest
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.
chowtest
uses the same specified predictors for all tests.
By default, chowtest
uses all variables in Tbl
that are not specified by the ResponseVariable
namevalue
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
Note
When
chowtest
conducts multiple tests, the function applies all single settings (scalars or character vectors) to each test.All vectorvalued specifications that control the number of tests must have equal length. Vector values and
Coeffs
arrays must share a common dimension, equal tonumTests
.If you specify
X
andy
, andbp
,Intercept
,Test
, orAlpha
are row vectors,chowtest
returns row vectors.
Output Arguments
h
— Test rejection decisions
logical scalar  logical vector
Test rejection decisions, returned as a logical scalar or vector with
length equal to the number of tests numTests
.
chowtest
returns h
when you
supply the inputs X
and y
.
The Chow test has the following hypotheses are:
H_{0}: Regression coefficients β, selected by the
Coeffs
namevalue argument, are identical across subsamples.H_{1}: At least one regression coefficient in β, selected by the
Coeffs
namevalue argument, exhibits significant change across subsamples.
Elements of h
have the following values and
meanings.
Values of
1
indicate rejection of the null hypothesis that the regression coefficients β, selected byCoeffs
, are identical across subsamples model, in favor of the alternative hypothesis.Values of
0
indicate failure to reject the null hypothesis.
stat
— Test statistics
numeric scalar  numeric vector
Test statistics, returned as a numeric scalar or vector with length equal
to the number of tests numTests
.
chowtest
returns stat
when
you supply the inputs X
and y
. For
details, see Chow Tests.
StatTbl
— Test summary
table
More About
Chow Tests
Chow tests assess the stability of the coefficients
β in a multiple linear regression model of the form y = Xβ +
ε. chowtest
supports the two variations of
the Chow test introduced in [1]: the break point and forecast tests.
The break point test is a standard F test
from the analysis of covariance. The forecast test makes use of the
standard theory of prediction intervals. Chow’s contribution
is to place both tests within the general linear hypothesis framework,
and then to develop appropriate test statistics for testing subsets
of coefficients (see Coeffs
). For teststatistic
formulae, see [1].
Tips
Chow tests assume continuity of the innovations variance across structural changes. Heteroscedasticity can distort the size and power of the test. Therefore, verify the innovationsvariancecontinuity assumption holds before using the test results for inference.
If both subsamples contain more than
numCoeffs
observations, you can conduct a forecast test instead of a break point test (seeTest
). However, the forecast test might have lower power relative to the break point test [1]. Nevertheless, Wilson (1978) suggests conducting the forecast test in the presence of unknown specification errors [4].You can apply the forecast test to cases where both subsamples have size greater than
numCoeffs
, where you would typically apply a breakpoint test. In such cases, the forecast test might have significantly reduced power relative to a break point test [1]. Nevertheless, Wilson (1978) suggests use of the forecast test in the presence of unknown specification errors [4].The forecast test is based on the unbiased predictions, with zero mean error, that result from stable coefficients. However, zero mean forecast error does not, in general, guarantee coefficient stability. Therefore, forecast tests are most effective in checking for structural breaks, rather than model continuity [3].
To obtain diagnostic statistics for each subsample, such as regression coefficient estimates, their standard errors, error sums of squares, and so on, pass the appropriate data to
fitlm
. For details on working withLinearModel
model objects, see Multiple Linear Regression.
References
[1] Chow, G. C. "Tests of Equality Between Sets of Coefficients in Two Linear Regressions." Econometrica. Vol. 28, 1960, pp. 591–605.
[2] Fisher, F. M. "Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note." Econometrica. Vol. 38, 1970, pp. 361–66.
[3] Rea, J. D. "Indeterminacy of the Chow Test When the Number of Observations is Insufficient." Econometrica. Vol. 46, 1978, p. 229.
[4] Wilson, A. L. "When is the Chow Test UMP?" The American Statistician. Vol. 32, 1978, pp. 66–68.
Version History
Introduced in R2015bR2022a: chowtest
returns a results table when you supply a table of data
If you supply a table of time series data Tbl
,
chowtest
returns a table containing variables for the
test rejection decisions h
, pvalues
pValue
, tests statistics stat
, and
critical values cValue
, with rows corresponding to separate
tests.
Before R2022a, chowtest
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 output position.
StatTbl = chowtest(Tbl,bp,Name=Value)
If you request more outputs, chowtest
issues an
error.
Also, access results by using table indexing. For more details, see Access Data in Tables.
See Also
fitlm
 LinearModel
 cusumtest
 recreg
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