In this blog entry we will offer a brief discussion on some aspects of seasonal nonstationarity and discuss two popular seasonal unit root tests. In particular, we will cover the Hylleberg, Engle, Granger, and Yoo (1990) and Canova and Hansen (1995) tests and demonstrate practically using EViews how the latter can be used to detect the presence of seasonal unit roots in a US macroeconomic time series. All files used in this exercise can be downloaded at the end of the entry.
Deterministic vs Stochastic Seasonality
When we talk about the concept of seasonality in time series, we usually refer to the idea of "... systematic, although not necessarily regular, intrayear movement caused by changes of the weather, the calendar, and timing of decisions..." (Hans Franses). Naturally, macroeconomic data observed with high periodicity (sampled more than once a year) usually exhibit this behavior.Seasonality can be modelled in two ways: deterministically or stochastically. The former arises form systematic cycles such as calendar effects or climatic phenomena and can be removed from data by the seasonal adjustment procedures  in other words, by including seasonal dummy variables. Formally, this implies deterministic seasonality evolves as:
$$ y_{t} = \mu + \sum_{s=1}^{S1}\delta_{s}D_{s,t} + e_{t} $$ where $ S $ is the total number of period cycles, $ D_{s,t} $ are seasonal dummy variables which equal 1 in season $ s $ and 0 otherwise, and $ e_{t} $ are the usual innovations. For example, in the case of quarterly data $ (S=4) $, one could postulate that seasonality evolves as:
$$ y_{t} = 15  D_{1,t}  4D_{2,t}  6D_{3,t} + e_{t}$$ The process is visualized below:
Figure 1: Deterministic Seasonality
$$ \widehat{y}_{S(t+h)s} = \widehat{\mu} + \widehat{\delta}_{s} $$ where $ s = S1, \ldots, 0 $. In other words, the optimal forecast of $ y_{t} $ in season $ s $ is the same at each future point in time for said season. It is precisely this property which formalizes the notion of systematic cyclicality.
On the other hand, stochastic seasonality describes nearly systematic cycles which evolve as seasonal ARMA$(p,q)$ processes of the form:
$$ (1  \eta_{1}L^{S}  \eta_{2}L^{2S}  \ldots  \eta_{p}L^{pS})y_{t} = (1 + \xi_{1}L^{S} + \xi_{2}L^{2S} + \ldots + \xi_{q}L^{qS})e_{t}$$ where $ L $ denotes the usual lag operator. In particular, when $ p = 1 $ and $ q = 0 $, the seasonal AR(1) model with $ \eta_{1} = 0.75 $ is visualized as follows:
Figure 2: Stochastic Seasonality
$$ \widehat{y}_{S(t+h)s} = \widehat{\eta}_{1}^{h}y_{Sts} $$ In other words, the forecast in any given season is a function of past data values, and is therefore considered to be stochastic.
So how does one identify whether a series exhibits deterministic or stationary seasonality? One useful tool is the periodogram which produces a decomposition of the dominant frequencies (cycles) of a time series. As it turns out, there are at most $ S $ frequencies in a time series exhibiting $ S $ period cycles. Formally, these are identified in conjugate pairs as follows:
$$ \omega \in \left\{0, \left(\frac{2\pi}{S}, 2\pi\frac{2\pi}{S}\right), \left(\frac{4\pi}{S}, 2\pi\frac{4\pi}{S}\right), \ldots, \pi \right\} $$ if $ S $ is even, and
$$ \omega \in \left\{0, \left(\frac{2\pi}{S}, 2\pi\frac{2\pi}{S}\right), \left(\frac{4\pi}{S}, 2\pi\frac{4\pi}{S}\right), \ldots, \left(\frac{\lfloor S/2 \rfloor\pi}{S}, 2\pi\frac{\lfloor S/2\rfloor\pi}{S}\right) \right\} $$ if $ S $ is odd.
Thus, given a stationary time series with $ S $ period cycles, we expect the periodogram to protrude at the nonzero frequencies. In particular, we present the periodogram for deterministic and stochastic seasonal processes below:




Seasonal Unit Roots
A particularly important form of stochastic seasonality manifests in the form of unit roots at some or all of the frequencies $ \omega $. In particular, consider the following process:$$ y_{t} = \eta y_{tS} + e_{t} $$ and note that the characteristic equation associated with the process is defined as:
\begin{align} 1  \eta z^{S} = 0 \quad \text{or} \quad z^{S} = 1/\eta \label{eq1} \end{align} Analogous to the case of classical unit root processes, when $ \eta=1 $ or $ z = 1^{1/S} = 1 $, $ y_{t} $ is in fact nonstationary. In contrast to the classical unit root case however, $ y_{t} $ can possess not one, but upto $ S $ unique unit roots. To see this, note that any complex number $ z = a + ib $ can be written in polar form as:
$$ z = \sqrt{a^{2} + b^{2}}(\cos(\theta) + i\sin(\theta)) = r(\cos(\theta) + i\sin(\theta)) $$ where $ r = z$ is called the magnitude of $ z $, but is also the radius of the circle in polar coordinates. Accordingly, when $ \eta  = 1 $ or $ z=1 $, $ z $ lies on a circle with radius $ r = 1 $. In other words, $ y_{t} $ is a unit root process. Next, recall Euler's formula:
$$ e^{ix} = \cos(x) + i \sin(x) $$ Clearly, any complex number $ z $ with magnitude $ r=1 $ satisfies Euler's formula. In other words, $ z = e^{i\theta} $. Since Euler's formula also implies that:
$$ e^{2\pi i k} = 1 \quad \text{for} \quad k=0,1,2,\ldots$$ when $ \eta=1 $ or $ z=1 $, the characteristic equation \eqref{eq1} can be expressed as:
\begin{align*} z = e^{i\omega} &= 1^{1/S} \notag\\ &= (e^{2\pi i k})^{1/S}\notag\\ &= e^{\frac{2\pi i k}{S}} \end{align*} where the relations above evidently hold for all $ k=0,1,2,\ldots, S1 $ since the solutions begin to cycle when $ k \geq S $. Now, taking logarithms of both sides, it is clear that:
\begin{align} \omega = \frac{2\pi k}{S} \quad \text{for} \quad k=0,1,2,\ldots, S1 \label{eq2} \end{align} In other words, the characteristic equation \eqref{eq1} has $ S $ unique solutions identified by the $ S $ relationships in \eqref{eq2}. These solutions are equally (by $ 2\pi k/S $ degrees) spaced on the unit circle, with two real solutions associated with $ \omega = 0 $ and $ \omega = \pi $, and the remaining $ S2 $ imaginary solutions organized in harmonic pairs.
Thus, when we identify $ S $ with a temporal frequency, namely a week, month, quarter, and so on, the problem of identifying roots of the characteristic equation \eqref{eq1} extends the classical unit root literature in which $ S=1 $ (or annual frequency), to that of identifying $ S > 1 $ possible roots on the unit circle.
In fact, like the classical unitroot literature in which unchecked unit roots are known to have severe inferential consequences, the presence of unit roots at seasonal frequencies can also give rise to similar inferential inaccuracies and concerns. Accordingly, identifying the presence of unit roots at one or more seasonal frequencies is the subject of the battery of tests known as seasonal unit root tests.
Seasonal Unit Root Tests
Historically, the first test for a seasonal unit root was proposed by Dickey, Hasza and Fuller (1984) (DHF). In its simplest form, the test is based on running the regression:$$ (1L^{S})y_{t} = \eta y_{ts} + e_{t} $$ and testing the null hypothesis $ H_{0}: \eta = 0 $ against the onesided alternative $ H_{A}: \eta < 0 $. The test is carried out using the familiar Student's$ t $ statistic on statistical significance for $ \eta $, and analogous to the classic augmented DickeyFuller (ADF) test, exhibits a nonstandard asymptotic distribution under the null. Nevertheless, the DHF test is very restrictive. Whereas the test imposes the existence of a unit root at all $ S $ seasonal frequencies simultaneously, in reality, a process may exhibit a seasonal unit root at some seasonal frequencies but not others.
HEGY Seasonal Unit Root Test
To correct for the shortcomings of the DHF test, Hylleberg, Engle, Granger and Yoo (1990) (HEGY) proposed a test for the determination of unit roots at each of the $ S $ seasonal frequencies individually, or collectively. In particular, following the notation in Smith and Taylor (1999), in its simplest form, the HEGY test is based on regressions of the form:\begin{align*} (1L^{s})y_{Sts} &= \mu + \pi_{0}L\left(1 + L + \ldots + L^{S1}\right)y_{Sts}\\ &+ L\sum_{k=1}^{S^{\star}}\left( \pi_{k,1}\sum_{j=0}^{S1}\cos\left((j+1)\frac{2\pi k}{S}\right)L^{j}  \pi_{k,2}\sum_{j=0}^{S1}\sin\left((j+1)\frac{2\pi k}{S}\right)L^{j} \right)y_{Sts}\\ &+ \pi_{S/2}L\left(1  L + L^{2}  \ldots  L^{S1}\right)y_{Sts} + e_{t}\\ &\equiv \mu + \pi_{0}y_{Sts1, 0} + \sum_{k=1}^{S^{\star}}\pi_{k,1}y_{Sts1,k,1} + \sum_{k=1}^{S^{\star}}\pi_{k,2}y_{Sts1,k,2} + \pi_{S/2}y_{Sts1, S/2} +e_{t} \end{align*} where $ S^{\star} = (S/2)  1 $ if $ S $ is even and $ S^{\star} = \lfloor S/2 \rfloor $ if $ S $ is odd, and as before, $ s = S1, \ldots, 1, 0 $.
In particular, when data is quarterly with $ S=4 $ and therefore $ S^{\star} = 1 $, then:
\begin{align*} y_{4ts, 0} &= (1+L+L^{2}+L^{3})y_{4ts}\\ y_{4ts, 1,1} &= L(1L^{2})y_{4ts}\\ y_{4ts, 1,2} &= (1L^{2})y_{4ts}\\ y_{4ts, 2} &= (1L+L^{2}L^{3})y_{4ts} \end{align*} Here, $ y_{4ts, 0} $ is in fact the series $ y_{4ts} $ filtered by the 0 frequency filter, $ y_{4ts, 1,1} $ is the series $ y_{4ts} $ filtered by the $ \pi/2 $ frequency filter, $ y_{4ts, 1,2} $ is the series $ y_{4ts} $ filtered by the $ 3\pi/2 $ frequency filter, and $ y_{4ts, 2} $ is the series $ y_{4ts} $ filtered by the $ \pi $ frequency filter.
To visualize the frequency filters, consider the spectral filter functions associated with each of the processes above. The latter are computed as $ \phi(e^{i\theta}) $ where $ \phi(\cdot) $ is the lag polynomial applied to $ y_{Sts} $, and $ \theta \in [0, 2\pi) $. For instance, in case of quarterly data, the 0 frequency filter is computed as $ 1 + e^{i\theta} + e^{i2\theta} + e^{i3\theta} + e^{i4\theta} $, and so on.
Figure 4: HEGY Seasonal Filters
It should also be noted here that while we have focused on the simplest form, the HEGY test can accommodate various deterministic specifications in the form of seasonal dummies, constants, and trends. Moreover, in the presence of serial correlation in the innovation process, the HEGY test can also be augmented with lags of the dependent variable as additional regressors to the principal equation presented above, in order to mitigate the effect.
In fact, the HEGY test is very similar to the ADF test which is effectively a unit root test at the 0frequency alone. Whereas the latter proceeds as a regression of a differenced series against its lagged level, the former proceeds as a regression of a seasonally differenced series against the lagged levels at each of the constituent seasonal frequencies. In this regard, the HEGY test is considered an extension of the ADF test in the direction of nonzero frequencies. As such, it also suffers from the same shortcomings as the ADF test, and can exhibit low statistical power when the individual frequencies are in fact stationary, but exhibit nearunit root behaviour.
CanovaHansen Seasonal Unit Root Test
One response to the low power of ADF tests in the presence of near unit root stationarity was the test of Kwiatkowski, Phillips, Schmidt, and Shin (1992) (KPSS), which is in fact a test for stationarity at the 0frequency alone. The analogous development in the seasonal unit root literature was the test of Canova and Hansen (1995) (CH). Like the KPSS test, the CH test is also a test for stationarity but extends to nonzero seasonal frequencies.The idea behind the CH test is to suppose that seasonality manifests in the process mean. In other words, given a process $ y_{t} $, if seasonal effects are present, then $ y_{t} $ will exhibit a seasonally dependent average. Traditionally, this is formalized using seasonal dummy variables as:
$$ y_{t} = \sum_{s=0}^{S1}\delta_{s}D_{s,t} + e_{t} $$ Nevertheless, it is well known that an equivalent representation using discrete Fourier expansions exists in terms of sine and cosine functions. In particular,
$$ y_{t} = \sum_{k=0}^{S^{\star}}\left(\delta_{k,1}\cos\left(\frac{2\pi k t}{S}\right) + \delta_{k,2}\sin\left(\frac{2\pi k t}{S}\right)\right) + e_{t} $$ where $ S^{\star} $ was defined earlier, and $ \delta_{k,1} $ and $ \delta_{k,2}$ are referred to as spectral intercept coefficients. In either case, the expression can be expressed in vector notation as follows:
\begin{align} y_{t} = \pmb{Z}_{t}^{\top}\pmb{\gamma}_{t} + e_{t} \label{eq3} \end{align} where $ \pmb{Z}_{t} = \left(1, \pmb{z}_{1,t}^{\top}, \ldots, \pmb{z}_{S^{\star},t}^{\top} \right) $ (or $ \pmb{Z}_{t} = \left(1, D_{1,t}, \ldots, D_{S1,t}\right) $) and $ \pmb{\gamma}_{t} = \left(\gamma_{1,t}, \ldots, \gamma_{S,t}\right) $ is a an $ S\times 1 $ vector of coefficients, and $ \pmb{z}_{k,t} = \left(\cos\left(\frac{2\pi k t}{S}\right), \sin\left(\frac{2\pi k t}{S}\right)\right) $ for $ j=1,\ldots, S^{\star} $, with the convention $ \pmb{z}_{S^{\star},t} \equiv \cos(\pi t) = (1)^{t} $ when $ S $ is even.
Next, to distinguish between stationary and nonstationary seasonality, CH assume that the coefficient vector $ \pmb{\gamma}_{t} $ evolves as the following AR(1) model:
\begin{align*} \pmb{\gamma}_{t} &= \pmb{\gamma}_{t1} + u_{t}\\ u_{t} &\sim IID(\pmb{0}, \pmb{G})\\ \pmb{G} &= \text{diag}(\theta_{1}, \ldots, \theta_{S}) \end{align*} Observe that when $ \theta_{k} > 0 $, then $ \gamma_{k,t} $ follows a random walk. On the other hand, when $ \theta_{k} = 0 $, then $ \gamma_{k,t} = \gamma_{k, t1} = \gamma_{k} $, a fixed constant for all $ t $. In other words, when $ \theta_{k} > 0 $, the process $ y_{t} $ exhibits a seasonal unit root at the harmonic frequency pair $ (\frac{2\pi k}{S}, 2\pi  \frac{2\pi k}{S}) $ for $ 1\leq k < \lfloor S/2 \rfloor $, and the frequency $ \frac{2\pi k}{S} $ if $ k=0 $ or $ k = \lfloor S/2 \rfloor $. In this regard, to test the null hypothesis that $ y_{t} $ exhibits at most deterministic seasonality at certain (possibly all) frequencies, against the alternative hypothesis that $ y_{t} $ exhibits a seasonal unit root at certain (possibly all) frequencies, define $ \pmb{A}_1$ and $ \pmb{A}_2 $ as mutually orthogonal, full columnrank, $(S \times a_1)$ and $(S \times a_2)$matrices which respectively constitute $1 \leq a_1 \leq S$ and $a_2 = S  a_1$ subcolumns from the order$S$ identity matrix $\pmb{I}_s$.
For instance, if one wishes to test whether a seasonal unit root exists at frequency $ \pi $, one would set $ \pmb{A}_{1} = (0,\ldots, 0,1)^{\top} $. Alternatively, if testing for a seasonal unit root at the frequency pair $ \left(\frac{2\pi}{S}, 2\pi  \frac{2\pi}{S}\right) $, then one would set:
$$ \pmb{A}_{1} = \begin{bmatrix} 0 & 0 \\ 1 & 0 \\ 0 & 1 \\ 0 & 0 \\ \vdots & \vdots \\ 0 & 0 \end{bmatrix} $$ Note further that one can further rewrite \eqref{eq3} as follows:
$$ y_{t} = \pmb{Z}_{t}^{\top}\pmb{A}_{1}\pmb{A}_{1}^{\top}\pmb{\gamma}_{t} + \pmb{Z}_{t}^{\top}\pmb{A}_{2}\pmb{A}_{2}^{\top}\pmb{\gamma}_{t} + e_{t} $$ Next, define $ \pmb{\Theta} = \left(\theta_{1}, \ldots, \theta_{S}\right)^{\top} $ and observe that the CH hypothesis battery reduces to:
\begin{align*} H_{0}: \text{}\pmb{A}_{1}^{\top}\pmb{\Theta} = \pmb{0}\\ H_{A}: \text{}\pmb{A}_{1}^{\top}\pmb{\Theta} > 0 \end{align*} where in addition to $ H_{0} $, it is implicitly maintained that $H_{M}:\text{} \pmb{A}_{2}^{\top}\pmb{\Theta} = \pmb{0} $. In particular, notice that when both $ H_{0} $ and $ H_{M} $ hold, equation \eqref{eq3} reduces to:
\begin{align} y_{t} = \pmb{Z}_{t}^{\top}\pmb{\gamma} + e_{t} \label{eq4} \end{align} where $ \pmb{\gamma} $ is now constant across time. In other words, $ y_{t} $ exhibits at most deterministic (stationary) seasonality. In this regard, holding $ H_{M} $ implicitly true, Canova and Hansen (1995) propose a consistent test for $ H_{0} $ versus $ H_{A} $, using the statistic:
\begin{align*} \mathcal{L} = T^{2} \text{tr}\left(\left(\pmb{A}_{1}^{\top}\widehat{\pmb{\Omega}}\pmb{A}_{1}\right)^{1}\pmb{A}_{1}^{\top}\left(\sum_{t=1}^{T}\widehat{F}_{t}\widehat{F}_{t}^{\top}\right)\pmb{A}_{1}^{\top}\right) \end{align*} where $ \text{tr}(\cdot) $ is the trace operator, $ \widehat{e}_{t} $ are the OLS residuals from regression \eqref{eq4}, $ \widehat{F}_{t} = \sum_{t=1}^{T} \widehat{e}_{t}\pmb{Z}_{1,t} $, and the HAC estimator
$$ \widehat{\pmb{\Omega}} = \sum_{j=T+1}^{T1}\kappa\left(\frac{j}{h}\right)\widehat{\pmb{\Gamma}}(j) $$ Above, $ \kappa(\cdot) $ is the kernel function, $ h $ is the bandwidth parameter, and $ \widehat{\pmb{\Gamma}}(j) $ is the autocovariance (at level $ j $ ) estimator
$$ \widehat{\pmb{\Gamma}}(j) = T^{1} \sum_{t=j+1}^{T} \widehat{e}_{t}\pmb{Z}_{t}\widehat{e}_{tj}\pmb{Z}_{tj}^{\top} $$ Naturally, we reject the null hypothesis when $ \mathcal{L} $ is larger than some critical value which depends on the rank of $ \pmb{A}_{1} $.
Unattended Unit Roots
A wellknown problem with the CH test concerns the issue of unattended unit roots. In particular, CH tests the null hypothesis $ H_{0} $ while imposing $ H_{M} $, where the latter lies in the complementary space to that generated by the former. In practice however, one does not know which spectral frequency exhibits a unit root. If one did know, the exercise of testing for their presence would be nonsensical. In this regard, if $ H_{0} $ is imposed but $ H_{M} $ is violated, then, Taylor (2003) shows that the CH test is severely undersized. To overcome the shortcoming, Taylor (2003) suggests filtering the regression equation \eqref{eq3} to reduce the order of integration at all spectral frequencies identified in $ \pmb{A}_{2} $. In particular, consider the filter:$$ \nabla_{2} = \frac{1  L^{S}}{\nabla_{1}} $$ where $ \nabla_{1} $ reduces, by one, the order of integration at each frequency identified in $ \pmb{A}_{1} $. For instance, if $ \pmb{A}_{1} $ identifies the 0frequency, then $ \nabla_{1} = (1  L) $ and $ \nabla_{2} = \frac{1L^{S}}{1L} = 1 + L + \ldots + L^{S1} $. Alternatively, if $ \pmb{A}_{1} $ identifies the harmonic frequency pair $ \left(\frac{2\pi k}{S}, 2\pi  \frac{2\pi k}{S}\right) $, then $ \nabla_{1} = 1  2\cos\left(\frac{2\pi k}{S}\right)L + L^{2} $, and so on. Accordingly, if we assume $ \pmb{\gamma}_{t} = \pmb{\gamma}_{t1} + u_{t} $, it is clear that $ \nabla_{2}y_{t} $ will not admit unit root behaviour at any of the frequencies identified in $ \pmb{A}_{2} $ and the maintained hypothesis $ H_{M} $ will hold. See Taylor (2003) and Busetti and Taylor (2003) for further details.
Furthermore, since $ \nabla_{2} $ acts only on frequencies identified in $ \pmb{A}_{2} $, it can also be formally shown that the regressors $ \nabla_{2}\pmb{Z}_{t}^{\top}\pmb{A}_{1}$ span a space identical to the space spanned by $ \pmb{Z}_{t}^{\top}\pmb{A}_{1}$. Accordingly, the strategy in Taylor (2003) is to run the regression:
\begin{align*} \nabla_{2}y_{t} &= \nabla_{2}\pmb{Z}_{t}^{\top}\pmb{A}_{1}\pmb{A}_{1}^{\top}\pmb{\gamma}_{t} + \nabla_{2}\pmb{Z}_{t}^{\top}\pmb{A}_{2}\pmb{A}_{2}^{\top}\pmb{\gamma}_{t} + \nabla_{2}e_{t} \\ &= \pmb{Z}_{t}^{\top}\pmb{A}_{1}\pmb{A}_{1}^{\top}\pmb{\gamma}_{t} + e_{t}^{\star} \end{align*} where $ e_{t}^{\star} = \nabla_{2}\pmb{Z}_{t}^{\top}\pmb{A}_{2}\pmb{A}_{2}^{\top}\pmb{\gamma}_{t} + \nabla_{2}e_{t} $. Naturally, the modified test statistic is now given by:
\begin{align*} \mathcal{L}^{\star} = T^{2} \text{tr}\left(\left(\pmb{A}_{1}^{\top}\widehat{\pmb{\Omega}}^{\star}\pmb{A}_{1}\right)^{1}\pmb{A}_{1}^{\top}\left(\sum_{t=1}^{T}\widehat{F}_{t}^{\star}\widehat{F}_{t}^{\star^{\top}}\right)\pmb{A}_{1}^{\top}\right) \end{align*} where $ \widehat{F}_{t}^{\star} = \sum_{t=1}^{T} \widehat{e}_{t}^{\star}\pmb{Z}_{1,t} $ and $ \widehat{\pmb{\Omega}}^{\star} $ is computed analogous to $ \widehat{\pmb{\Omega}} $ upon replacing $ \widehat{e}_{t} $ with $ \widehat{e}_{t}^{\star} $.
Seasonal Unit Root Test in EViews
Starting with version 11 of EViews, a battery of tests aimed at diagnosing unit roots in the presence seasonality are now supported natively. These tests include the most famous Hylleberg, Engle, Granger, and Yoo (1990) (HEGY) test as well its Smith and Taylor (1999) likelihood ratio variant, the Canova and Hansen (1995) (CH) test, and the Taylor (2005) variance ratio test.Here, we will apply the HEGY and CH tests to detect the presence of seasonal unit roots in quarterly U.S. government consumption expenditures and gross investment data running from 1947 to 2018. We have named the series object containing the data as USCONS. The latter can either be opened from the workfile associated with this blog, or by running a fetch procedure to grab the data directly from the FRED database. In case of the latter, in EViews, issue the following commands in the command window:
wfcreate q 1947q1 2018q4 fetch(d=fred) NA000333Q rename NA000333Q usconsWe begin with a plot of the data. To do so, double click on a USCONS in the workfile to open the series object. Next, click on View/Graph.... This will open a graph options window. We will stick with the defaults so click on OK. The output is reproduced below.
Figure 5: Time Series Plot of USCONS
Figure 6: HEGY Test Dialog
As discussed earlier, in case of serially correlated errors, the HEGY test can be augmented by lags of the dependent variable added as additional regressors to the HEGY regression. To determine the precise number of lags to add, EViews offers both automatic and manual methods. The default is automatic lag selection with Akaike Information Criterion and maximum of 12 lags. The details can be changed of course, or, if automatic selection is undesired, a User Selected value can be specified. We will stick with the defaults. Hit OK.
Figure 7: HEGY Test Output
Consider next the CH test applied to the same data. To bring up the CH test options, from the series object, once again click on View/Unit Root Tests/Seasonal Unit Root Tests... and under the Test type dropdown, select Canova and Hansen. As before, we will leave the Periodicity unchanged and will change the NonSeasonal Deterministics to Intercept and trend. Note here that the traditional Canova and Hansen (1995) paper does not allow for the inclusion of deterministic trends. However, as noted in Busetti and Harvey (2003), we can relax ``the conditions of CH by showing that the distribution is unaffected when a deterministic trend is included in the model''.
Figure 8: CH Test Dialog
We will also leave the Include lag of dep. variable untouched. As noted in Canova and Hansen (1995), the inclusion of a lagged dependent variable in the CH regression ``will reduce this serial correlation (we can think of this as a form of prewhitening), yet not pose a danger of extracting a seasonal root''. At last, note the HAC Options button which opens a set of options associated with how the longrun variance is computed and gives users the option to customize which kernel and bandwidths are used, and whether further residual whitening is desired. We stick with default values and simply click on OK to execute the test.
Figure 9: CH Test Output
The next table presents a detailed look at the harmonic pair test. Although we did not explicitly ask for this test, EViews presents a breakdown of the joint test requested into its constituent restrictions. These are harmonic pair tests in which the restriction matrix $ \pmb{A}_{1} $ would be $ S\times 2 $. In this case, the test for no seasonal unit root at the harmonic pair is 2.968384 which is clearly larger than any of the critical values associated with the limiting distribution. In other words, we reject the null and conclude that there's evidence of a unit root at the harmonic pair frequencies. Notice also that in addition to the CH test statistic EViews also offers an additional test statistic marked by an asterisk for differentiation. This is in fact the test statistic that corresponds to the Taylor (2003) version of the CH test robustified to the possible violation of the maintained hypothesis $ H_{M} $ discussed earlier.
The table beneath the harmonic pair tests is a table summarizing CH tests corresponding to the individual breakdown of all frequencies under consideration. In other words, these are individual tests in which the restriction matrix $ \pmb{A}_{1} $ would be $ S\times 1 $. Since the frequency $ \pi $ was requested as part of the joint test, it is reported here. Clearly, with the test statistic equaling 3.842780, we reject the null hypothesis and conclude in favor of evidence supporting the existence of a unit root at the frequency $ \pi $. As before, note here that below the test statistic associated with the $ \pi $ frequency is an additional statistic differentiated by an asterisk. This, as before, is the Taylor (2003) version of the CH test robustified to unattended unit roots.
At last, the final table presents the CH regression. The residuals from this regression are used in the computation of the CH test statistics.
Conclusion
In this entry we gave a brief introduction into the subject of seasonal unit root tests. We highlighted the need to distinguish between deterministic and stochastic cyclicality and discussed several statistical methods designed to do so. Among these, our focus was on the HEGY tests, which is effectively an extension of the ADF test in the direction of nonzero seasonal frequencies, and the CH test, which is the analogue of the KPSS test in the direction of nonzero seasonal frequencies. We also looked at some of the mathematical details which underly these methods. At last, we closed with a brief application of both tests to the US Government consumption expenditure and investment data, sampled quarterly from 1947 to 2018. Both tests overwhelmingly supported evidence of unit roots at both individual and joint frequencies.Files
The workfile and program files can be downloaded here.References
1  Fabio Busetti and AM Robert Taylor. Testing against stochastic trend and seasonality in the presence of unattended breaks and unit roots. Journal of Econometrics, 117(1):2153, 2003. [ bib ] 
2  Fabio Busetti and Andrew Harvey. Seasonality tests. Journal of Business & Economic Statistics, 21(3):420436, 2003. [ bib ] 
3  Fabio Canova and Bruce E Hansen. Are seasonal patterns constant over time? a test for seasonal stability. Journal of Business & Economic Statistics, 13(3):237252, 1995. [ bib ] 
4  Svend Hylleberg, Robert F Engle, Clive WJ Granger, and Byung Sam Yoo. Seasonal integration and cointegration. Journal of econometrics, 44(12):215238, 1990. [ bib ] 
5  Denis Kwiatkowski, Peter CB Phillips, Peter Schmidt, and Yongcheol Shin. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of econometrics, 54(13):159178, 1992. [ bib ] 
6  Richard J Smith and AM Robert Taylor. Likelihood ratio tests for seasonal unit roots. Journal of Time Series Analysis, 20(4):453476, 1999. [ bib ] 
7  Robert AM Taylor. Robust stationarity tests in seasonal time series processes. Journal of Business & Economic Statistics, 21(1):156163, 2003. [ bib ] 
8  AM Robert Taylor. Variance ratio tests of the seasonal unit root hypothesis. Journal of Econometrics, 124(1):3354, 2005. [ bib ] 
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