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The problem I've found is that after performing all tests, and finding that my methodology seems correct no unit roots, no OVB, homoscedasticity, no structural break, evidence of L-R relationships That is, that when I perform my ARDL, I don't find statistically significant results, and as you may think, this would make my thesis and all my work useless.
Would love to hear any new ideas or any opinion on how I could approach this. I've added unemployment and economic uncertainty to see if, with some new variables, my model could change, but it didn't. Attached you will find my methodology. Thanks, and hoping to received feedback soon. Tags: None. Justin Niakamal. It looks like you're using first differences and then running ardl. I suggest verifying that none of your variables are I 2 and then running ardl on the levels of your variables to capture the long-run relationship.
The short term relationships are captured by the first difference terms of the levels in ardl , so you don't need to create them. Also, I would skip the varsoc step and let ardl select the lag order for your model.
Hope this helps. Comment Post Cancel. Hi Justin, Many thanks for your answer. I'm doing the first differences because I'm following a paper by Hasan and Nasir Given that my variables appear to have a unit root in their levels, do you think that applying the ARDL method without taking FD would be ok? As I understand it, you use the FD to take out the unit root effect of those variables, and that's when you run the ARDL, but maybe I'm wrong about that.
Would you run the ARDL using prices of the market index or using returns without the log? Also, if I skip the varsoc step, an error appears saying " of lag permutations exceeds setting of 'maxcombs' ". How do you think should I approach this?
Munkhtuul Gantumur. Hi, sorry to ask a different question here. I have been a little confused about interpreting my results from "ardl" command and "ardl, ec" command. Could anyone please help me with answering these questions? Prior to the bound test, I am running the command "ardl [indep. I was wondering whether these coefficients are the ones I am getting from the regression equation on Slide 5 from your presentation file here? If so, could you please tell me if it is correct to just interpret them as I would interpret normal regression results?
If the above is correct, are the long-run and short-run coefficients I get from "ardl, ec" command is the coefficients from the regression equation in slide 12? How do I explain it if the lagged terms of the same variables have different signs? This study examines the relationship between public debt on both short and long-run economic growth, in a panel of selected Asian countries for the period of — We employ several econometrics methods: pooled mean group, mean group, dynamic fixed effects and also allow for common correlated effects.
The impact of a change in public debt is also analysed using asymmetric panel ARDL method. Our results indicate that an increase in government debt is negatively associated with economic growth in both the short and long-run. The relationship between public debt and economic growth has been a subject of increasing interest amongst academic scholars and policy makers.
The problem of rising public debt is nothing new to developed countries and has also been an issue of increased interest in developing countries. Indeed, in their study Herndon et al. Also, Reinhart and Rogoff do not deal with the issue of causality and Dafermos shows that their results are heavily impacted by periods of low economic growth in which there is usually a noticeable increase in public debt.
There are two mechanisms through which this happens: i low economic growth directly impacts on the debt to GDP ratio since GDP is the denominator of this ratio and ii low economic growth tends to worsen fiscal deficits due to the impact of automatic stabilisers. This study analyses whether rising public debt is harmful for growth, in both the short-run and long-run using data from fourteen Asian countries.
The fact that the Asian countries are the biggest borrowers among emerging economies means that the issue of rising public debt is a particularly important issue in that region of the world. The Asian economies have also been exposed to two major crises during the period under study, the Asian financial crisis of and the global financial crisis of which has boosted the public debt to GDP ratios in these countries.
We firstly use a simple bivariate model to assess the direct impact of government debt on economic growth. This study contributes to the literature by examining a set of countries that has not previously been explored, using both a linear and non-linear methodology.
Our research also contributes by looking at the issue of cross-sectional dependence in macro panel data. The presence of cross-sectional dependence may be caused by numerous aspects: spatial spillover, omitted and unobserved common factors as discussed in Breitung and Pesaran Ignoring these factors may lead to the inconsistency of parametric and nonparametric estimators as pointed out by Baltagi The remainder of this study is organised as follows: section 2 presents a review of the literature, section 3 explains the data and sample selection, section 4 presents the empirical methodology and section 5 discuss the empirical results.
Finally, section 6 concludes. Debt-related problems are nothing new to developed and developing countries. Many different empirical approaches have been used to examine the link between public debt and economic growth. The results can be heavily influenced by the time period of the study, country selection and estimation methods.
As pointed out by Panizza and Presbitero at the theoretical level models yield ambiguous results concerning the relationship between public debt and economic growth and hence the link between the two is basically an empirical issue. They also argue that there is no empirical study that can make a compelling case for a causal relationship going from debt to economic growth.
Nonetheless, a strong adverse effect of high levels of public debt in low-income countries is found in the study of Pattillo et al. Cecchetti et al. Several studies argue that there is a non-linear link in which increased government debt may increase economic growth initially but after a certain point lead to a decrease in the growth rate.
Based on historical data series for two decades, they analyse the link among inflation, high central sovereign debt, and economic growth in both developed and developing nations. However, the Rogoff and Reinhart results have become controversial due to some computational errors in their calculations that were pointed out by Herndon et al. Kumar and Woo find a similar result using panel data for 38 advanced and emerging countries. However, the quadratic relationship is very sensitive to extreme values, particularly in a small sample of observations as pointed out by Panizza and Presbitero Presbitero uses total government debt in analysing the debt-growth link in developing countries for the period — Apart from the non-linearity debate, the issue of reverse causality needs to be taken into consideration, that is, whether debt leads to higher growth or vice-versa.
Their results suggest that the two-step GMM is more favourable regarding efficiency. Kumar and Woo use a system GMM dynamic panel regression approach to address the endogeneity issue. The GMM method is considered to be more efficient and give more precise estimations since this approach is applicable for large cross-country analysis see Roodman Presbitero also consider the foreign currency debt as a proportion of government debt, as an instrumental variable.
However, the use of this variable is questionable, in terms of the economic interpretation and according to Woo and Kumar this variable cannot meet the restriction criteria of a good IV estimator and its usage as an instrument is highly questionable for high-income countries where there is a low level of foreign currency proportion of debt.
To avoid reverse causality, Woo and Kumar use initial debt levels to analyse the effect on future growth. Due to the problem of finding suitable external instrumental variables, the standard system GMM estimator is used to address the potential endogeneity issue.
They find that a high initial level of public debt is significantly associated with slower subsequent growth in a large panel of countries made up of developed and emerging market economies. While Baum et al. They use the dynamic panel method of GMM to estimate the linear model and modified Caner and Hansen approach to estimate the debt threshold. Sen et al. In the spirit of debt overhang, they examine external debt and find that borrowing severely hinders growth in Latin America and has a mildly negative effect in the case of Asia.
However, the GMM only captures the dynamics of short-run and ignores the long-run relationship since the estimator is designed for a small time span. Consequently, as shown by Christopoulos and Tsionas the outcomes may show a spurious result instead of long-run equilibrium. Moreover, in the case of a small N and large T, the GMM estimator may suffer from an autocorrelation problem in the residuals of the first-difference estimation, see Roodman Focusing on time series estimation, the authors find that the adverse impact is persistent in the long-run, but there are positive effects for some member countries in the short-run.
Conversely, Eberhardt and Presbitero use a dynamic model of common correlated effects of pooled group and mean group estimators to analyse the link between debt and growth and they also use the traditional mean group and dynamic two-way fixed effects as a means of comparison. Using data from countries, the authors allow for heterogeneity in the long-run and short-run link. They find a significant positive effect on average in the long-run debt but an insignificant result in the short-run.
The use of panel autoregressive distributed lag ARDL models for analysing the impact of public debt on economic growth can also be found in Chudik et al. They also find no simple debt threshold for either developed or developing countries after accounting for the impact of global factors and spillover effects. Panel estimation is chosen in this study to control for individual heterogeneity, to identify unobservable characteristics and to give more information on reliable estimation, see Baltagi Our analysis uses the data of 14 countries in Asia over a period of 33 years — , resulting in a total of observations see Table 1 for countries in the sample.
The choice of the countries was determined by issues of data availability. Japan was excluded from the analysis due its high public debt level. Table 1 provides comparative data for countries debt-to-GDP ratio. However, when T is larger than N as in our case the ARDL approach is appropriate and therefore is the preferred method for our analysis.
Footnote 1. Following Ala-i-martin et al. With the inclusion of several control variables to overcome the problem of omitted variables bias. The variables used in our study are listed below:. Trade openness in log : This study uses sum of import and exports as a percentage of GDP to account for international trade activity. We use several econometrics methods to examine the relationship between public debt and economic growth particularly in Asian countries and consider both the long-run and short-run relationships, along with the presence of nonlinearity.
We first conduct panel unit root tests before performing the main estimations, the tests are necessary to check whether the variables are non-stationary. Several tests are conducted: Im et al. The LL test is based on the assumption of non-heterogeneity of the autoregressive parameter, while the IPS test allows the heterogeneity while the CIPS unit root test relaxes the assumption of cross-sectional independence of the contemporaneous correlation All of these tests use the null hypothesis of non-stationarity.
The selection of the lag length is chosen using the Bayesian-Schwarz criteria. Another test we conduct is Cross-Sectional CD Pesaran which accounts for the presence of cross-sectional dependence. Panel data estimation assumes that disturbances are cross-sectionally independent, however, with the cross-country influences in the population, the issue of a cross-sectional link may arise.
This dependence might be caused by similar geographical area, political or economic inducement Gaibulloev et al. Two panel cointegration tests are employed here, based on the results of preliminary tests of non-stationarity. If the variables are non-stationary, then an examination for cointegration is conducted, using cointegration tests of Pedroni and Westerlund These cointegration tests are expected to reveal the existence or otherwise of a long-run relationship.
The Pedroni test proposes seven different panel cointegration tests to check for the absence of cointegration. The seven-test relies on three between-dimension approaches and four within-dimension methods. Generalised least square correction is used to correct the independent idiosyncratic error terms across individuals.
The Westerlund test exhibits four-panel cointegration estimation with the null of no cointegration, rejection of null hypothesis can be considered as the presence of cointegration in at least one individual unit. This method is superior regardless of whether the underlying regressors exhibit I 0 , I 1 or a mixture both Pesaran and Shin with a time span of over 20 years, the macro panel data method can be implemented. It was not appropriate to use the GMM estimator due to the nature of dataset.
The main model of panel ARDL approach is to obtain the relationship between public debt and economic growth:. By reparameterising eq. The PMG restricts long-run equilibrium to be homogenous across countries, while allowing heterogeneity for the short-run relationship. The short-run relationship focuses on the country specific heterogeneity, which might be caused by different responses of stabilisation policies, external shocks or financial crises for each country.
The MG estimator allows for heterogeneity in the short-run and long-run relationship. To be consistent, this estimator is appropriate for a large number of countries. For a small number of N, this method is sensitive to permutations of non-large model and outliers Favara, By contrast, the DFE estimator restricts the speed of adjustment, slope coefficient and short-run coefficient to exhibit non-heterogeneity across countries.
Accepting this estimator as the main analysis tool requires the strong assumption that countries responses are the same in the short-run and long-run, which is less compelling. Another drawback is that this approach may suffer from simultaneity bias in a small sample case due to the endogeneity between err the eror term and lagged explanatory variables Baltagi et al.
In the case of our data it is derived from middle-income countries which exhibit similar behaviour in the long-run, regarding economic growth. The short-run is expected to be non-homogenous due to the country specific differences, as such the PMG estimator seems to be superior to other methods. We use the Hausman test to verify the significance of each estimator. The common correlated effect is introduced in the panel ARDL estimation to account for contemporaneous correlation.
It is expected that CCEPMG to be consistent and efficient in this estimation, under the null hypothesis of no heterogeneity in the long-run. Following Eberhardt and Presbitero , this study attempts to look at the asymmetric response of long-run and short-run response of public debt accumulation in economic growth. We start our empirical analysis by conducting panel unit root tests for all our variables. The unit root tests which are summarised in Table 2 , show that variables of interest have both non-stationary and stationary characteristics.
Real GDP, openness and human capital are I 1 according to all unit root tests. Consequently, it is necessary to perform cointegration tests between real GDP and public debt to GDP to check for the possible existence of a long-run relationship.
Footnote 2. Two cointegration tests are conducted to analyse the long-run relationship between government debt and growth. Pedroni test results see Table 3 show that the null hypothesis of no cointegration in a heterogeneous panel cannot be rejected. To accept the alternative hypothesis the panel variance has to possess a large statistical value and the latter six tests have to show large negative values Pedroni The same result is obtained from Westerlund test of no cointegration between variables, showing high probabilities of no rejection in the p values.
The rationale here is to test for the absence of cointegration by determining whether an Error Correction Model ECM exists for individual panel members or for the panel as a whole. Two different classes of tests can be used to evaluate the null hypothesis of no cointegration and the alternative hypothesis: group-mean tests G and panel tests P. Footnote 3 The results of all these additional cointegration tests are summarised in Table 4 and in all cases show no evidence of cointegration.
As previously stated, the panel ARDL method can be utilised to account for long-run and short-run relationships, even for the case of non-stationary variables but without cointegration. Table 5 , Panel A reports the estimates for all three methods and shows a significant result in the short-run that increased government debt adversely affects economic growth in the bivariate model. However, none of these tests are significant in the long-run.
The ECM has a significant negative sign for the error correction term which implies that this model converges to a long-run relationship. The next estimation presented in Table 5 , Panel B uses all the determinants of growth and shows a similar result as in the bivariate case model. In the short-run, three estimators show significant negative results of public debt on economic growth.
The investment ratio has a significant positive effect on economic growth. However, although human capital and trade openness have a positive sign as expected they are largely not significant, except in the case of the human capital proxy variable in the MG estimator. The negative long-run relationship of public debt in the estimation is significant only in the PMG method. The other two estimators show a negative but insignificant sign.
Investment and openness in the long-run are signed as expected but not significant. The DFE approach exhibits a significant positive effect of human capital in the long-run. The error correction terms are again negative and significant showing convergence in the long-run. Among all of the error correction results, the highest speed of adjustment of As stated before, we expect the PMG estimator to be the best approach. PMG allows the short-run to have differing responses across countries, while it restricts the long-run to exhibit non-heterogeneity.
One advantage of using the PMG is that for a relatively small cross section of data 14 countries the PMG is less sensitive to the existence of outliers Pesaran et al. In addition, the problem of serial autocorrelation can be corrected simultaneously.
The benefit of using panel ARDL with sufficient lags is a reduction of the problem of endogeneity Pesaran and Smith, which has been a concern in the recent debt-growth literature. This chosen estimator is valid only if the assumption of the long-run restriction is not rejected. As can be seen from Table 5 , Panel B, the homogeneity restriction is efficient and significant under such a hypothesis.
Moreover, the Hausman test for the first and second model reveals a preference for PMG approach. The residuals show an I 0 integration suggesting the regressions are not spurious. Despite the significant result of the variables of interest, the ARDL method disregards contemporaneous correlation across countries, which is caused by unobserved factors. Ignoring these factors can lead to less consistent parametric and non-parametric estimators Baltagi This is shown from the CD test Pesaran result which indicates a high value of cross-sectional dependence in the error term and clearly rejects the null of weakly cross-sectional dependence.
The contemporaneous correlation is expected to diminish when the common correlated model is introduced. In the bivariate model, the CCEPMG estimator shows a significant result in the short-run and long-run and error correction term. In the multivariate model, both estimators show a significant negative debt relationship in the long-run while neither is significant in the short-run although the error correction terms remains negative and appears to be a much higher value.
The control variable of the investment ratio is positively associated in the short-run and long-run in the CCEPMG result showing that it is a key determinant of economic growth. By contrast, the human capital coefficient is not significant in this estimation and has a negative sign in the short-run. This result is somewhat surprising given the idea that human capital is an important driver of economic growth.
One possible explanation along the lines of Van Leeuwen is that average years of schooling is an imperfect measure of human capital, he argues that this variable cannot capture the increased efficiency in the economy resulting from education. Moreover, since this variable is not expressed in terms of a monetary unit, it is not comparable with the capital stock formation monetary unit measurement. CCEPMG and CCEMG estimators show that trade openness is significantly negative in the short-run when it might be the case of trade liberalisation undermines domestic production due to import competition, see Gries and Redlin All four tests show negative and significant result for the error correction term, supporting the evidence of a long-run relationship.
When a deviation from the long-run exists, the speed of adjustment to the long-run equilibrium is derived from the absolute value of the error correction term. In the bivariate model, deviations can be corrected for at a rate of In the multivariate growth model, the speed of adjustment is much higher at The residual tests are I 0 for all estimations, it is worth noting that the CCE estimator is valid even in the presence of serial correlation in the error term Pesaran In order to be a valid estimator, CCEMG should satisfy two requirements i the number of cross-section averages should be at least equal to the number of unobserved common factors and ii sufficient lags of cross section averages, see Chudik and Pesaran However, including more lags of averages variables is not desirable in our case because of the relatively small sample size.
The CCEPMG estimator is chosen as the preferred approach because of the econometric theory behind this estimator and the significance of outcomes in both models. Besides, the estimator is correctly specified without the problem of autocorrelation and cross-sectional dependence.
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