Abstract
Selected Issues
The New Minimum Wage Policy in Korea
A. Recent Developments
1. The new administration of President Moon announced in July 2017 its economic policy platform that aims to shift the “growth paradigm from large production and fast-follower-led growth to sustainable growth.” One of the key pillars of this platform is income-led growth, which includes raising the minimum wage. In line with this policy direction, the Minimum Wage Commission comprising representatives from labor, businesses, and the public dramatically increased the minimum wage for 2018 to KRW 7,530 per hour, a 16.4 percent rise compared to this year. To partially compensate small businesses for the increased labor cost, the government plans to pay part of the additional burden, which is estimated at KRW 3 trillion (0.2 percent of GDP) for 2018.
Korea’s Minimum Wage
(Change, in percent)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database, IMF World Economic Outlook; Staff calculations.Korea’s Minimum Wage
(Change, in percent)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database, IMF World Economic Outlook; Staff calculations.Korea’s Minimum Wage
(Change, in percent)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database, IMF World Economic Outlook; Staff calculations.2. The 2018 minimum wage hike is by far the largest increase in real terms since the minimum wage system was established in late 1980s. Only the minimum wage hike in 2001 comes close to the current hike. That hike followed sharp increases in output and employment, after the contraction in output and wage growth in the wake of the Asian crisis. This time, the minimum wage growth is projected to substantially exceed the growth rates of real and nominal output.
OECD - Minimum Wages - 2016
(Hourly, in 2016 PPP U.S. dollars 1/)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database; IMF World Economic Outlook: Staff calculations1/ Purchasing power parties for phase computation.OECD - Minimum Wages - 2016
(Hourly, in 2016 PPP U.S. dollars 1/)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database; IMF World Economic Outlook: Staff calculations1/ Purchasing power parties for phase computation.OECD - Minimum Wages - 2016
(Hourly, in 2016 PPP U.S. dollars 1/)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database; IMF World Economic Outlook: Staff calculations1/ Purchasing power parties for phase computation.3. This hike will bring Korea’s minimum wage close to the OECD average. Last year, the minimum wage in Korea stood at $5.8 per hour, estimated using the purchasing power parity for private consumption. In 2018, this rate will increase to $7.2 per hour. According to the OECD, Korea’s 2016 minimum wage ranked 16th out of 27 OECD countries, and the 2018 minimum wage would put Korea in the middle of the group, assuming no change in other economies’ minimum wages. The hike will also push the ratio of minimum to average wage slightly above the OECD average.
OECD Minimum relative to average wages of full time workers 2016
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database: Staff calculationsOECD Minimum relative to average wages of full time workers 2016
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database: Staff calculationsOECD Minimum relative to average wages of full time workers 2016
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: OECD Database: Staff calculationsB. The Impact of a Minimum Wage Hike—General Considerations
4. A hike in the minimum wage could have an impact on multiple fronts. In general, it effects employment, especially youth employment, the overall wage levels in the economy, income distribution, and competitiveness of the firms. These in turn affect economic growth and inflation.
Employment
5. The impact of minimum wage on employment is by far the most important and controversial part of the debate on minimum wages. An increase in the minimum wage could potentially cause some low-skilled workers to lose employment, as their contribution to the firm is deemed to be less than their wage, and/or some workers maintain their jobs and see their wages rise to the new minimum wage level. Non-compliance with the regulations or reduction in nonwage benefits are also possibilities. To what degree a higher minimum wage effects employment in any given economy is ultimately an empirical question.
6. Academic literature on the impact of minimum wage on employment spans decades and covers hundreds of studies. The key takeaways are that (1) the impact of minimum wage on total employment is still being debated; and (2) there is quite strong evidence that higher minimum wages reduce the employment of low-skilled low-wage workers (Neumark and Wascher (2003, 2007)). Indeed, based on international data, Neuwark and Wascher find that a one percent increase in the minimum wage would lower youth employment by around 0.12 percent in the short run and 0.21 percent in the longer run. An IMF study (IMF (2016)) finds that in Central Eastern and South Eastern European (CESEE) economies, a 10 percent increase in minimum wage is associated with a youth employment reduction of 2 percent.
7. OECD takes a cautious view (OECD (2015)), arguing that “at reasonable levels, increases in the minimum wage are unlikely to cause substantial job loss,” but notes that country- and time-specific factors determine what is a reasonable level. OECD recognizes that the negative employment impact could be larger on the youth and other vulnerable groups. Case studies provide further evidence of the complexity of the debate. For example, studies on the minimum wage increase in New Jersey do not find any systemic impact even on firms that employ low-skilled labor, while studies on the sharp rise in the minimum wage in Seattle find a significant and negative impact on low-skilled labor. The case study on France shows that when the minimum wage is high relative to the average wage, it can lead to lower overall employment (Box).
The Overall Wage Level in the Economy
8. An increase in the minimum wage mechanically directly increases the overall wage level in the economy, and indirectly through the “ripple” effect. For example, in the U.S., a one percent increase in the minimum wage is estimated to increase payments to workers at or just above the minimum wage by 0.8 percent and payments to workers further up the wage scale around 0.25 – 0.4 percent. IMF work on CESEE countries also reveal a significant pass-through of minimum wage hikes to general wages, up to 0.15 percent over two years in response to a one percent increase in minimum wage.
An Example of the Impact of Minimum Wage Increases on Employment: France
In France a minimum wage was introduced in 1950. In 1970, the government established the “SMIC” (Salaire Minimum Interprofessionnel de Croissance), which links the minimum wage to the in-purchasing power of workers’ wages, as well as a measure of consumer price inflation. The government also decided to add discretionary increases on top of the formula (“coups de pouce”). This mechanism led to a substantial increase in the ratio of minimum to average wage from 39 percent in 1968 to above 50 since the 1980s.
These changes in minimum wage policy lowered wage inequality by increasing wages at the bottom. However, several studies showed that high minimum wages priced low-skilled workers and youth out of the market, thereby contributing to high unemployment.
Recognizing the negative impact of high minimum wages, the governments since 1990s have implemented a number of measures to alleviate the burden. They largely aimed to reduce labor costs through lowering the social contributions of employers and providing targeted tax credits to promote consumption of household services.
These policies managed to stabilize low-skilled employment. However, the fiscal cost of these measures was high at around 1 percent of GDP in 2007. To ensure that changes to the minimum wage better reflect economic conditions the government introduced a commission of independent experts in 2008 to give official advice each year on “coups de pouce.”
IMF Article IV discussions with France consistently recommended that France should consider keeping the ratio of minimum to average wage to below 50 percent, to avoid increasing unemployment of the young and the low-skilled. In the 2017 Article IV discussions, staff recommended to limit automatic minimum wage increases to inflation, and allow the future National Productivity Board to play an advisory role on wage setting, including by providing guidance on the link between wage dynamics and economic conditions.
9. Widespread non-compliance in both advanced and emerging economies dampens this impact. Non-compliance, for example, in the garment sector in California was observed in two-thirds of the factories. In the U.K., Low Pay Commission uncovered that more than 10 percent of the workers in the social care sector were paid less than the minimum wage they were entitled to receive.
Inequality
10. Minimum wage is an important tool to manage income inequality. And unlike most other policy measures, it does not involve direct fiscal costs.
11. There is broad consensus that in general minimum wages lower inequality, provided they are not high enough to significantly lower employment, especially for youth and low-skilled. In a seminal paper, Autor, Manning, and Smith (2016) reverse the question and argue that during 1979–89, the decline in the real value of the minimum wage was responsible for 30 to 55 percent of the raise observed in the lower tail inequality. Similar correlations were also found for the U.K. in the late 1980s and early 1990s. Several other academic works (Koeniger et al. (2007); Crivellaro (2014)) also find a positive correlation between minimum wages and lower wage inequality.
Competitiveness
12. Firms that face a higher minimum wage has three options: pass the cost on to the consumers; accept lower profits; or push up productivity. Empirical studies find evidence of all these factors in different countries, sectors, and periods. For example:
Pass-though. Aaronson (2001) find that in Canada and the U.S., restaurant prices generally rise when their wage bill rises, especially during the period minimum wage changes are legislated.
Lower profits. Bell and Machin (2015) find that the sizable change in the minimum wage in the U.K. that caught the markets by surprise led to significant falls in the stock market value of low-wage firms.
Higher productivity. Riley and Bodibene (2015) show that firms with a large share of low-skilled workers raised productivity in response to higher minimum wages.
The variety of the results suggest that the impact of higher minimum wages on competitiveness depends on country—and sector-specific characteristics.
Growth and Inflation
13. The impact of minimum wage increases on growth is controversial. Proponents of minimum wage increases argue that redistribution of income from high savers to high consumers, which tend to be those who receive low wages, would spur overall consumption and growth. An extreme case of this is Japan, where higher minimum wages are projected to reduce deflationary pressures and add to economic growth (Everaert and Ganelli (2017)). Opponents argue that higher minimum wages would weaken firms’ competitiveness and reduce employment.
14. On the empirical side, Onaran and Galanis (2012) look at the wage share in the economy during 1960–2007 and show that a decline in the wage share tends to lower growth in a number of countries, including Korea, and raise growth in others. They argue that at the global level, there is lack of wage-led growth and policies that would spur wage growth would strengthen the global recovery from the global financial crisis. Bassanini and Venn (2008) find a positive impact of minimum wage on productivity, and Askenazy (2003) finds that minimum wage increases speed up long-run growth in an open economy and that the growth surplus could be significant. Sabia (2015), on the other hand, finds no evidence that minimum wage increases were associated with changes in output, but finds a shift of output from low- to higher- skilled industries.
15. These studies do not take into account the economic cycle the economy is in to examine the impact of an increase in the minimum wage on growth and inflation. If there is slack in the economy, and if minimum wages are at moderate levels, a minimum wage hike could boost overall wages without significantly lowering employment. In this case, domestic demand could rise and help close the output gap. However, there are other cases where output gap may be negative and large because of lack of competitiveness. In these cases, wage moderation coupled with appropriate macro and structural policies could be the solution to higher growth. If the output gap is closed or positive, an increase in minimum wage could lead to higher domestic demand and inflation, which could trigger monetary tightening and slower growth.
16. There is evidence that minimum wage increases have a small and positive impact on inflation. Indeed, Lemos (2004) concludes that a 10 percent increase in the minimum wage in the U.S. would increase overall prices by no more than 0.4 percent (although food prices increase by 4 percent). This is consistent with the general finding that minimum wages create upward pressures on average wages, and that there is overall wage pass-through to inflation (e.g. Goretti (2008)).
C. The Case of Korea
17. In Korea, about 7 percent of workers receive the minimum wage. This compares to less than 5 percent in the U.S. and the U.K. and around 10 percent in France and Germany, which have significantly higher minimum wages.
Historical Analysis
18. To understand the historical relationship between minimum wages, average wages, and employment in Korea, we conducted an econometric analysis. Following the literature, we run a co-integrating regression, where the real minimum wage, real average wages, and employment were con-integrated. As exogenous variable, which also reflect the supply side, change in terms of trade, productivity growth, and population growth were used. A dummy variable was included to prevent the sharp changes during the Asian crisis to dominate the econometric results.
19. Our statistical analysis showed that historically the minimum wage in Korea has followed, rather than lead average wages (see Tables 1–8). A one percent increase in average real wages increased the real minimum wage a year later by about the same magnitude. Since the decision to set the minimum wage for a particular year is made in the previous year, this reverse causation suggests that minimum wage is based on past economic performance rather than expectations of future developments.
20. Moreover, in the short run, employment is positively correlated with average and minimum wages. This is consistent with the finding that average wages lead minimum wages. In the longer run, however, estimation results suggest that total employment declines with higher minimum wages. Furthermore, separating total employment into youth and non-youth employment, minimum wages explain about 30 percent of the variation in youth employment (corresponding to 1 percentage point), compared with around 10 percent for non-youth employment (corresponding to 0.2 percentage point). The estimation method controls for working age population growth and other exogenous factors. This result suggests that minimum wage increases tend to lower youth employment in the longer run. However, this result should be interpreted with caution—Lee (2008) analyzes the effect of Korea’s minimum wage increase on youth employment and does not find any statistically significant impact. This may be partially because in Korea youth are highly educated, and 60 percent of Korea’s unemployed youth are university graduates, and partly because of the fact that with youth population increase job mismatches have increased.
21. Minimum wage increases have had an impact on income distribution through two channels: wages and employment. Given that minimum wages have tended to follow average wages, and that they have not changed overall employment significantly, it is unlikely that increases in the minimum wage level have materially changed the overall income distribution after the initial adjustment when it was first established in late 1980s. Yoon (2015) suggested an opposing view: “with the rise of the working poor and many minimum wage workers in the lowest income group, minimum wage increases are deemed to have considerable impact on the working poor status.”
22. To understand the impact on competitiveness, we look at the overall growth of the Korean economy. The strength of exports show that competitiveness of the most export-oriented sectors has remained strong in recent history. Moreover, most exporters make little use of low-skilled labor; therefore, it is unlikely that they would be significantly affected by minimum wage increases, although large wage increases would reduce their competitiveness because exporters in general tend not to be able to pass on cost increases to prices. Regarding firms in service sectors, where the bulk of the minimum wage earners are employed, we have not come across any evidence that minimum wage hikes have unduly impacted these firms. This may be partly because they have more opportunities to pass on wage increases to prices, but more analysis is needed in this area to reach a firm conclusion.
23. Similarly, historically we have not seen a clear evidence of minimum wage increases affecting growth and inflation. This is likely because minimum wages have followed productivity growth and wages, not the other way round.
The 2018 Minimum Wage Hike
24. The 2018 minimum wage hike is unprecedented. First, the increase in the minimum wage is substantially higher than the productivity growth, wage growth, and inflation observed in recent past. And second, it is taking place at a time when youth unemployment is high and increasing. The only point in Korea’s history that is comparable to the size of the 2018 hike is 2001, when productivity growth and wage growth were very strong in the previous couple of years and youth employment was declining. Therefore, we should be cautious in using historical patterns to analyze the current case.
25. We expect the sharp increase in the minimum wage to push overall wages up. This is based on two arguments: first, this time around the increase in the minimum wage does not follow the historical pattern of reflecting past wage increases; and second, the increase is by far the largest in real terms since the minimum wage system was established, and should have an important ripple effect through the rest of the wage structure.
26. This increase may contribute to the difficulties low-skilled workers, youth, and the elderly are facing in finding employment. If the cost increase for some firms that typically hire young employees is too high for them to sufficiently pass on to prices, these firms may choose to reduce employment and increase labor productivity to safeguard profitability. Also, the elderly that own or are employed by small mom-and-pop shops will be vulnerable to closure of those shops. The final impact depends on how much the economy is generating employment in other areas.
Youth employment by monthly wage
(Below age 30, employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.Youth employment by monthly wage
(Below age 30, employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.Youth employment by monthly wage
(Below age 30, employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.27. Firms will face two opposing forces: higher domestic demand from workers who receive higher wages, and higher labor costs, which will increase costs. Currently Korea’s firms are overall in good health, as reflected in the strong increase in Korea’s stock market this year. Moreover, average wage appears broadly in line with average value added per worker, compared to other OECD countries (Figure), Nevertheless, those firms that cannot pass on the cost increases or commensurately become more productive will likely lose competitiveness and some of them will fall below the break-even point. These vulnerable firms are those that employ primarily low-skilled workers, so that their ability to migrate the work to higher-skilled labor is limited. In Korea, these firms tend to be in the service sector and are generally small, including mom-and-pop shops.
Productivity and Wages · 2016
(in USD, PPP)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources; OECD and staff calculations.Productivity and Wages · 2016
(in USD, PPP)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources; OECD and staff calculations.Productivity and Wages · 2016
(in USD, PPP)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources; OECD and staff calculations.
Employment by monthly wage
(all employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.Employment by monthly wage
(all employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.Employment by monthly wage
(all employed persons in 2016)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis, Staff calculations.28. The 2018 minimum wage hike should support growth, but may adversely affect some low-skilled workers. The hike will boost domestic demand, which has been the key missing element of growth in Korea. Domestic demand will increase because income will shift to lower-income workers, which have lower saving and higher consumption rates. However, some low-skilled workers may face greater difficulty finding jobs.
Consumption Rate per Quintile
(Consumption expenditure in percent of disposable income; income quintiles)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis.Consumption Rate per Quintile
(Consumption expenditure in percent of disposable income; income quintiles)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis.Consumption Rate per Quintile
(Consumption expenditure in percent of disposable income; income quintiles)
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Sources: Kosis.Policy Implications
29. Given the unprecedented nature of the minimum wage hike, the government should be mindful of the following policy options:
Analyze the impact of the 2018 wage hike before additional hikes. While the overall impact is expected to be positive, at this early stage it is necessary to monitor the impact on the youth, the elderly, smaller firms, and firms that mostly employ low-skilled labor. The literature on minimum wages confirms that a “moderate” minimum wage level would not be detrimental to the economy, but the devil is in the detail of deciding what a “moderate” level is. With the 2018 hike, Korea’s minimum wage level will be at many of the other advanced economies. Another large hike on the heels of the hike in 2018 may stress the labor markets and firms to such a degree that they may not be able to cope with it in a short period of time.
Boost active labor market policies. Those that lose employment as a result of the 2018 hike will need training to be more productive and earn higher wages. They will also need government support for matching jobs with their skills. Effective active labor market policies require significant resources. It would be preferable to channel resources to active labor market policies than to use resources to subsidize SME’s costs. The latter would prevent firms to adjust and become more efficient, and perpetuate the “Peter Pan Syndrome” where firms have incentives not to grow, which will undercut efforts to boost the growth of the economy.
Annex
Vector Error Correction Estimates
Vector Error Correction Estimates
| Vector Error Correction Estimates | |||
| Date: 08/01/17 Time: 14:12 | |||
| Sample (adjusted): 1994 2015 | |||
| Included observations: 22 after adjustments | |||
| Standard errors in () A t-statistics in [] | |||
| Cointeg rating Eq: | CointEql | ||
| DW(-1) | 1.000000 | ||
| DMW(-1) | −0.847257 | ||
| (0.39300) | |||
| [-2.15586] | |||
| DE(-1) | −5.831048 | ||
| (1.08247) | |||
| [-5.38677] | |||
| C | 9.710589 | ||
| Error Correction | D(DW) | D(DMW) | D(DE) |
| ColntEq1 | 0.041928 | 0.279085 | 0.274830 |
| (0.09415) | (0.12762) | (0.04089) | |
| [0.44532] | [2.13641] | [6.72189] | |
| D(DW(-1)) | −0.921586 | 0.119487 | −0.030794 |
| (0.23914) | (0.32414) | (0.10385) | |
| [-3.65382] | [ 0.36862] | [-0.29654] | |
| D(DW(-2)) | −0.441785 | 0.250011 | 0.382699 |
| [0.18898) | (0.25615) | (0.08206) | |
| [-2.33779] | [0.97602] | [4.66347] | |
| D(DMW(-1)) | 0.037859 | −0.351988 | 0.318631 |
| (0.20637) | (0.23041) | (0.08984) | |
| [0.18301] | [-1.25524] | [3.54682] | |
| D(DMW(-2)) | 0.111997 | 0.058571 | 0.090727 |
| (D.19497) | (0.26428) | (0.08467) | |
| [0.57443] | [0.22162] | [1.07158] | |
| D(DE(-1)) | 0.641835 | 1.451752 | 0.177944 |
| (D.38758) | (0.52536) | (0.16831) | |
| [1.65600] | [2.76335] | [1.05725] | |
| D(DE(-2)) | 0.251653 | 0.397559 | −0.020205 |
| (0.26689) | (0.36177) | (0.11590) | |
| [0.94290] | [ 2.43104] | [-0.17433] | |
| C | −0.184591 | 6.572368 | −0.823492 |
| (2.77134) | (3.75651) | (1.20346) | |
| [-0.06661] | [1.74960] | [-0.68427] | |
| DTT | 0.122179 | 0.132998 | 0.011973 |
| (0.12483) | (0.16920) | [0.05421) | |
| [0.97879] | [0.73603] | [0.22087] | |
| DUM | −7.910645 | 1.287457 | −5.030200 |
| (2.36818) | (3.21003) | (1.02839) | |
| [-3.34D39; | [ 0.40107] | [-4.89134] | |
| DP | 0.486205 | 0.180879 | 0.075538 |
Vector Error Correction Estimates
| Vector Error Correction Estimates | |||
| Date: 08/01/17 Time: 14:12 | |||
| Sample (adjusted): 1994 2015 | |||
| Included observations: 22 after adjustments | |||
| Standard errors in () A t-statistics in [] | |||
| Cointeg rating Eq: | CointEql | ||
| DW(-1) | 1.000000 | ||
| DMW(-1) | −0.847257 | ||
| (0.39300) | |||
| [-2.15586] | |||
| DE(-1) | −5.831048 | ||
| (1.08247) | |||
| [-5.38677] | |||
| C | 9.710589 | ||
| Error Correction | D(DW) | D(DMW) | D(DE) |
| ColntEq1 | 0.041928 | 0.279085 | 0.274830 |
| (0.09415) | (0.12762) | (0.04089) | |
| [0.44532] | [2.13641] | [6.72189] | |
| D(DW(-1)) | −0.921586 | 0.119487 | −0.030794 |
| (0.23914) | (0.32414) | (0.10385) | |
| [-3.65382] | [ 0.36862] | [-0.29654] | |
| D(DW(-2)) | −0.441785 | 0.250011 | 0.382699 |
| [0.18898) | (0.25615) | (0.08206) | |
| [-2.33779] | [0.97602] | [4.66347] | |
| D(DMW(-1)) | 0.037859 | −0.351988 | 0.318631 |
| (0.20637) | (0.23041) | (0.08984) | |
| [0.18301] | [-1.25524] | [3.54682] | |
| D(DMW(-2)) | 0.111997 | 0.058571 | 0.090727 |
| (D.19497) | (0.26428) | (0.08467) | |
| [0.57443] | [0.22162] | [1.07158] | |
| D(DE(-1)) | 0.641835 | 1.451752 | 0.177944 |
| (D.38758) | (0.52536) | (0.16831) | |
| [1.65600] | [2.76335] | [1.05725] | |
| D(DE(-2)) | 0.251653 | 0.397559 | −0.020205 |
| (0.26689) | (0.36177) | (0.11590) | |
| [0.94290] | [ 2.43104] | [-0.17433] | |
| C | −0.184591 | 6.572368 | −0.823492 |
| (2.77134) | (3.75651) | (1.20346) | |
| [-0.06661] | [1.74960] | [-0.68427] | |
| DTT | 0.122179 | 0.132998 | 0.011973 |
| (0.12483) | (0.16920) | [0.05421) | |
| [0.97879] | [0.73603] | [0.22087] | |
| DUM | −7.910645 | 1.287457 | −5.030200 |
| (2.36818) | (3.21003) | (1.02839) | |
| [-3.34D39; | [ 0.40107] | [-4.89134] | |
| DP | 0.486205 | 0.180879 | 0.075538 |
Vector Error Correction Estimates
Vector Error Correction Estimates
| (0.22777) | (0.30575) | (0.09891) | |
| [2.13458] | [0.58585] | [0.76369] | |
| DROP | −0.984724 | −5.335493 | 0.807566 |
| (2.40915) | (3.26556) | (1.04618) | |
| [-0.40874] | [-1.63387] | [0.77192] | |
| R-squared | 0.757105 | 0.592372 | 0.945591 |
| Adj. R-squared | 0.469920 | 0.143980 | 0.886740 |
| Sum sq. resids | 44.84456 | 82.39453 | 8.456573 |
| S.E. equation | 2.117653 | 2.870445 | 0.919596 |
| F-statistic | 2.633640 | 1.321104 | 15.79926 |
| Log likelihood | −39.05041 | −45.74189 | −20.69956 |
| Akaike AIC | 4.640946 | 5.249263 | 2.972688 |
| Schwarz SC | 5.236060 | 5.844377 | 3.567S02 |
| Mean dependent | −0.188636 | 0.197273 | 0.006364 |
| S.D. dependent | 2.965077 | 3.102468 | 2.720512 |
| Determinant resid covarla nee (dot adj.) | 23.04615 | ||
| Determinant resid covarla nee | 2.164364 | ||
| Log likelihood | −102.1433 | ||
| Akaike information criterion | 12.33121 | ||
| Schwarz criterion | 14.76533 | ||
Vector Error Correction Estimates
| (0.22777) | (0.30575) | (0.09891) | |
| [2.13458] | [0.58585] | [0.76369] | |
| DROP | −0.984724 | −5.335493 | 0.807566 |
| (2.40915) | (3.26556) | (1.04618) | |
| [-0.40874] | [-1.63387] | [0.77192] | |
| R-squared | 0.757105 | 0.592372 | 0.945591 |
| Adj. R-squared | 0.469920 | 0.143980 | 0.886740 |
| Sum sq. resids | 44.84456 | 82.39453 | 8.456573 |
| S.E. equation | 2.117653 | 2.870445 | 0.919596 |
| F-statistic | 2.633640 | 1.321104 | 15.79926 |
| Log likelihood | −39.05041 | −45.74189 | −20.69956 |
| Akaike AIC | 4.640946 | 5.249263 | 2.972688 |
| Schwarz SC | 5.236060 | 5.844377 | 3.567S02 |
| Mean dependent | −0.188636 | 0.197273 | 0.006364 |
| S.D. dependent | 2.965077 | 3.102468 | 2.720512 |
| Determinant resid covarla nee (dot adj.) | 23.04615 | ||
| Determinant resid covarla nee | 2.164364 | ||
| Log likelihood | −102.1433 | ||
| Akaike information criterion | 12.33121 | ||
| Schwarz criterion | 14.76533 | ||
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Johansen Cointegration Test
Johansen Cointegration Test
| Date: 08/02/17 Time: 09:01 | ||||
| Sample (adjusted): 1994 2015 | ||||
| Included observations: 22 after adjustments | ||||
| Trend assumption: Linear deterministic trend | ||||
| Series: DW DMW DE | ||||
| Exogenous series: DTT DUM DP DPOP | ||||
| Warning: Critical values assume no exogenous series | ||||
| Lags interval (in first differences): 1 to2 | ||||
| Unrestricted Cointegration Rank Test (Trace) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.527413 | 53.17176 | 29.79707 | 0.0000 |
| At most 1 | 0.394881 | 14.52096 | 15.49471 | 0.0697 |
| At most 2 | 0.145905 | 3.469679 | 3.841466 | 0.0625 |
| Trace test indicates 1 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| ** MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegration Rank Test (Maximum Eigenvalue) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Max-Eigen Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.827413 | 38.65080 | 21.13162 | 0.0001 |
| At most 1 | 0.394881 | 11.05125 | 14.26460 | 0.1517 |
| At most 2 | 0.145905 | 3.469679 | 3.841466 | 0.0625 |
| Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=1): | ||||
| DW | DMW | DE | ||
| −0.208539 | 0.176686 | 1.216002 | ||
| 0.261789 | 0.529335 | −1.543629 | ||
| −1.160915 | 0.104206 | 1.173728 | ||
| Unrestricted Adjustment Coefficients (alpha): | ||||
| D(DW) | −0.201055 | −0.690052 | 0.346756 | |
| D(DMW) | −1.336288 | −0.905181 | −0.434964 | |
| D(DE) | −1.317863 | −0.085882 | 0.017538 | |
| 1 Cointegrating Equation(s): Log likelihood -102.1433 | ||||
| Normalized cointegrating coefficients (standard error in parentheses) | ||||
| DW | DMW | DE | ||
| 1.000000 | −0.647257 | −5.831048 | ||
| (0.39300) | (1.08247) | |||
| Adjustment cointegrating coefficients (standard error in parentheses) | ||||
| D(DW) | 0.041928 | |||
| (0.09415) | ||||
| D(DMW) | 0.279085 | |||
| (0.12762) | ||||
| D(DE) | 0.274630 | |||
Johansen Cointegration Test
| Date: 08/02/17 Time: 09:01 | ||||
| Sample (adjusted): 1994 2015 | ||||
| Included observations: 22 after adjustments | ||||
| Trend assumption: Linear deterministic trend | ||||
| Series: DW DMW DE | ||||
| Exogenous series: DTT DUM DP DPOP | ||||
| Warning: Critical values assume no exogenous series | ||||
| Lags interval (in first differences): 1 to2 | ||||
| Unrestricted Cointegration Rank Test (Trace) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.527413 | 53.17176 | 29.79707 | 0.0000 |
| At most 1 | 0.394881 | 14.52096 | 15.49471 | 0.0697 |
| At most 2 | 0.145905 | 3.469679 | 3.841466 | 0.0625 |
| Trace test indicates 1 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| ** MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegration Rank Test (Maximum Eigenvalue) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Max-Eigen Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.827413 | 38.65080 | 21.13162 | 0.0001 |
| At most 1 | 0.394881 | 11.05125 | 14.26460 | 0.1517 |
| At most 2 | 0.145905 | 3.469679 | 3.841466 | 0.0625 |
| Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=1): | ||||
| DW | DMW | DE | ||
| −0.208539 | 0.176686 | 1.216002 | ||
| 0.261789 | 0.529335 | −1.543629 | ||
| −1.160915 | 0.104206 | 1.173728 | ||
| Unrestricted Adjustment Coefficients (alpha): | ||||
| D(DW) | −0.201055 | −0.690052 | 0.346756 | |
| D(DMW) | −1.336288 | −0.905181 | −0.434964 | |
| D(DE) | −1.317863 | −0.085882 | 0.017538 | |
| 1 Cointegrating Equation(s): Log likelihood -102.1433 | ||||
| Normalized cointegrating coefficients (standard error in parentheses) | ||||
| DW | DMW | DE | ||
| 1.000000 | −0.647257 | −5.831048 | ||
| (0.39300) | (1.08247) | |||
| Adjustment cointegrating coefficients (standard error in parentheses) | ||||
| D(DW) | 0.041928 | |||
| (0.09415) | ||||
| D(DMW) | 0.279085 | |||
| (0.12762) | ||||
| D(DE) | 0.274630 | |||
Johansen Cointegration Test
Johansen Cointegration Test
| (0.04089) | |||
| 2 Cointegrating Equation(s): | Log likelihood | −96.61770 | |
| Normalized cointegrating coefficients (standard eiror In parentheses) | |||
| DW | DMW | DE | |
| 1.000000 | 0.000000 | −5.850362 | |
| (0.84441) | |||
| 0.000000 | 1.000000 | −0.022796 | |
| (0.73688) | |||
| Adjustment coefficients (standard error in parentheses) | |||
| D(DW) | −0.13S720 | −0.400792 | |
| (0.13229) | (0.22057) | ||
| D(DMW) | 0.042119 | −0.715601 | |
| (0.18104) | (0.30185) | ||
| D(DE) | 0.251562 | −0.273900 | |
| (0.06434) | (0.10628) | ||
Johansen Cointegration Test
| (0.04089) | |||
| 2 Cointegrating Equation(s): | Log likelihood | −96.61770 | |
| Normalized cointegrating coefficients (standard eiror In parentheses) | |||
| DW | DMW | DE | |
| 1.000000 | 0.000000 | −5.850362 | |
| (0.84441) | |||
| 0.000000 | 1.000000 | −0.022796 | |
| (0.73688) | |||
| Adjustment coefficients (standard error in parentheses) | |||
| D(DW) | −0.13S720 | −0.400792 | |
| (0.13229) | (0.22057) | ||
| D(DMW) | 0.042119 | −0.715601 | |
| (0.18104) | (0.30185) | ||
| D(DE) | 0.251562 | −0.273900 | |
| (0.06434) | (0.10628) | ||
Vector Error Correction Estimates
Vector Error Correction Estimates
| Vector Error Correction Estimates | |||
| Date: 08/01/17 Time: 14:20 | |||
| Sample (adjusted): 1994 2015 | |||
| Included observations: 22 after adjustments | |||
| Standard errors in () & t-statisties in [] | |||
| Cointeg, rating Eq: | CointEq1 | ||
| DW(-1) | 1.000000 | ||
| DMW(-1) | −5.255119 | ||
| (11.1225) | |||
| [-0.47247] | |||
| DYE(-1) | −159.4390 | ||
| (21.6860) | |||
| [-7.23499] | |||
| C | −215.1063 | ||
| Error Correction: | D(DW) | D(DMW) | D(DYE) |
| CointEq1 | −0.000450 | 0.003651 | 0.011288 |
| 0.00249 | (0.00413) | (0.00242) | |
| [-0.18049] | [0.88371] | [4.65549] | |
| D(DW(-1)) | −0.911625 | 0.405529 | 0.502732 |
| (0.24348) | (0.40333) | (0.23668) | |
| [-3.74421] | [1.00545] | [2.12409] | |
| D(DW(-2)) | −0.440376 | 0.335475 | 0.679647 |
| (0.19571) | (0.32420) | (0.13025) | |
| [-2.25019] | [1.03478] | [3.57246] | |
| D(DMW(-1)) | −0.042276 | −0.509089 | 0.163324 |
| (0.18950) | (0.31391) | (0.18421) | |
| [-0.22310] | [-1.62174] | [0.88661] | |
| D(DMW(-2)) | 0.075653 | −0.093310 | −0.078853 |
| (0.16214) | (0.30173) | (0.17706) | |
| [0.41535] | [-0.30925] | [-0.44534] | |
| D(DYE(-1)) | 0.291351 | 0.589798 | 0.355958 |
| (0.25380) | (0.42044) | (0.24672) | |
| [1.14795] | [1.40283] | [1.44277] | |
| D(DYE(-2)i | 0.134145 | 0.352768 | 0.064183 |
| (0.15032) | (0.24902) | (0.14613) | |
| [0.89239] | [1.41664] | [0.43923] | |
| C | −0.263707 | 5.338282 | −2.27691S |
| (2.70116) | (4.47461) | (2.62578) | |
| [-0.09763] | [1.19302] | [-0.86714] | |
| DTT | 0.068767 | 0.115255 | 0.343225 |
| (0.13649] | (0.22610) | (0.13266) | |
| [0.50382] | [0.50374] | [2.56682] | |
| DUM | −7.755125 | 2.407322 | −4.183656 |
| (2.43305) | (4.03048) | (2.36516) | |
| [-3.18740] | [0.59728] | [-1.76687] | |
| DP | 0.473242 | 0.130813 | 0.126919 |
Vector Error Correction Estimates
| Vector Error Correction Estimates | |||
| Date: 08/01/17 Time: 14:20 | |||
| Sample (adjusted): 1994 2015 | |||
| Included observations: 22 after adjustments | |||
| Standard errors in () & t-statisties in [] | |||
| Cointeg, rating Eq: | CointEq1 | ||
| DW(-1) | 1.000000 | ||
| DMW(-1) | −5.255119 | ||
| (11.1225) | |||
| [-0.47247] | |||
| DYE(-1) | −159.4390 | ||
| (21.6860) | |||
| [-7.23499] | |||
| C | −215.1063 | ||
| Error Correction: | D(DW) | D(DMW) | D(DYE) |
| CointEq1 | −0.000450 | 0.003651 | 0.011288 |
| 0.00249 | (0.00413) | (0.00242) | |
| [-0.18049] | [0.88371] | [4.65549] | |
| D(DW(-1)) | −0.911625 | 0.405529 | 0.502732 |
| (0.24348) | (0.40333) | (0.23668) | |
| [-3.74421] | [1.00545] | [2.12409] | |
| D(DW(-2)) | −0.440376 | 0.335475 | 0.679647 |
| (0.19571) | (0.32420) | (0.13025) | |
| [-2.25019] | [1.03478] | [3.57246] | |
| D(DMW(-1)) | −0.042276 | −0.509089 | 0.163324 |
| (0.18950) | (0.31391) | (0.18421) | |
| [-0.22310] | [-1.62174] | [0.88661] | |
| D(DMW(-2)) | 0.075653 | −0.093310 | −0.078853 |
| (0.16214) | (0.30173) | (0.17706) | |
| [0.41535] | [-0.30925] | [-0.44534] | |
| D(DYE(-1)) | 0.291351 | 0.589798 | 0.355958 |
| (0.25380) | (0.42044) | (0.24672) | |
| [1.14795] | [1.40283] | [1.44277] | |
| D(DYE(-2)i | 0.134145 | 0.352768 | 0.064183 |
| (0.15032) | (0.24902) | (0.14613) | |
| [0.89239] | [1.41664] | [0.43923] | |
| C | −0.263707 | 5.338282 | −2.27691S |
| (2.70116) | (4.47461) | (2.62578) | |
| [-0.09763] | [1.19302] | [-0.86714] | |
| DTT | 0.068767 | 0.115255 | 0.343225 |
| (0.13649] | (0.22610) | (0.13266) | |
| [0.50382] | [0.50374] | [2.56682] | |
| DUM | −7.755125 | 2.407322 | −4.183656 |
| (2.43305) | (4.03048) | (2.36516) | |
| [-3.18740] | [0.59728] | [-1.76687] | |
| DP | 0.473242 | 0.130813 | 0.126919 |
Vector Error Correction Estimates
Vector Error Correction Estimates
| (0.21937) | (0.36339) | (0.21325) | |
| [2.15731] | [0.35998] | [0.59518] | |
| DROP | −1 029590 | −4.224148 | 2.934076 |
| (2.33884) | (3.87440) | (2.27357) | |
| [-0.44021] | [-1.09027] | [1.29051] | |
| R-squared | 0.767611 | 0.417518 | 0.882539 |
| Adj. R-squared | 0.511983 | −0.223212 | 0.753332 |
| Sum sq. reslds | 42.90486 | 117.7379 | 40.54375 |
| S.E. equation | 2.071349 | 3.431295 | 2.013548 |
| F-stofistic | 3.002847 | 0.651629 | 6.330413 |
| Log likelihood | −38.56402 | −49.66825 | −37.94138 |
| Akaike AIC | 4.596729 | 5.606204 | 4.540125 |
| Schwarz SC | 5.191843 | 6.201318 | 5.135239 |
| Mean dependent | −0.188336 | 0.197273 | 0.198182 |
| S.D. dependent | 2.965077 | 3.102468 | 4.054201 |
| Determinant resid covariante (dof adj.) | 61.17289 | ||
| Determinant resid covariante | 5.745012 | ||
| Log likelihood | −112.8816 | ||
| Akaike information criterion | 13.80742 | ||
| Schwarz criterion | 15.74154 | ||
Vector Error Correction Estimates
| (0.21937) | (0.36339) | (0.21325) | |
| [2.15731] | [0.35998] | [0.59518] | |
| DROP | −1 029590 | −4.224148 | 2.934076 |
| (2.33884) | (3.87440) | (2.27357) | |
| [-0.44021] | [-1.09027] | [1.29051] | |
| R-squared | 0.767611 | 0.417518 | 0.882539 |
| Adj. R-squared | 0.511983 | −0.223212 | 0.753332 |
| Sum sq. reslds | 42.90486 | 117.7379 | 40.54375 |
| S.E. equation | 2.071349 | 3.431295 | 2.013548 |
| F-stofistic | 3.002847 | 0.651629 | 6.330413 |
| Log likelihood | −38.56402 | −49.66825 | −37.94138 |
| Akaike AIC | 4.596729 | 5.606204 | 4.540125 |
| Schwarz SC | 5.191843 | 6.201318 | 5.135239 |
| Mean dependent | −0.188336 | 0.197273 | 0.198182 |
| S.D. dependent | 2.965077 | 3.102468 | 4.054201 |
| Determinant resid covariante (dof adj.) | 61.17289 | ||
| Determinant resid covariante | 5.745012 | ||
| Log likelihood | −112.8816 | ||
| Akaike information criterion | 13.80742 | ||
| Schwarz criterion | 15.74154 | ||
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Response to Cholesky One S. D. Innovations
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Variance Decomposition
Citation: IMF Staff Country Reports 2018, 041; 10.5089/9781484341636.002.A007
Johansen Cointegration Test
Johansen Cointegration Test
| Date: 08/02/17 Time: 09:02 | ||||
| Sample (adjusted): 1994 2015 | ||||
| Included observations: 22 after adjustments | ||||
| Trend assumption: Linear deterministic trend | ||||
| Series: DW DMW DYE | ||||
| Exogenous series: DTT DUM DP DROP | ||||
| Warning: Critical values assume no exogenous series | ||||
| Lags interval (In first differences): 1 to 2 | ||||
| Unrestricted Cointegration Rank Test (Trace) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob.** |
| Nene * | 0.855680 | 63.20067 | 29.79707 | O.OOCC |
| At most 1 * | 0.546277 | 20.61483 | 15.49471 | 0.0077 |
| At most 2 | 0.136506 | 3.228909 | 3.841466 | 0.0723 |
| Trace test indicates 2 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegration Rank Test (Maximum Eigenvalue) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Max-Elgen Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.855680 | 42.58583 | 21.13162 | 0.0000 |
| At most 1 * | 0.546277 | 17.38592 | 14.26460 | 0.0155 |
| At most 2 | 0.136506 | 3.228909 | 3.841465 | 0.0723 |
| Max-eigenvalue test indicates 2 cointegratjng eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointeg rating Coefficients (normalized by b’*S11*b=1): | ||||
| DW | DMW | DYE | ||
| −0.005648 | 0.029681 | 0.900512 | ||
| 0.290752 | −0.516266 | 0.086314 | ||
| 1.058762 | −0.056587 | −0.896632 | ||
| Unrestricted Adjustment Coefficients (alpha): | ||||
| D(DW) | 0.079705 | 0.548191 | −0.437010 | |
| D(DMW) | −0.546485 | 1.628364 | 0.241526 | |
| D(DYE) | −1.998566 | 0.702985 | −0.190043 | |
| 1 Cointeg rating Equation(s): | Log likelihood | −112.8816 | ||
| Normalized cointeqratina coefficients (standard error In parentheses) | ||||
| DW | DMW | DYE | ||
| 1.000000 | −5.255119 | −159.4390 | ||
| (11.1226) | (21.8860) | |||
| Adjustment coefficients (standard error in parentheses) | ||||
| D(DW) | −0.000450 | |||
| (0.00249) | ||||
| D(DMW) | 0.003651 | |||
| (0.00413) | ||||
| D(DYE) | 0.011288 | |||
Johansen Cointegration Test
| Date: 08/02/17 Time: 09:02 | ||||
| Sample (adjusted): 1994 2015 | ||||
| Included observations: 22 after adjustments | ||||
| Trend assumption: Linear deterministic trend | ||||
| Series: DW DMW DYE | ||||
| Exogenous series: DTT DUM DP DROP | ||||
| Warning: Critical values assume no exogenous series | ||||
| Lags interval (In first differences): 1 to 2 | ||||
| Unrestricted Cointegration Rank Test (Trace) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Trace Statistic | 0.05 Critical Value | Prob.** |
| Nene * | 0.855680 | 63.20067 | 29.79707 | O.OOCC |
| At most 1 * | 0.546277 | 20.61483 | 15.49471 | 0.0077 |
| At most 2 | 0.136506 | 3.228909 | 3.841466 | 0.0723 |
| Trace test indicates 2 cointegrating eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointegration Rank Test (Maximum Eigenvalue) | ||||
| Hypothesized No. of CE(s) | Eigenvalue | Max-Elgen Statistic | 0.05 Critical Value | Prob.** |
| None * | 0.855680 | 42.58583 | 21.13162 | 0.0000 |
| At most 1 * | 0.546277 | 17.38592 | 14.26460 | 0.0155 |
| At most 2 | 0.136506 | 3.228909 | 3.841465 | 0.0723 |
| Max-eigenvalue test indicates 2 cointegratjng eqn(s) at the 0.05 level | ||||
| * denotes rejection of the hypothesis at the 0.05 level | ||||
| **MacKinnon-Haug-Michelis (1999) p-values | ||||
| Unrestricted Cointeg rating Coefficients (normalized by b’*S11*b=1): | ||||
| DW | DMW | DYE | ||
| −0.005648 | 0.029681 | 0.900512 | ||
| 0.290752 | −0.516266 | 0.086314 | ||
| 1.058762 | −0.056587 | −0.896632 | ||
| Unrestricted Adjustment Coefficients (alpha): | ||||
| D(DW) | 0.079705 | 0.548191 | −0.437010 | |
| D(DMW) | −0.546485 | 1.628364 | 0.241526 | |
| D(DYE) | −1.998566 | 0.702985 | −0.190043 | |
| 1 Cointeg rating Equation(s): | Log likelihood | −112.8816 | ||
| Normalized cointeqratina coefficients (standard error In parentheses) | ||||
| DW | DMW | DYE | ||
| 1.000000 | −5.255119 | −159.4390 | ||
| (11.1226) | (21.8860) | |||
| Adjustment coefficients (standard error in parentheses) | ||||
| D(DW) | −0.000450 | |||
| (0.00249) | ||||
| D(DMW) | 0.003651 | |||
| (0.00413) | ||||
| D(DYE) | 0.011288 | |||
Johansen Cointegration Test
Johansen Cointegration Test
| (0.00242) | |||
| 2 Cointegrating Equation(s): | Log likelihood | −104.1886 | |
| Normalized cointegrating coefficients (standard enor in parentheses) | |||
| DW | DMW | DYE | |
| 1.000000 | 0.000000 | 81.81172 | |
| (10.8073) | |||
| 0.000000 | 1.000000 | 45.90776 | |
| (6.04315) | |||
| Adjustment coefficients (standard error in parentheses) | |||
| D(DW) | 0.158938 | −0.230647 | |
| (0.11812) | (0.21004) | ||
| D(DMW) | 0.477102 | −0.859858 | |
| (0.15111) | (0.26871) | ||
| D(DYE) | 0.215633 | −0.422247 | |
| (0.10680) | (0.18991) | ||
Johansen Cointegration Test
| (0.00242) | |||
| 2 Cointegrating Equation(s): | Log likelihood | −104.1886 | |
| Normalized cointegrating coefficients (standard enor in parentheses) | |||
| DW | DMW | DYE | |
| 1.000000 | 0.000000 | 81.81172 | |
| (10.8073) | |||
| 0.000000 | 1.000000 | 45.90776 | |
| (6.04315) | |||
| Adjustment coefficients (standard error in parentheses) | |||
| D(DW) | 0.158938 | −0.230647 | |
| (0.11812) | (0.21004) | ||
| D(DMW) | 0.477102 | −0.859858 | |
| (0.15111) | (0.26871) | ||
| D(DYE) | 0.215633 | −0.422247 | |
| (0.10680) | (0.18991) | ||
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