The more a country engages in oil trade, the more it is open to the elements of price shocks on global commodity markets (Rentschler, 2013). Pindyck (1991) argues that changes in energy prices create ambiguity about future energy prices, causing firms to postpone irreversible investment decisions in reaction to the outlook for profits. Hassan and Ratti (2012) argue that oilprice volatility influence stock prices through affecting expected cash flows and discount rates. Oilprice shocks affect corporate cash flow since oil is an input in production and because oilprice changes can influence the demand for output at industry and national levels. Rentschler (2013) posits that the impacts of sudden changes in oil prices can have detrimental effects and repercussions throughout the economy, disturbing macro-indicators such as employment, trade balance, inflation and public accounts, as well as stock market prices and exchange rates.
The sample Hamilton (1983) used to estimate his model, 1949Q2-1972Q4, did not include major oilprice decreases, as Mork (1989) noted, and thus was not likely to hold after the large drop of the oilprice in 1986. To investigate the validity of this assumption, we estimate Hamilton’s VAR model and apply recursive exclusion tests, which are explained below, for all oilprice coefficients in the GDP equation. This model includes four lags and the following variables: the log change in the real GDP, the log change of the GDP implicit price deflator, the log change of the average hourly earnings in the manufacturing sector, the log change of the oilprice specification, the level of the US civilian unemployment rate and the level of the three months Treasury bill rate (the secondary market one). The data is provided by the Federal reserve Bank of St Louis, taken at a quarterly rate (a simple average is performed for the monthly data), and spans the period starting in the 1st quarter of 1948 and ending in the 2 nd quarter of 2009, which offers 246 observations.
173 Enormous literature exists on the theoretical and empirical linkages between energy and economic growth for review. The study of (Dasgupta et al., 2002) shows a strong correlation between oil prices and the economic growth in the exporting countries. Energy, especially oil prices have always played a crucial role in determining the cycles of the world economy, inclusive of both oil producing and oil importing countries. Therefore, higher oil prices lead to inflation, increased input costs, lower investment and reduced non-oil demand. Revenue from tax declines and the budget deficit rises. This is due to government expenditure rigidities, which moves interest rates up. As a result of resistance to real fall in wages, rise in oilprice may typically lead to upward pressure on nominal wage levels. Pressures in wages together with reduced demand lead to higher level of unemployment, at least in the short term. Majid (2006) notes that these effects are greater, more sudden and more pronounced when the prices rise and are magnified by the impact of higher prices on business and consumer confidence. Nonetheless, this degree of the direct effect of a given price increase depends on the share of the cost of oil in national income, the magnitude of dependence on imported oil and the ability of end-users to reduce their consumption and switch away from oil. In addition, Majid (2006) notes that this also depends on the extent of increase in oil prices, the oil intensity of the economy and the impact of higher prices on other forms of energy that compete with oil.
This paper examines the impact oil prices has on economic growth in Nigeria from 1980 to 2016. An exploratory data analysis is employed using secondary data, employing the unit root test for stationarity, the co-integration to test for longrun relationship between the variables and finally the OLS estimating for the relationship between the key and control variables in concordance with our objectives. The research found that there is a significant and positive relationship between oilprice changes and economic growth in Nigeria. In the short-run, Nigeria was able to have increasing growth because of the high global oil prices, but in the long-run, the inconsistency of oil prices and lack of diversification of the productive base has not really helped the Nigeria economy. Thus, the research suggests that oil prices are the cause of Nigeria’s volatile growth rate. A combination of strict fiscal policy focused on the actual implementation of developmental strategy, diversification and industrialization might be effective to protect the country’s economy and lead to increasing and consistent economic progress.
have low power against trend stationary alternatives, we also use the KPSS test [see Kwiatkowski, Phillips, Schmidt, and Shin, 1992] to test the null hypothesis of stationarity. As shown in Table 1, the null hypothesis of a unit root cannot be rejected at conventional significance levels by both the ADF and DF-GLS test statistics in all data. Moreover, the null hypothesis of stationarity can be rejected at conventional significance levels by the KPSS test in some series; however, it can be accepted to be rejected approximately in all series. We thus conclude that real GDP and the real oilprice for Iran are nonstationary, or integrated of order one, I(1).
How will the drop in oil prices affect European economies? Bert Scholtens argues that, the general expectation is that a fall in oil prices should assist economic growth in Europe. However, there are several factors that are important in how the fluctuating oilprice transmits into individual economies. Oil prices are very volatile in recent periods. During the last six months, prices have fallen by more than 60 per cent. Some economists suggested an increase in oil prices of $20 per barrel will cause the GDP growth of a particular country to drop by 0.25 per cent in the first year and 1 per cent after three years. For the sake of convenience, the impact of an oilprice reduction could be assumed to be the opposite. Many analysts are therefore quite optimistic about the effect on the economies of most European countries.
A number of studies have investigated the effects of oilprice shocks on various macroeconomic variables in the case of Nigeria. For example, Ayadi et al. (2000) examine the impact of the energy (or oil) sector on the Nigerian economy, including the financial markets, using standard vector autoregression (VAR) and find the energy sector exerts significant influence on the economy. In addition, Ayadi (2005) analyzes the relation between oilprice changes and economic development via industrial production with standard VAR and finds that an increase in oil prices does not lead to an increase in industrial production in Nigeria. Recent studies have revisited the issue. For example, Chuku et al. (2011) assess the relation between oilprice shocks and current account dynamics in Nigeria using a standard VAR and find oilprice shocks to have a significant short-run effect on current account balances. Moreover, Iwayemi and Fowowe (2011) find oilprice shocks to have a weak impact on most macroeconomic variables in Nigeria.
Since the past decades, humans watched the changes of politic and economy in the world from the small campaign into huge riot. This kind of events happened all over the world due to the demand for the better administration and ruler had increased until today. Nowadays, the world witnessed the effects of the political economic on the human needs. However, oilprice is the major parts of the human needs which affected due to the world political economy revolution. As the revolution increase from time to time, the oilprice faced challenges and dilemma in terms of the rise and fall of the oil prices. Oil industry plays huge roles on the social development and economy as it effects all the countries in the world as well. Nevertheless, the oilprice cannot be analyzed theoretically and practically based on the political issues in the oil producer country while political economy hold the vital role in determining the right price of the oil in the world.
In this paper, the behavior of real oilprice and OPEC and non- OPEC production behavior during 1973-1996 and 1997-2013 are modelled. Interactions among OPEC and non-OPEC oil production, global oil consumption, and real oilprice are estimated using a structural VAR model (SVAR).The contribution of this study is to analyze the behavior of the real price of crude oil, OPEC and non- OPEC oil production by taking global oil consumption into account. Gately (2007) stated that in comparing OPEC to non-OPEC oil production as a combination of world oil production, it is important to recognize that oil consumption in OPEC countries is rapidly increasing. Gately et al. (2013) point out that since 1970 domestic consumption of OPEC oil has risen steeply and that collectively in recent years OPEC oil consumption approaches that of china. They argued that this result would be associated with major consequences for OPEC oil production, export level, and global oilprice. Kilian and Hicks (2013) indicate that rapid growth of emerging economies led to increase in the real price of crude oil during 2003-2008. In the following, the existing literature is reviewed. Stationary of oil prices, with respect to exogenous and endogenous structural breaks, is the subject of section three. Research model and the time path of its variables are explained in section four. Model estimation and conclusion are given in section five and six.
In order to assess the relevance of a peso problem inherent in forecasters expectations we conduct the following experiment. As in Froot and Thaler (1990) we assume that forecasters have in mind two possible states of the future oilprice. One state or regime consists of the idea that the oilprice further follows its bubble path and the second state implies the return to its fundamental value. Estimating a two-state Markov regime-switching model then provides us with a time-varying (smoothed) probability, which forecasters have assigned to the bubble-bursting regime. 5
Past Nigeria specific studies on oilprice shocks-macroeconomy association had earlier discovered a significant real effective exchange rate appreciation, which is suggestive of the existence of “Dutch Disease” in Nigeria. As a result, this paper undertook a detailed investigation into oilprice shocks-non-oil macroeconomy association, which it believes should be the first major step to solving the “Dutch Disease” problem in Nigeria. Analysis was conducted using linear and non-linear variants of oilprice, employing the multivariate Vector Autoregressive (VAR) and Vector Error Correction (VEC ) models respectively. Results indicate that both measures of oilprice account for remarkable changes in real exchange rates, and the transmission effects of these variations on non-oil export and import are both negative. On the bases of this, the paper recommend policies geared towards evolving realistic and stable exchange rates for the naira, to complement current efforts being made to diversify the economy in the direction of non-oil productions.
There is a large body of research on analyzing production reactions to oilprice changes (see Lee & Ni (2002), Ramcharran (2002), Fukunaga et al. (2010), among others). The theoretical literature states that crude oil is a basic raw material at many production levels and a rise in its price increases production costs, which give rise to a drop in productivity due to the use of a more costly input. Higher costs seem to be insufficient to explain the observed effects of oilprice fluctuations on production (see, e.g., Rotemberg & Woodford (1996), Atke- son & Kehoe (1999)) and the related literature has tried to find complementary explanations. Some of these explanations are based on the gradual decline in the share of oil in total gross value added and consumption (see Blanchard & Gal´ı (2010)), 1 the existence of different manufacturing structures or the rigidities in
Concerning the interest rates relationship with FSI included, we get some more significant results on especially short rates. For the symmetric variable we gain a significant granger causality to Canadian short rates where the relationship is volatile. On the NOPD variable we lose the significant relationship to Canadian long rates while we gain a significant relationship to U.S long rates where we find a volatile reaction. For the O variable we lose a significant relationship to Canadian long rates and gain one to Canadian short rates. Both of these new relationship is volatile. We also gain a new significance between the O+ and Canadian short rates . The sample we test the financial stress variable on is limited and it is thus hard to draw any general conclusion about the effect of financial stress. But for these two economies there were definitely a difference in results. Most new and lost results came from the relationship between the oilprice variables and interest rates. This could be an indication that the theory lifted by Balke, Brown and Yucel, (2002) that the asymmetric relationship between interest rates and oilprice changes could be attributed to financial stress. The lost relationships between the interest rate and oilprice changes could be caused by the variation previously attributed to interest rates where in fact variation coming from some sort of financial stress and when the FSI variable was added the relationship between changes in oilprice and interest rates became insignificant.
We develop two- and three-state Regime-Switching (RS) models and test their forecasting ability for oil prices. Taking advantage of the deviations we periodically observe between the market price of oil and its fundamental value, our models relate the expected gross return in the oilprice to deviations from fundamentals and an additional explanatory variable. Speci…cally, we compare the predictive power (in both statistical and economic evaluation terms) of twelve alternative macroeconomic/indicator variables assuming a forecast horizon of one month. Our …ndings indicate substantial bene…ts, in terms of forecasting accuracy, when RS models are employed relative to the Random Walk (RW) benchmark, especially in economic evaluation terms. Moreover, the RS models enriched with one of the predictors proposed in this study often outperform simple RS models that contain no predictors (other than deviations from fundamentals).
Figure 1 displays the impulse responses over the first 20 quarters after a 10 percent oilprice shock, which corresponds to the estimated standard deviation of the residuals of the oil equation in the VAR model. The solid lines denote impulse responses, which are computed from the estimated VAR coefficients. The shaded areas are the 68 percent Hall percentile confidence intervals, which are constructed from a bootstrap procedure with 1000 replications (Hall, 1994). All variables are expressed in percent terms, except for the nominal short–term interest rate which is expressed in basis points at an annual rate (100 basis points equal one percent). When a ten percent oilprice shock hits the economy, there is an immediate increase in the general price level of roughly 0.1 percent. This is not a surprise because the oilprice has a direct and instantaneous impact on the prices for heating oil and gasoline. At the same time, the short–term interest rate is shifted upwards, presumably in an attempt of the central bank to counter the inflationary impulse, and the real exchange rate devaluates. In the subsequent quarters, the price effect increases further to a maximum of 0.15 percent after four quarters before it slowly dies out. This reflects the well-known sluggishness of a wide array of consumer prices. For example, the prices for alternative energy sources like natural gas and for services like transport typically react with a delay. In addition, it may take some time, until producers pass on their cost increases to consumers. Finally, second–round effects operating through wage negotiations are implemented with some delay. Mirroring the price reaction, the short–term interest rate hike continues for some quarters. Subsequently, it is quickly taken back and reaches a trough after 12 quarters. This probably reflects the attempt of the central bank to stabilize the economy as real output starts to decline after two, and reaches a trough after ten, quarters.
declining consistently since the 80s, and the sector’s share of GDP is quite limited. If our macro scenario holds (oilprice stabilization, China slowdown but no hard landing, very slow normalization of Fed policy and relative resilience of US economy) the recent selloff can provide opportunities to investors, especially in terms of income. We should, however, acknowledge that volatility is expected to remain very high, and therefore we believe a long-term approach is needed. At the same time, a re-rating of inflation expectations has hit inflation-related assets (i.e., bond linkers), which now discount a very negative outlook. We believe that the market is underestimating the inflation pressures that could come from wage growth (in the US), and therefore we believe that opportunities may open up in the linkers segment.
Columns (2) and (3) of Table (6) report results when the model includes the oil shock variables, ∆𝑜 𝑡 and 𝑜 𝑡−1 where 𝑜 𝑡 = 𝑝𝑔𝑚𝑎𝑥(12) 𝑡 and 𝑝𝑔𝑚𝑎𝑥(12) 𝑡 is equal to the growth rate of real oilprice 𝑝𝑔 𝑡 if 𝑝𝑔 𝑡 is greater than the maximum of the last 12 months and zero otherwise. In both regressions the effects of the oil shock variables are measured by the change in oilprice shock and the lag of oilprice shock. Firm stock price volatility is excluded in column (2) but included in column (3). The main objective of column (3) is to compare the results when two oil shock variables are added to Bloom et al.‟s (2007) investment model , equation (6). In both columns (2) and (3) the coefficient estimate of the lag of oil shock, 𝑜 𝑡−1 , is statistically significant. 23 The result is consistent with the findings of Elder and Serletis (2010) and Rahman and Serletis (2010), among others, in that oilprice shocks affect long-run firm-level capital accumulation. Note that an interaction term between oilprice shock and sales growth 𝑜 𝑡 ∗ ∆𝑦 𝑖𝑡 is included in both columns (2) and (3). This term has a negative coefficient and is statistically significant in column (2) but not in column (3). The results, not shown, suggest that the exclusion of the interaction term do not alter the significance of other coefficient estimates in columns (2) and (3).
K.S. Sujit and B.RajeshKumar (2011) their study examined the dynamic relationship between gold price, oilprice, stock market returns and exchange rate. The study focused with the objective of validate the relationship systematically.to examine the impact of oilprice, exchange rate and stock market on gold price Vector Auto Regression(VAR) technique has been used. The study takes gold daily price in dollar and other currencies data from world gold council and from yahoo finance website. The study conclude that the simple relationship between currencies through a single common commodity does not exist and the inter linkage between gold price, oilprice and exchange rates are all complex in nature.it is clear from analysis that the variations in gold prices are largely depends on gold itself rather than exchange rate, oilprice and other indices. Jana simkova(2010) conducted a study on analysis of the relationship between oil and gold prices. The aim of the study is to analyze the relationship between gold and oilprice levels. The research was conducted for the periods of 1970-2010 and then adapted separately to each quantitative analysis. Correlation is used as one of the tools for analysis. Strong positive correlation in the whole sample between gold and oil was found out. Proportional analysis confirmed that gold/oil ratio is during this period moving on its long term values. Both gold and oil prices were influenced by certain factors. Correlation analysis revealed that it in case of inflation, industry, stock prices of gold mining companies and interest rates. The study also conducted a granger causality test. It helps to identify the causal links between gold and oilprice levels.
The first mechanism is elaborately studied in the literature. In fact, numerous researchers have studied the effects of oilprice changes on economic activity and discussed the mechanisms through which these effects transmit to other macroeconomic indicators (e.g. Hamilton, 1983, 1996; Pindyck and Rotemberg, 1983; Bernanke et al., 1997; Bernanke, 2004; Devlin and Lewin, 2004; Cologni and Manera, 2007). In addition to these papers which are focused on industrialized oil importing economies, some have studied developing -or recently developed- oil importing countries (e.g. Ziramba, 2010 in South Africa, Bashiri and Manso, 2012 in Portugal, Ghosh, 2011 in India and Ou and et al., 2012 in China) as well as oil exporting countries (e.g. Dibooğlu and Aleisa, 2004 in Saudi Arabia; Mehrara and Oskui, 2007 in four oil exporters; Lescaroux and Migno, 2008 in OPEC members; and Mehrara and Mohaghegh, 2012 in oil exporting countries). All these studies have confirmed that oilprice change is an important source of macroeconomic fluctuations both in national and global level. In brief, as He et al. (2010) assert, oilprice movements systematically change economic indicators in the world market in both short- and long-run (He et al., 2010). So, evidently oilprice affects both supply and demand sides of the methanol world market.
The ADF test, LM test with two breaks, and GUR test reject a stochastic trend at the 5% level except for model (1), providing strong evidence against a stochastic trend. The sharp difference between (1) and (2) through (4) illustrates the importance of allowing for the nonlinear specification when examining the time series properties of oilprice. Nonlinear specification is also emphasized by Ahrens and Sharma (1997) and Lee, List and Strazicich (2006).