A. The GVAR-Oil model

To simplify the exposition we consider the simple dynamic oil price equation (here we set all lag orders to unity, but consider more general dynamics in the empirical application)

where

y_it and $q_{it}^o$ are the real income and quantity of oil output of country i at time t, respectively, w_i and $w_i^o$ are the weights attached to country i’s real income and oil production in the construction of the world GDP (y_t) and oil supply $\left(q_t^o\right){\tilde{p}}_t^o$ is the weighted average of country-specific log real oil prices, defined by

$P_t^o$ is the nominal price of oil in US dollar, E_it is country i^th exchange rate measured by the units of country i^th currency in one US dollar, and P_it is the general level of prices in country i. $u_t^o$ represents the global oil demand shock to be distinguished from country-specific oil supply shocks defined in the country-specific models (specified below). The above decomposition of country-specific real oil prices into the US dollar price component and the “real” exchange rate component (here defined by ep_it = e_it − p_it) is important, since only the US dollar oil price component, $p_t^o$, can be regarded as weakly exogenous. The real exchange rate component, ep_it, is determined endogenously with the other variables in the country-specific models, such as interest rates and real output.

In order to integrate the oil price equation within a multi-country set-up we need to write the oil price equation in terms of $p_t^o$. To this end using (4) in (3) we first note that ${\tilde{p}}_t^o=p_t^o+e{p}_t$,

where ³

Using this result the oil price equation can be written as

In the GVAR set-up, the country-specific variables, ep_it, y_it and $q_{it}^o$, are determined jointly with the other macro variables. Specifically, we consider the following country-specific models (for i = 1, 2, …, N)

where a_i0, a_i1, Φ_i, Λ_i0, Λ_i1, Υ_i0 and Υ_i1 are vectors/matrices of fixed coefficients that vary across countries, x_it is k_i × 1 vector of country-specific endogenous variables that include ep_it, y_it, and $q_{it}^o$ (as applicable), and ${\text{x}}_{it}^{*}$ is $k_i^{*}\times 1$ vector of country-specific weakly exogenous (or ‘star’ variables). The ‘star’ variables, ${\text{x}}_{it}^{*}$, are constructed using country-specific trade shares, and defined by

where w_ij = 1, 2, … N, are bilateral trade weights, with wⁱⁱ = 0, and ${\mathrm{\Sigma }}_{j=1}^N{w}_{ij}=1$.

In our application each country-specific model has a maximum of six endogenous variables. Using the same terminology as in equation (7), the k_i × 1 vector of country-specific endogenous variables is defined as ${\text{x}}_{it}={(q_{it}^o,y_{it},{\pi }_{it},e{p}_{it},r_{it}^S,r_{it}^L,)}^{\prime }$, where ${\text{x}}_{it}^{*}$ is the log of oil production at time t for country i, y_it is the log of real Gross Domestic Product, π_it is the rate of inflation, ep_it is the log deflated exchange rate, and $r_{it}^S\left(r_{it}^L\right)$ is the short (long) term interest rate, if country i is a major oil producer, otherwise ${\text{x}}_{it}=(y_{it},{\pi }_{it},e{p}_{it},r_{it}^S,r_{it}^L,{)}^{\prime }$.⁴ The model for the US differs from the rest in two respects: given the importance of US financial variables in the global economy, the log of world real equity prices, eq_t, is included in the US model as an endogenous variable, and as weakly exogenous in the other country models $(e{q}_{it}^{*}=e{q}_t)$ whilst US dollar exchange rates are included as endogenous variables in all models except for the United States. The endogenous variables of the US model are therefore given by ${\text{x}}_{US,t}=(e{q}_t,q_{US,t}^o,y_{US,t},{\pi }_{US,t},r_{US,t}^S,r_{US,t}^L{)}^{\prime }$.

In the case of all countries, except for the US and the euro area, the foreign variables included in the country-specific models, computed as in equation (8), are given by ${\text{x}}_{it}^{*}=(e{q}_{it}^{*},y_{it}^{*},{\pi }_{it}^{*},e{p}_{it}^{*},r_{it}^{*S},r_{it}^{*L}{)}^{\prime }$. The trade weights are computed as three-year averages over 2007-2009.⁵ We excluded the foreign inflation variable, ${\pi }_{EA,t}^{*}$ from the euro model since, based on some preliminary tests, we could not maintain that ${\pi }_{EA,t}^{*}$ is weakly exogenous. Also, given the pivotal role played by the US in global financial markets, we excluded the foreign interest rates, $r_{US,t}^{*S}$ and $r_{US,t}^{*L}$, from the US model. The exclusion of these variables from the US model was also supported by preliminary test results showing that $r_{US,t}^{*S}$ and $r_{US,t}^{*L}$ cannot be assumed to be weakly exogenous when included in the US model. A similar result was found when the foreign inflation variable, ${\pi }_{US,t}^{*}$, was included in the US model. In short, the US model includes only two foreign variables, namely ${\text{x}}_{US,t}^{*}={(y_{US,t}^{*},e{p}_{US,t}^{*})}^{\prime }$ where $e{p}_{US,t}^{*}={\mathrm{\Sigma }}_{j=1}^N{w}_{USA,j}(e_{jt}-p_{jt}),w_{USA,j}$ is the share of US trade with country j, e_jt is the log of US dollar exchange rate with respect to the currency of country j, and p_jt is the log CPI price index of country j.

The country-specific VARX* models, (7), are combined with the oil price equation, (6), and solved for all the endogenous variables collected in the vector, ${\text{z}}_t={(p_t^o,{\text{x}}_{1t}^{\prime },{\text{x}}_{2t}^{\prime },\mathrm{\dots },{\text{x}}_{Nt}^{\prime })}^{\prime }={(p_t^o,{\text{x}}_t^{\prime })}^{\prime }$. We refer to this combined model as the GVAR-Oil model, which allows for a two-way linkage between the global economy and oil prices. Changes in the global economic conditions and oil supplies affect oil prices with a lag, with oil prices potentially influencing all country-specific variables. Similarly, changes in oil supplies, determined in country models for the major oil producers, are affected by oil prices and in turn affect oil prices with a lag as specified in the oil price equation, (6).

Although estimation is carried out on a country-by-country basis, the GVAR model is solved for oil prices and all country variables simultaneously, taking account of the fact that all variables are endogenous to the system as a whole. To solve for the endogenous variables, z_t, using (8) we first note that ${\text{x}}_{it}^{*}={\text{W}}_i{\text{x}}_t$, where W_i is a $k_i^{*}\times (k+1)$, matrix of fixed constants (which are either 0 or 1 or some pre-specified weights, w_ij), $k={\mathrm{\Sigma }}_{i=1}^N{k}_i,k_i^{*}=\text{dim}\left({\text{x}}_{it}^{*}\right)$. Stacking the country-specific models we now have

where

We also note that the oil price equation (6) can be written as

where w_ep, w_y and w_g are k × 1 vectors whose elements are either zero or are set equal to the weights w_i or $w_i^0$, assigned to ep_it, y_it or $q_{it}^0$, as implied by (5) and (2), respectively. Combining the above oil price equation with the country-specific models we obtain

which can be written more compactly as

Under the assumption that I_k − H₀ is invertible the GVAR-Oil model has the following reduced form solution

where ${\text{a}}_t={\text{G}}_0^{-1}{\text{b}}_t$ and $\text{F}={\text{G}}_0^{-1}{\text{G}}_1,{\xi }_1={\text{G}}_0^{-1}{\text{v}}_1$.

B. Effects of a fall in oil prices

We use the GVAR-Oil model to examine the direct and indirect effects of negative oil price shocks on the world economy, on a country-by-country basis, and provide the time profile of the effects on real outputs across countries, interest rates, inflation and real global equity prices. As explained earlier, the modelling approach is based on that in Mohaddes and Pesaran (2016), we therefore do not present the country-specific estimates and the associated diagnostic tests here, but refer the reader to Mohaddes and Pesaran (2016).⁶

Figure 2 displays the plots of generalized impulse responses for the effects of a negative short-term oil price shock on global real equity prices, long-term interest rates, as well as real output (based on PPP-GDP weighted responses of the 27 countries in our sample). It can be seen that negative oil price changes tend to increase real equity prices and reduce interest rates. The same pattern is also evident when considering the country-by-country impulse responses. In particular Figure 3 illustrates the fall in long-term interest rates across the major economies in the world following an oil price decline.⁷ We also find strong disinflation pressures in all major (net) oil importers, see Figure 4. These results are as expected, and are in line with those reported in the literature. See, for instance, Dees et al. (2007).

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Effects of Lower Oil Prices on Global Real Equity Prices, Long-Term Interest Rates, and Real GDP

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Figures show median impulse responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds. The horizon is quarterly.

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Effects of Lower Oil Prices on Long-Term Interest Rates in Various Countries

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Figures show median impulse responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds. The horizon is quarterly.

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Effects of Lower Oil Prices on Inflation in Various Countries

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Figures show median impulse responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds. The horizon is quarterly.

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While the responses of global equity prices, long-term interest rates and inflation show up relatively quickly and within a few quarters, the effects of oil price changes on real output, both at country levels and globally, take longer to manifest themselves. More specifically, the impulse responses for global GDP following an oil price fall is positive in the medium-term (Figure 2), which is also the case for the individual country responses in Figure 5. Thus the empirical evidence based on the GVAR-Oil model supports the view that an oil price fall is good news for the US, the other major economies, as well as for the global economy.

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Effects of Lower Oil Prices on Real GDP in Various Countries

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Figures show median impulse responses to a one-standard-deviation decrease in oil prices, with 95 percent bootstrapped confidence bounds. The horizon is quarterly.

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A. Has the relationship between real oil and equity prices been stable over time?

Figure 7 shows the monthly evolution of real oil prices, in 2015 US dollars per barrel, and US real equity prices, as measured by the S&P 500 index, from which it is clear that taking a relatively long historical perspective (1946-2016), there seems little evidence of a stable relationship between oil prices and real equity prices. Moreover, Table 2 illustrates that there are sub-periods where changes in real oil prices and real equity prices are unrelated, as well as sub-periods over which they are negatively and positively correlated. However, over the full sample the simple correlation coefficient is not significant.

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Real Oil Prices and Real US Equity Prices (S&P 500), 1946M1–2016M3

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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To conduct a more robust statistical analysis we use rolling regressions of the rate of change of real equity prices on the rate of change of real oil prices, estimated with 10-year windows, and then plot the coefficient of the rate of change of real oil prices (blue solid) and its two standard error bands (red dashed) in Figure 8. This figure shows that the coefficients were not statistically different from zero before 1990, became negative in 1991 and initially falling (being statistically significant from 1991 to 2001), and then eventually rising and becoming positive since the 2008 financial crisis (being statistically significant from 2012 onwards). It is then perhaps not surprising that there is no consensus in the literature on the relationship between oil and equity prices (Jones and Kaul 1996 and Wei 2003).

As Table 2 and Figure 7 show, a significantly positive relationship between oil and equity prices has emerged since the global financial crisis in 2008, which has been discussed extensively by the media as well as by prominent economists (see Bernanke‘s blog at Brookings on February 2016 and Obstfeld et al.’s IMF blog on March 2016) over the last few months. The question is why is this the case? There could be a number of reasons. Firstly, while markets are generally efficient and therefore equity prices reflect the fundamentals, there are also episodes when real equity prices do not reflect the state of the economy. In such periods any evidence of a perverse relationship between real equity and oil prices could be due to the disconnect between equity markets and economic fundamentals and not necessarily any breaks in the relationship between oil prices and the real economy. Secondly, Sovereign Wealth Funds (SWFs) accumulated large assets during the most recent oil boom (2002-2008) and they have come to play a major role in reserve management of oil revenues. The prominent examples are Norway’s Government Pension Fund ($830), Abu Dhabi Investment Authority ($773), Saudi Arabia’s Fund (SAMA) ($685), Kuwait Investment Authority ($592), and Qatar Investment Authority ($256), with the number in brackets referring to their market values in billions in June 2015. On average 65% of SWF assets are held in public and private equities (61% Norway; 72% SAMA; 65% Kuwait; 68% Qatar; 62% Abu Dhabi–figures based on 2014).⁸ During periods of rising oil prices, these funds are topped up with equity purchases. However, when oil prices are falling most major oil exporters withdraw money from the funds in order to maintain, for instance, their welfare expenditure. The equity transactions of SWFs in turn induce an unintended positive correlation between oil and equity prices. Whilst it is true that such effects might not be that large, they could trigger larger effects due to known market over-reactions.

Table 2.

Correlations between Changes in Real Oil Prices, Equity Prices and Dividends

Notes: A bold correlation highlights significance, with standard errors in parentheses. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

Table 2.

Correlations between Changes in Real Oil Prices, Equity Prices and Dividends

Period	Real Oil and Equity Prices	Real Oil Prices and Dividends
Full Period
1946M2-2016M3	0.008 (0.035)	−0.105 (0.034)
Sub-Periods
1960M1-1980M12	0.018 (0.063)	−0.071 (0.063)
1981M1-2000M12	−0.139 (0.064)	−0.163 (0.064)
2001M1-2016M3	0.199 (0.073)	−0.252 (0.072)
Sub-Sub-Periods
2001M1-2007M12	−0.144(0.109)	−0.088 (0.110)
2008M1-2016M3	0.404 (0.093)	−0.329 (0.096)

Notes: A bold correlation highlights significance, with standard errors in parentheses. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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Table 2.

Correlations between Changes in Real Oil Prices, Equity Prices and Dividends

Period	Real Oil and Equity Prices	Real Oil Prices and Dividends
Full Period
1946M2-2016M3	0.008 (0.035)	−0.105 (0.034)
Sub-Periods
1960M1-1980M12	0.018 (0.063)	−0.071 (0.063)
1981M1-2000M12	−0.139 (0.064)	−0.163 (0.064)
2001M1-2016M3	0.199 (0.073)	−0.252 (0.072)
Sub-Sub-Periods
2001M1-2007M12	−0.144(0.109)	−0.088 (0.110)
2008M1-2016M3	0.404 (0.093)	−0.329 (0.096)

Notes: A bold correlation highlights significance, with standard errors in parentheses. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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Rolling Estimates of the Effects of Changes in Oil Prices on Equity Prices

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Rolling estimates of the coefficient of the rate of change of real oil prices and its two standard error bands. Dependant variable is the rate of change of real US equity prices (S&P 500). The window size is 120 months. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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Overall, the empirical evidence suggests that the relationship between real oil and stock prices is not stable over time. As such, the recent perverse relationship between equity returns and oil price changes should not be taken as evidence that lower oil prices are bad for the real economy.

B. Are lower oil prices beneficial for the US and the world economy?

Ideally we need to consider how oil prices and real activity are related (as opposed to equity markets). However, quarterly GDP series that exist are not sufficiently long for a reliable analysis of output-oil price relationship over different sub-periods, particularly the post-2008 crisis period. Also, unfortunately, there are no reliable monthly observations on aggregate real activity. While a number of investigators have used monthly measures of US manufacturing output, this is not sufficiently representative of an economy such as that of the US.

Instead we use real dividends on S&P 500 as a proxy for economic activity. The rationale is that if the demand for companies’ products does not rise and they do not experience growth they cannot make profits, and if they do not have enough profits they could not pay dividends. While it is true that some companies strategically pay dividends even if their profitability is low, this can only be sustained in the short run (say one or two years). In the long run these companies need to be profitable in order to be able to continue paying out dividends. In other words, there has to be a relationship between real dividends and the economic climate in the long run.

Figure 9 shows the relationship between real oil prices and real dividends on the S&P 500 over the last 71 years, from which we observe that generally lower (higher) oil prices have been associated with higher (lower) dividends. Table 2 reports the simple correlation between changes in real oil prices and dividends, clearly showing a negative relationship between them over all sub-periods. More specifically the relationships are statistically significant for the full sample (1946 to 2016), as well as the two sub-samples, 1981–2000 and 2011–2016, but not for the sub-period 1960–1980. More importantly we find that changes in real oil prices are negatively related to changes in real dividends over the post-2008 crisis period, and this relationship is also highly statistically significant.

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Real Oil Prices and Real Dividends (S&P 500), 1946M1-2016M3

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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Using a relatively long monthly time series data on dividends and oil prices (1970–2016) we estimate rolling regressions (with 10-year windows) of the rate of change of real dividends on the rate of change of real oil prices, and plot the coefficient of the rate of change of real oil prices (blue solid) and its two standard error bands (red dashed) in Figure 10. As can be seen the rolling estimates of the coefficient of real oil price changes on dividends have been negative over the whole sample period, and statistically significantly negative for most of the period. Interestingly enough, the beneficial effects of lower oil prices on dividends have become even much stronger over the more recent episodes, with the rolling estimates becoming particularly large and statistically significant post 2009.

The rolling estimates give a clear indication of the changing nature of the relationships between oil prices, equity prices, and dividends, but do not allow for changing dynamics between these variables. Therefore, to check the robustness of the results to the dynamics of adjustments between oil price changes and the economy, we also estimated autoregressive distributed lag (ARDL) models, one with the rate of change of real equity and oil prices and another with the rate of change of real dividends and oil prices.⁹ Instead of rolling windows we estimated the ARDL models on the full sample period (1970M1 to 2016M4) and three sub-samples, namely 1970M1–1989M12, 1990M1–2007M12, and 2008M1–2016M4. We selected the lag order of the ARDL regressions with equity prices using the Akaike Information Criterion (AIC) with a maximum lag order set to 12. The estimates of the long-run coefficient of real oil prices are reported in panel (a) of Table 3, from which we can see that the coefficients are negative and statistically significant for the full sample and in two sub-samples (1970–1989 and 1990–2007), but the coefficient is positive and significant based on the 2008-2016 sub-sample. This provides further evidence for the unstable relationship between these two variables, and matches the results in Section A. and Figure 8. Turning to the ARDL regressions with real dividends, we see that in all cases the coefficient of the oil price variable is negative, being statistically significant in all sub-samples even in the post-2008 period, see panel (b) of Table 3.¹⁰ These results are in line with those using simple correlations in Table 2 and rolling estimates in Figure 10, and therefore suggest that lower oil prices are good for the US economy, even if we only consider the period after the Great Recession.

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Rolling Estimates of the Effects of Changes in Oil Prices on Real Dividends

Citation: IMF Working Papers 2016, 210; 10.5089/9781475552034.001.A001

Notes: Rolling estimates of the coefficient of the rate of change of real oil prices and its two standard error bands based. Dependant variable is the rate of change of real dividends (S&P 500). The window size is 120 months. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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For completeness, we also considered different measures of monthly economic activity, namely US industrial production and manufacturing indices, which are widely used in empirical work with monthly data. As before we estimated ARDL models over the full sample and the three sub-samples, now between the oil price variable and these two new measures of economic activity. The results for the ARDL models with industrial production are reported in panel (c) and for the ones with manufacturing production in panel (d) of Table 3. The coefficient of the oil price variable is negative in all sample periods and for both activity measures, but they are statistically significant only for the full sample and the first sub-sample, 1970M1–1989M12, thus supporting the results in panel (b) of Table 3.

To summarize, unlike the relationship between equity and oil prices, we find a stable negative relationship between oil prices, dividends and monthly real activity measures such as industrial production, which supports the results from the GVAR-Oil model (see Figure 5), and does not support the view that lower oil prices have not been good for the US economy since the 2008 financial crises.

Nevertheless, the fall in oil prices has hit the major oil exporters the hardest given that almost all of them substantially expanded their welfare programs during the period of unusually high oil prices that preceded the current price falls. For instance, post-2011, the GCC countries increased their social spending by around $150 billion. Saudi Arabia increased government employees pay and benefits by $93 billion and similar increases in welfare were put into effect by other GCC countries (Bahrain, Kuwait, Oman, Qatar, and the UAE); see, for instance, Abdel Ghafar (2016) and Devarajan (2016). In Iran, with declining oil revenue, the government initiated a subsidy reform, predicated on increasing fuel prices and using the revenue generated by the increase to finance a universal cash transfer; see Mohaddes and Pesaran (2014). It is not surprising therefore that the fall in oil prices has forced oil exporters to cut back on their welfare programs, withdraw from their oil funds, and attempt to diversify their economies.

At the world level, however, we would expect the increase in spending by oil importers to exceed the decline in expenditure by oil exporters (given their different marginal propensities to consume/invest), and so eventually lower oil prices should also be beneficial for the world economy. This was also clearly illustrated within the GVAR-Oil framework in Section B.; see, in particular, the responses of global and country level GDPs following a fall in oil prices in Figures 2 and 5. This in turn implies that demand for energy is going to start to rise, which will put upward pressure on oil prices in the medium term, and the equilibrating process starts to take place.

Table 3.

Estimates of the Long-run Coefficients of Real Oil Prices based on Various ARDL Regressions and Sub-samples, 1970M1–2016M4

Notes: Symbols ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. The lag order of the ARDL regressions with real equity prices, industrial and manufacturing production indices were selected using the Akaike Information Criterion with a maximum lag order set to 12. For the ARDL models with real dividends the lag order was selected using the Schwarz Bayesian Criterion; see also footnote 10. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

Table 3.

Estimates of the Long-run Coefficients of Real Oil Prices based on Various ARDL Regressions and Sub-samples, 1970M1–2016M4

	1970M1–2016M4	1970M1–1989M12	1990M1–2007M12	2008M1–2016M4
(a) ARDL Model with Real Equity Prices
Oil Price Coefficient	−0.159**	−0.176*	−0.185***	0.202*
	(0.073)	(0.100)	(0.039)	(0.118)
ARDL Order	(6,12)	(2,12)	(1,1)	(4, 4)
(b) ARDL Model with Real Dividends
Oil Price Coefficient	−0.016	−0.046***	−0.092**	−0.111**
	(0.017)	(0.014)	(0.043)	(0.048)
ARDL Order	(1, 3)	(2, 1)	(5, 0)	(1, 0)
(c) ARDL Model with Industrial Production
Oil Price Coefficient	−0.053**	−0.084***	−0.019	−0.098
	(0.025)	(0.029)	(0.014)	(0.075)
ARDL Order	(12, 11)	(2, 11)	(3, 3)	(12, 10)
(d) ARDL Model with Manufacturing Production
Oil Price Coefficient	−0.075***	−0.116***	−0.022	−0.067
	(0.027)	(0.036)	(0.017)	(0.063)
ARDL Order	(3,11)	(2,11)	(3,3)	(12,8)

Notes: Symbols ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. The lag order of the ARDL regressions with real equity prices, industrial and manufacturing production indices were selected using the Akaike Information Criterion with a maximum lag order set to 12. For the ARDL models with real dividends the lag order was selected using the Schwarz Bayesian Criterion; see also footnote 10. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).

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Table 3.

Estimates of the Long-run Coefficients of Real Oil Prices based on Various ARDL Regressions and Sub-samples, 1970M1–2016M4

	1970M1–2016M4	1970M1–1989M12	1990M1–2007M12	2008M1–2016M4
(a) ARDL Model with Real Equity Prices
Oil Price Coefficient	−0.159**	−0.176*	−0.185***	0.202*
	(0.073)	(0.100)	(0.039)	(0.118)
ARDL Order	(6,12)	(2,12)	(1,1)	(4, 4)
(b) ARDL Model with Real Dividends
Oil Price Coefficient	−0.016	−0.046***	−0.092**	−0.111**
	(0.017)	(0.014)	(0.043)	(0.048)
ARDL Order	(1, 3)	(2, 1)	(5, 0)	(1, 0)
(c) ARDL Model with Industrial Production
Oil Price Coefficient	−0.053**	−0.084***	−0.019	−0.098
	(0.025)	(0.029)	(0.014)	(0.075)
ARDL Order	(12, 11)	(2, 11)	(3, 3)	(12, 10)
(d) ARDL Model with Manufacturing Production
Oil Price Coefficient	−0.075***	−0.116***	−0.022	−0.067
	(0.027)	(0.036)	(0.017)	(0.063)
ARDL Order	(3,11)	(2,11)	(3,3)	(12,8)

Notes: Symbols ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. The lag order of the ARDL regressions with real equity prices, industrial and manufacturing production indices were selected using the Akaike Information Criterion with a maximum lag order set to 12. For the ARDL models with real dividends the lag order was selected using the Schwarz Bayesian Criterion; see also footnote 10. Data sources: Robert Shiller’s online database, Federal Reserve Economic Data (FRED), and United States Energy Information Administration (EIA).