Panel data model

(8)

where are allowed to differ across firms and measure the individual excess return during the event period (CAAR).

One assumption of this approach is that the disturbances are independent and

identically distributed within each equation, and can vary across equations. The advantages of the approach are in the hypothesis testing since contemporaneous dependence of the

disturbances explicitly incorporated into the tests (Binder, 1985b).

Thus, there are a number of statistics available to test the joint hypothesis. Those are Wilks’ lambda (Wilks, 1932), Pillai's trace (Pillai, 1955), Lawley-Hotelling trace (Hotelling, 1951; Lawley, 1938), and Roy’s largest root (Roy, 1939). I note that each of statistics can be exactly distributed as F, approximately, or to show the upper bound of F. In our study all statistics are exactly F-distributed.

4.2.3 Panel data model

I examine the effect of news about RE policy on corporate performance. I use panel data model to obtain the average effect of the policy announcements on firms' performance

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during the whole period of study. I provide tests to decide whether the fixed or random effects model, or pooled OLS is appropriate. I use Driscoll-Kraay standard errors (Cameron &

Trivedi, 2010), which are assumed to be heteroskedastic, correlated between the groups, and is allowed to be serially correlated for m lags. I use Hausman test (Wooldridge, 2015) to determine whether I need to use fixed or random effects model. In this, I acknowledge that if I will use fixed effect model, the time invariant variables will be wiped out. Nevertheless, since I am mainly interested in the interaction effect (not main effect) this is not an issue. The results of all tests are listed in the A.5.

I denote EPO as a dummy variable for the policy announcement time period. The equation of interest is:

(9) where is the unobserved firm effect or firm specific effect; is the error term. I note that if the pooled OLS is the most appropriate (we will test for the random effects - see Appendix A.5), the composite error will be = , and the equation will be as:

(10) The hurdle is that STATA (the statistical program used) provides Driscoll-Kraay standard errors just with pooled or fixed effect models. Nevertheless, if the tests show that the random effects model is the most appropriate, I still can use the pooled OLS, because under the random effects assumptions it will still provide consistent estimates (Cameron & Trivedi, 2010; Wooldridge, 2015). In this, if the random effects model is the most appropriate, I will show the results of both models, i.e. random effects model and pooled OLS. For brevity in the further discussion of the models, I will provide just the models with unobserved firm specific effect , nevertheless, the discussion of this paragraph applies to the all further model.

Second, I will estimate the effect of RE policy on the US RE firms given different firm-specific characteristics such as RE technology and firm size (measured by market capitalization). As I have mentioned in the previous chapter firms that produce biofuels are more diversified than firms within other RE technology. Such diversification could make biofuel firms less responsible to the policy announcement. In addition, the Chinese RE firms are within solar technology, therefore, it is of interest to measure whether solar firms

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experience different effect of policy announcement than firms with other technologies (i.e.

wind, geothermal, wave). Thus, I define two groups, one for solar and one for biofuel firms.

In addition, firms with different market capitalization could experience different effect of the policy announcements. In particular, small firms could be more sensitive to the policy announcements than big. Kothari and Warner (2004) in their study noted that individual firms' security variances and their abnormal return variances exhibit an inverse relationship to the firm size and can vary systematically by industry. "Small-firm effect" is when small firms appear to have higher average returns then large firms (Bodie et al., 2011). Originally, the small-firm effect was documented by Banz (Basu, 1983, 1997), who stated that small firms have a higher risk-adjusted return than large firms. Thus, the higher average returns of small firms could be justified by the additional risks borne in an efficient market (Chan, 1985).

Thus, I define three groups for market capitalization, i.e. small, medium, and big.

I define firms with a big market capitalization as firms which have market

capitalization between $10 BN to $200 BN; mid cap - ranging from $2 BN to $10 BN, this group of companies is considered to be more volatile than the big-cap; small cap - have a market capitalization less than $2 BN.¹⁵

Thus, the equations of interest are:

a). Interaction with technologies:

(11)

where the base group are firms within wind, wave, and geothermal technologies;

and measures the differences of policy effect between solar firms and base group and biofuel firms and base group respectively; solar - equals unity for firms within solar

technology, biofuels - equals unity for firms that produce biofuels.

b). Interaction with market capitalization:

(12)

where small firms are the base group; M - defines firms with different market capitalization (small, medium, big), e.g. equals one for firms with mid market capitalization and zero otherwise.

15 http://www.investopedia.com/terms/m/marketcapitalization.asp. Extracted: 25.04.2017.

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As the last step of my study, I want to estimate whether policy affect differently the performance of Chinese and US firms.

c). RE firms China vs. US:

(13) where US equals to one for the US firms and zero otherwise; measures the

difference in policy effect between China and the US.

As a robustness check for the market model I specify Corrado's rank test. In addition, to check the robustness of the results of the effect of news about RE policy on the firms' performance, I use several different tests: (1) I use statistics provided together with

multivariate analysis-of-variance (MANOVA), and (2) panel data model with Driscoll-Kraay standard errors.

5 Empirical results

In this chapter, I present the results of the effect of political events and policy

announcements on energy firms' performance. First, I look at the effect of political events and discuss first three hypotheses. Then, I estimate the effect of policy announcements in both China and the US and discuss hypotheses 4-6. Next, I use a panel data model to test whether the effect of policy news on RE firms differs across two countries. Finally, I present the results of robustness check.