Freeze-Lapse-Reverse La velocidad como exploración retrospectiva

This section will present the model equations that will be estimated in the next chapter as a handy reference in order to facilitate the comparison between the model blocks and equations in order to clarify the interrelationships in the model as a whole.

LCPR = α1, 1 * LCPR _1+ α1, 2* LYPDR + α1, 3* LMSR - α1, 4* LMSR _1+ α15*

TREND --- (4.1)

CGC = α2, 1 + α2, 2* TAXDIRC + α2, 3* TAXDIRC (-1) + α2, 4* TAXINDC+α2,5*

OILREV --- (4.2)

IPNOILR = α3, 1* IPNOILR _1+ α3, 2* DKNOILR + α3, 3* IGNOILR _1–

α3, 4* DUM8185 --- (4.3)

KNOILR = KNOILR _1+ IPNOILR + IGNOILR - DEPNOILR --- (4.4)

LIMCONR = α4, 1* LIMCONR _1+ α4, 2*LCPR - α4, 3*LEXCHRATE _1–

α4, 4* PPRICE + α4, 5* PPRICE _1--- (4.5)

LIMCAPR = α5, 1* LIMCAPR _1+ α5, 2* LIGNOILR + α5, 3* LDKNOILR –

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EXOILMB = α6, 1* EXOILMB _1+ α6, 2* XOILMB – α6, 3*

XOILMB _1--- (4.7)

WAGEFBC = α7, 1* WAGEFBC _1- α α7, 2+ α7, 3* FLABOR –

α7, 4* FLABOR _1--- (4.8)

EXOILOC = EXOILMB * PEXOILLD --- (4.9)

NFIFBC = α8, 1 * NFIFBC _1+ α8, 2* FINACC --- (4.10)

EXTOTC = EXOILOC + EXOTHC --- (4.11) IMTOTC = IMCONR * PIMC + IMCAPR * PIM + IMOTHC --- (4.12) NETYFBC = WAGEFBC + NFIFBC --- (4.13) BOTGSC = EXTOTC - IMTOTC + BOTGSDISCREP --- (4.14) BOPC = BOTGSC - NETYFBC + FINACC + BOPOTHC --- (4.15) IRC = IRC _1+ BOPC --- (4.16)

XNOILR = α9,1 + α9,2* KNOILR - α9,3* KNOILR(-1) + α9,4* IMCAPR + α9,5*

IMCAPR_1+ α9,6* CRNOILR --- (4.17)

GDPR = XNOILR + XOILR + GDPRDISCREP --- (4.18) PGDP = - α10, 1 + α10, 2* PGDP _1+ α10, 3* EXCHRATE + α10, 4* PIM +

α10, 5 * DMS --- (4.19)

INFGDP = (DPGDP / PGDP_1) * 100 --- (4.20)

PGDPNOIL = α11, 1 * PGDPNOIL_1+ α11, 2 * TREND + α11, 3* PGDP

+ α11, 4 * CUGDP --- (4.21)

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PWOILLD = PWOILUS$ * EXCHRATE --- (4.23)

PEXOILLD = α12, 1 * PEXOILLD (-1) + α12, 2 * PWOILLD –

α12, 3* PWOILLD (-1) + α12, 4 * PWOILLD (-2) --- (4.24)

PCP = α13, 1 +α13, 2 * PCP _1+ α13, 3* PGDP - α13, 4 * PGDP _1+

α13, 5* PGDP_2--- (4.25)

PIT = α14, 1 + α14, 2 * PIT _1+ α14, 3 * PGDP - α14, 4 * PGDP _1+

α14, 5 * PGDP _2--- (4.26)

GDPCfc = (XNOILR * PGDPNOIL) + (XOILR * PGDPOIL) --- (4.27)

TAXDIRC = α15, 1 * TAXDIRC _1+ α15, 2 * GDPCFC _1+

α15, 3* TREND --- (4.28)

TAXINDC = TARIFFC + TAXNTARC --- (4.29) GTYC = TAXDIRC + TAXINDC + OILREV + TAXOTHSC --- (4.30) YPDC = GDPmp + SUBSIDIES + NETYFBC – GTYC --- (4.31) YPDR = YPDC / PGDP --- (4.32) GTEC = CGC + IGNOILC + OTHGEC --- (4.33) BUDGET = GTYC – GTEC --- (4.34) NDAC = CRNOILC + OTHDASC --- (4.35) GDPmp = GDPCfc + TAXINDC - SUBSIDIES + GDMPDISCREP --- (4.36) GDPRG = (DGDPR / GDPR _1) * 100 --- (4.37)

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4.4 Conclusion

This chapter has described the theoretical background of the model by reviewing all the theories that related to the equations of the model in order to justifying the variable in each equation, and shed light on the debate about these theories. It is known that the Libyan economy is a small open one, reflecting most of the characteristics of developing economies and it depends on the production and export of one commodity as the main source of income. Additionally, it imports much of its needs from other economies. The private sector in Libya is unable to undertake the role that has been assigned to it.

These characteristics are the dominant feature of the Libyan economy, accordingly, this model has been constructed to reflect, clearly, these characteristics, with an attempt to explain the variables contained in the model equations.

Accordingly, this chapter has focused on the important variables in aggregate demand and supply, and has highlighted the role of the government and external sectors, in a developing economy such as the Libyan economy.

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CHAPTER FIVE: ESTIMATION OF THE MODEL

5.1 Introduction

Using econometric models in economic research is one of the most important approaches that have helped to advance economics during the past three decades because of the importance of practical applications in strengthening the theoretical aspects, and then consolidating the beliefs around them (Frisch, 1971).

In fact, economic studies that use econometric models seek to achieve all or some of the goals that such models aspire to attain. Structural analysis of the study model is considered the first of these goals (Intriligator, Bodkin, and Hsiao, 1996, (see in particular chapter14)). This goal will be achieved in this chapter by estimating the single equations of the model in order to clarify its underpinning theory (through the magnitude and the sign of the different parameters in the model equations) as an economic evaluation, in addition to emphasize goodness of fit for these equations, as a statistical evaluation. The other two goals that are sought to be achieved in econometric models are forecasting and the evaluation of economic policies (Intriligator, Bodkin, and Hsiao, 1996, (specifically in chapters, 15 and 16, respectively)). This subject will be addressed in the next chapter where it will utilize a simulation technique to achieve these two goals simultaneously by using the results of the simulation process to apply forecasting inside the sample period. Additionally, the aim was to accomplish a policy analysis by evaluating different available scenarios using the control solution of the model, which was obtained from the aforementioned simulation process.

174 The Dutch economist Jan Tinbergen has summarized econometric models research in three steps: firstly, one must prepare a list of variables that must be taken into account. Secondly, there is the preparation of a scheme of equations or the relationship between the variables involved in the study, and finally the validity of these equations should be tested by estimating their parameters (Tinbergen, 1971).

The estimation of the parameters in the single equations, and testing their goodness of fit, is the basis upon which to build the entire econometric model and thus its ability to predict; this then can be used in the model analysis of different policies. This is despite the fact that the goodness of fit in the single equations’ estimation is not sufficient in some cases to guarantee the goodness of its performance in the model as a whole, as will be seen later in the next chapter (Pindyck and Rubinfeld, 1998; cf. Pagan, 1999).

The two previous chapters (chapters three and four) have introduced the required theoretical framework of the model of the study and paved the way to this chapter that will deal, as mentioned above, with the estimation of the single equations of the model.

To achieve the mentioned task, this chapter will be divided into four sections in addition to the introduction and the conclusion, which will be shown in sections one and six respectively. The second section will review estimation techniques with a focus on a methodology used by the London School of Economics (LSE) which is considered the philosophical foundation of the estimation method that will be used for the single equations in this study. The estimation process of the single equations (that forms the six blocks in this model) will be addressed in section three, while section four will present

175 the estimated model in its final form. Finally, section five will provide a list of all the variables in the model of the study.