Oil and the Macroeconomy of Kazakhstan Prepared for the 30 th USAEE North American Conference, Washington DC By Ferhat Bilgin, Ph.D. Student and Fred Joutz Department of Economics George Washington University

Objectives Analyze the role of oil in Kazakhstan’s economy. Specifically, study the effects of oil price on the macroeconomic dynamics of Kazakhstan Attempt to find long-run relationship and short-run dynamics The hypothesis is that capital stock and oil price are the major determinants of GDP growth; and growth of GDP and oil price are the main sources of government revenue growth.

Summary of Results We found both long run and short run GDP and Revenue growth model The long-run relationship yields – Rise of the oil price and accumulation of capital stock raise the level of GDP. – Higher level of government revenue is determined by higher level of GDP and world oil price In the short run – GDP growth rate is driven by changes in contemporaneous and lagged oil price, but government taxation has a negative effect on GDP growth – Oil price has a direct positive effect on government revenue in addition to the effects through error correction mechanism

Organization Brief Economic History of Kazakhstan Literature Review General Model Data Empirical Model for the DGP – Unrestricted VAR Cointegration Testing and Analysis Equilibrium Correction Model

Kazakhstan: Gross Domestic Product Basic Oil Exporting Country Macroeconomic Model: Linkage Economic Growth, Fiscal Revenues, Capital Stock, and Oil Sector

Government Revenue, real and nominal Unit: USD

Oil Price, real and nominal In USD

Kazakhstan’ oil output, oil exports Unit: in million barrels a day

Oil export’s share in total exports Oil output’s share in total output Unit: (%)

Inflation (1992 – 2010) Inflation (%) Inflation (%)

Inflation ( ) Unit: (%)

Employment (in millions)

Literature Review Gronwald, Mayr and Orazbayev (2009): studied the effects of oil price decline on Kazakhstan’s real GDP, budget revenue, exports and real exchange rate => oil price decline has negative effect on these variables Rautava (2004): studied the effects of oil price on Russian GDP and government revenue using CVAR and VeqEM approach. 10% permanent increase in oil price => 2.2 % rise in GDP, 4.6% in Rev. 10% increase in oil price => 0.5% rise in GDP, 3.9% in Gov’t Rev. Ito (2008): studied the effects of oil price on Russian GDP and inflation using CVAR and VEC approach and found that oil price has a positive effect on GDP and inflation Venezeula: El-Anshasy, Bradley, Joutz (2007): used CVAR and VeqEM modeling approach to study the long run and short run relationship between oil price and Venezuela's GDP and government revenue. 10% rise in oil price => 2.5% and 2.3% rise in the level of GDP and Rev, and adds 1.7% and 2.8% directly to the short run growth rate

General Macro Model Basic Oil Exporting Country Macroeconomic Model: Y = K α *L β *(Poil) γ Linkage Economic Growth, Fiscal Revenues, Capital Stock, Labor and Oil Sector

Data (sample: 1996q1-2010q3) All in a log form

Data Analysis Sample period is 1996Q1 through 2010Q3 Capital stock represents the capital acquired after 1995q1 and used as a proxy for the entire capital stock in the economy Augmented Dickey Fuller Test  The levels are nonstationary  The first differences are stationary  all of the five series are I(1) series  We can apply CVAR and VEC modelling approach

VAR in Levels and Cointegration Analysis Lag Structure Analysis  VAR(5) -> VAR(4)  VAR (4) is the most appropriate Model Stability  1-step Chow test  Break point Chow test => model is stable Johansen Test => two cointegrating vector

Long Run Equilibrium Relations GDP and Government Revenue GDP relations gdp = 0.35*capital stock *oil price Government revenue relations revenue = 0.56*gdp *oil price Capital stock is weakly exogenous => capital stock does not enter VEC model as endogenous variable

Table 3: Final Vector Error Correction Model Sample Period 1996q1 – 2010q3 DLrgdp: Change in real quarterly GDP DLrgrev: change in real quarterly Government Revenue Equation for DLrgdpCoefficientt-value Equation for DLrgrevCoefficient t-value Constant Constant DLrgrev_ DLroilprice DLrgrev_ ECMg_ DLrgrev_ I:1998(4) DLroilprice I:1999(2) DLroilprice_ I:2005(4) ECMy_ I:2008(4) ECMg_ I:1998(4) I:2009(2) sigma =0.0341sigma = RSS = RSS = R^2 = F(30, 70) =9.7785[0.0000] Vector AR 1-4 test:F(16,48)= [0.3927] Vector Normality test:Chi^2(4)=3.6889[0.4497] Vector Hetero test:F(51, 72)=0.8709[0.6967] Vector RESET2 test:F(8, 56)=1.5634[0.1569]

Interpreting the Final Error Correction Model Feedback from Long-Run Relations Inclusion and estimation of the CVAR provide important insights to long- run economic performance And provides information in the specification and understanding of the short-run dynamics. Fiscal ECM enters the change equations for – GDP (0.32) higher revenue encourages higher spending – Government Revenue (-0.75) sluggish adjustment to deviation from “steady-state” Economic Growth ECM enters the change equations for – GDP (-0.44) slow adjustment to deviations from “steady state”

Interpreting the Final Error Correction Model Short-Run Dynamics (Growth or Changes) Contemporaneous and lagged oil price changes have a direct positive effect on GDP growth rate Changes in contemporaneous and lagged values of government revenue have a negative effect on GDP. Oil prices have a positive direct dynamic effect on government revenues.

Conclusions Comments and suggestions welcome Thank you!

23 Autometrics – General to specific modeling algorithm Hendry and Krollzig (2001) There are five basic steps: 1.Specification of the GUM (General Unrestricted Model) by the empirical modeler. 2.Tests for mis-specification usually through residual diagnostics. 3.Begin Model Reduction Process. Investigation of possible paths for variable selection. Elimination of “irrelevant” variables. 4.Test terminal models or paths for congruency. 5.Evaluate terminal models for final model(s) through encompassing tests. End Result: Retain only Variables in GUM that Matter!!

Reduction from the General Cointegrated VAR Process of Reducing the Statistical Model Retain Characteristics of the Original Relationships in Data or DGP The pattern of short run dynamics is identified by sequentially eliminating insignificant regressors and then estimating the resulting model with FIML. Hypothesis Tests used for Model Evaluation and Model Design Parsimony Maintain White Noise Property of Residuals Stability Interpretability Final Model is Congruent