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Dynamic Effects of Government Spending on Foreign Trade Master Thesis Presentation Professor Moisa Altar, PhD Student: Florea Adina Maria Bucharest July 13, 2008 TABLE OF CONTENT • Introduction • Literature review • Testing Strateg Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • About DSGE models • The model • Solving an RBC model • Conclusions • Bibliography Introduction Introduction The present paper studies the dynamic effects of a temporary increase in government spending on foreign trade. First, it seeks to establish empirically how the exchange rate, the terms of trade and the trade balance (net exports) respond to an exogenous increase in government spending. Second, it rationalizes these responses within a stochastic general equilibrium model which features price rigidities and thus allows for a potentially important role of monetary policy TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • About DSGE models • The model • Solving an RBC model • Conclusions • Bibliography Literature Review Literature Review Based on Vector Autoregressions (VAR), empirical investigations of the dynamic effects of fiscal policy in a closed economy context was numerous. Attempts have a been made to account for this evidence using different versions of stochastic general equilibrium models, e.g. Fata´s and Mihov (2001), Burnside et al. (2004) and Galı´ et al.(2005). Little evidence, has been put forward regarding the dynamic effects of government spending on foreign trade. Exceptions are Kim and Roubini (2003) and Giuliodori and Beetsma (2004), who do not, explore their empirical findings within a formal theoretical framework. Canzoneri et al. (2003) provide a VAR analysis of the effects of fiscal policy on foreign trade and, although they analyze their findings within a general equilibrium model, they make the restrictive assumption that trade is always balanced. TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • About DSGE models • The model • Solving an RBC model • Conclusions • Bibliography Testing Strategy Testing Strategy Preliminary Removig Measuring Testing Testing VaR Analysis seasonability Correlation CoIntegration Causality Model VaR Model Prelimi Removi Measu Testing Testin Va nary g ring g R Preliminary Analysis Analysi season Correl CoInteg ration Causa Mo s ability ation lity del By assessing the time series conclusions can be made, both individual and related to each other, about the characteristics of the time series. Future conclusions can also be compared to test the plausibility of the findings . When working with statistical data it is important to assess the data by doing a graphical analysis . By doing this we can find out the characteristics of the series. When working with time series it could be important if the series is stationary or non-stationary. A stationary time series is having a constant mean and a constant variance. A non-stationary time series have booth a varying mean and variance VaR Model Preliminary Analysis Mean Media Maximu Minimu St.Deviatio Skewnes Kurtosi n m m n s s Exhange 0.471655 0.4714 0.664696 0.351431 0.073939 0.652313 3.263714 rate 09 GDP 4.542360 4.5512 4.662212 4.386703 0.077326 -0.205011 2.021476 58 Governmen 6.723044 6.8163 7.023225 6.287115 0.229315 -0.472499 1.762508 t Spending 23 Private 8.866546 8.8547 9.127926 8.620724 0.165382 -0.012440 1.545872 Spending 66 Terms of -0.03017 - 0.023209 -0.07283 0.019904 -0.071568 3.305647 Trade 0.0295 6 Exports -2.26755 - -2.150777 -2.36398 0.065956 0.106906 1.610720 2.2781 5 Private 7.134391 7.1637 7.489188 6.688977 0.258951 -0.303227 1.603231 Investment 94 Private 8.670441 8.6514 9.923671 8.463391 0.147886 0.144053 1.646388 Consumptio 21 n Conclusions: private spending, government spending, terms of trade and exchange series has a leptokurtosis distribution as the Kurtosis >3 the other series has a platy kurtosis distribution( Kurtosis <3 ) Removing seasonability Prelimi Removi Measu Testing Testin Va nary g ring g R Removing seasonability Analysi season Correl CoInteg ration Causa Mo s ability ation lity del Time series that are based on monthly or quarterly data often follow a cyclical behavior, which repeats itself; this is called seasonality (Quantitative Micro Software, 2005). Granger say that such time series “have an observable component consisting of a fairly constant shape repeated every 12 months The underlying component causing this behavior can be extracted relieving the time series from these cyclical repetitions. There is a concern of autocorrelation that one time series are automatically adjusting to the other, between the two time series that would generate spurious results if no adjustment for seasonality would be done. When removing the seasonality in the data we are using a moving average additive method as we have also negative data.(Census X12) Measuring Correlation Prelimi Removi Measu Testing Testin Va nary g ring g R Measuring Correlation Analysi season Correl CoInteg ration Causa Mo s ability ation lity del Next step is to investigate the correlation. If there would to be no correlation there would be no causality. The correlation answers to if the strength and direction of two variables is similar. A value is received when doing a correlation test, which ranges from 1.0 to -1.0. A number more separated from 0 means that there is a strong positive or negative correlation e.g. 1.0 or -1.0. Testing CoIntegration Prelimi Removi Measu Testing Testin Va nary g ring g R Testing CoIntegration Analysi season Correl CoInteg ration Causa Mo s ability ation lity del According to Nelson and Plosser (1982) and Engle & Granger (1987) there exist unit roots in most macroeconomic time-series. Further they state that time series exhibit stochastic non-stationarity due to this unit root. Engle & Granger (1987) state that a linear combination of two or more non-stationary time series can be said to be stationary. If a stationary linear combination exists is the non-stationary time series co-integrated. “The stationary linear combination is called the co-integrating vector and may be interpreted as a long-run equilibrium relationship among the variables” The hypothesis behind is that random shocks in the economy have long lasting effects (Engle & Granger, 1987). This is important before doing a causality test, as it must be decided whether there exists a common unit root. “If variables follow a unit root process, it can lead to spurious results when the levels of the variables are used for estimation purposes because the variance of the process becomes infinite. In that case, least squares estimation with levels variables is clearly inappropriate”. (Jung and Seldon, 1995, pp. 580) First I estimate the auto correlation function (ACF) which generates a diagram Testing CoIntegration Testing CoIntegration As the geometrical shape of the bars in figures show a falloff to zero in AC and values near 1.000 in lag 0, and close to zero in the other lags in Partial Correlation (PAC)), it indicates non-stationarity. The diagram shows us that is valid to perform a pretest for integration using Augmented Dickey Fuller test. After doing the ADF test : exchange rate series is non stationary, exports is non stationary and terms of trade is non stationary Testing Causality Prelimi Removi Measu Testing Testin Va nary g ring g R Testing Causality Analysi season Correl CoInteg ration Causa Mo s ability ation lity del The Granger test was conducted on seasonal adjusted data, as the results from the augmented Dickey Fuller test and Co-integration test suggests.. From the test we can see all the probabilities are within the 5%(and 10% ) significant level, hence it can be rejected. These results indicate that there is a two-way causal relationship TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • About DSGE models • The model • Solving an RBC model • Conclusions • Bibliography VaR Model Prelimi Removi Measu Testing Testin VaR nary g ring g VaR Model Analysi season Correl CoInteg ration Causa Model s ability ation lity VAR includes six variables: the log of real government spending per capita, the log of real private spending per capita (private consumption plus investment), the log of the GDP deflator, the log of the nominal exchange rate, the log of the terms of trade . For the latter were considered net exports (scaled by GDP), the log of real private investment per capita the log of real private consumption per capita. The baseline specification includes four lags of each The VAR model includes 4 lags of each endogenous variable, a constant and a linear time trend. For the estimation, U.S. quarterly data period (1990:1 -2003:4) VaR Model VaR Model: In order to identify an exogenous shock to government spending, it is assumed that government spending does not respond contemporaneously to changes in the other variables included in the VAR. While the solid line gives the point estimates, the shaded area gives the 95% confidence Interval VAR includes six variables: the log of real government spending per capita, the log of real private spending per capita (private consumption plus investment), the log of the GDP deflator, the log of the nominal exchange rate, the log of the terms of trade . For the latter were considered net exports (scaled by GDP), the log of real private investment per capita the log of real private consumption per capita. The VAR model includes 4 lags of each endogenous variable. For the estimation, U.S. quarterly data period (1990:1 -2003:4) VaR model VaR Model – Impulse Functions: In order to trace out the responviness of the dependent variables in VaR To shock to the error term we have made the impulse functions: Government Spending: Government spending (panel a) rises significantly and persistently Private Spending: Private spending increases mildly, but not significantly and starts to decline after a few quarters VaR Model VaR Model: Terms of trade: The terms of trade appreciate sharply on impact Nominal Exchange The nominal exchange rate depreciates on Rate impact and this effect becomes stronger and significant after six- seven quarters VaR Model VaR Model: Net Exports: Net exports increase significantly on impact and remain above trend for an extended period. Investment Private investment is depressed VaR Model VaR Model: Consumption: Consumption increases gradually TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • The model • Solving an RBC model • Conclusions • Bibliography The model Theoretical Model: To rationalize the evidence obtained from the VAR a two-country general equilibrium model is proposed.(The origin of DSGE comes from Real Business Cycle model which was developed by Kydland and Prescott (1982), to capture the output fluctuations ) Hypothesis: 1. both countries supply distinct goods to the world market thereby giving an important role to the allocation of private spending 2. Fiscal policy is characterized by an exogenous process for government spending financed entirely through sum taxes 3. Private spending is biased towards domestically produced goods, while government spending falls entirely on domestic goods 4. Monetary policy is characterized by an interest rate feedback rule 5. Regarding the structure of international financial markets, is distinguished the case where financial markets are complete at the international level from a set up where only non-state-contingent bonds are traded across countries. Since the countries are symmetric, the exposition is focused on the home country using the following notation: - foreign variables within the home economy are indexed by the subscript ‘F’, while foreign variables in the foreign economy are indexed by a star. TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • The model • Solving an RBC model • Conclusions • Bibliography Solving an RBC Modell Solving an RBC Model: The model introduced here is a basic RBC model with monopolistic competition The model was solved with Matlab using Dynare; The model equations can be found in the document attached : Results : Solving an RBC Modell Solving an RBC Model – Impulse Functions: C- consumption l – labour R – interest rate(cost Of capital) K – capital stock I – investmenr W – wage Y – production Z - technology TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • The model • Solving an RBC model • Conclusions • Bibliography Solving an RBC Modell Conclusions This paper has tried to empirically establish the dynamic effects of an exogenous increase in government spending on the nominal exchange rate, the terms of trade and the trade balance. The main finding proves to be robust across various VAR specifications: the exchange rate depreciates, the terms of trade appreciate and the trade balance moves into surplus after an exogenous increase in government spending. The strong and significant response of the terms of trade provides a guideline for the theoretical exploration of the empirical findings. Specifically, It was investigated whether a two-country two-good general equilibrium model with price rigidities can account for the evidence, and if so under what conditions and as an example of solving a DSGE model with Dynare , the solution Of an RBC model was presented. TABLE OF CONTENT • Introduction • Literature review • Testing Strategy Preliminary Analysis Removing seasonability Measuring correlation Testing Co Integration Testing for Causality A VaR Model • Theoretical Model • The model • Solving an RBC model • Conclusions • Bibliography Solving an RBC Modell Conclusions Ahmed, S., 1986. 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