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the market-determined parallel exchange rates to classify the exchange-rate regime. In their classification, they separate the exchange rate regime into five categories: 1 if a country maintainsed fixed regimes, 2 is the crawling pegs, 3 is the managed floatinging, 4 is the freely floating, and 5 is freely falling. In order to compare with the results of the IMF’s classifications, following Alfaro (2004), we define the dummy variable as 0 if the country maintained a floating regime, 3,4,5, in Reinhart and Rogoff’s (2004) classification; 1 if the country maintained a intermediate regime, 2, in Reinhart and Rogoff’s (2004) classi-fication; and 2 if the country maintained a fixed regime, 1, in Reinhart and Rogoff’s (2004) classification.
4.2 Cross-Sectional Analysis
4.2.1 The Openness and Inflation
In most of the literature studies, researchers usually use the OLS method for their empirical work between openness and inflation. But the shortcomings of OLS estimators is that it just describes the “mean” effects of openness on inflation, it cannot analyze the effects of openness on inflation across quantiles.
We apply the QR method to analyze the cross-sectional data in 1973-2008 and show the different conditional quantile effects.
Given θ ∈ (0, 1), and the model is defined as below:
log πi = Xi0β(θ) + ei,θ,
where πi is the inflation rate, and Xi is the independent variable including the trade openness, GDP per capita, and the constant term. Furthermore, we use the bootstrap method to estimate the stand error of QR estimators and we repeat this process 1000 times.
In this section, trade openness is measured as the share of imports in GDP.
Following Romer (1993) and Lane (1997), we take the log of inflation to reduce the effects influenced by the outlier inflation countries.
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Table 6 shows the 0.1-0.9 QR estimators and the OLS estimator. In order to compare the difference between OLS and QR, we draw the OLS and QR estimators in the same figure. Figure 3 shows the cross-sectional analysis and uses estimator values as vertical axis and quantile as horizontal axis. Where the horizontal solid line is the OLS estimator, the solid curve stands for the QR estimators, and the two dotted curves represent the 95% confidence interval of QR estimators.
In Table 6 and Figure 3, our empirical results show that the OLS coefficients of trade openness are negative and significant which is the same as Romer (1993). The QR estimators show that the effects of trade openness on inflation are all negative. Even more so, the QR estimators show that the effect of trade openness on inflation is stronger when the quantile is higher, and the QR estimators are only significant in quantile 0.3, 0.7, 0.8, and 0.9 when the measure of trade openness is a share of imports in GDP. In Table 6, the OLS coefficient of trade openness is −3.22 × 10−3, and the 0.7-0.9 quantile coefficients are −3.19 × 10−3, −4.34 × 10−3, and −6.40 × 10−3 respectively.
The effects of trade openness on inflation at quantile 0.8-0.9 are stronger than the effect of OLS. The negative and significant effects of trade openness on inflation only exist when inflation is high. When a country has higher inflation, the greater openness makes inflation drop significantly.
The cross-sectional empirical results support the arguments of Romer (1993).
When there is an absence of a credible commitment, the monetary authori-ties have a motive to pursue expansionary monetary policy. It deserves to be mentioned that the cross-sectional analysis describes the long run effects, and it may have no thought for the short-run characteristics. So we use the panel data to test whether or not a negative relationship exists between trade openness and inflation in the short-run.
In addition, we consider the potential endogeneity of openness of the
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sectional empirical work. Following Romer (1993), we use population as an instrument variable to deal with the potential endogeneity of trade openness and apply IVQR to the empirical work between openness and infaltion.
In Table 6, our empirical results show that the IVQR estimates are all negative but only significant at the 0.7-0.8 quantiles after using the instrument to deal with the endogeneity of trade openness. It shows that the negative effect of openness is stronger when the quantile is higher when applying the QR and IVQR methods, see Figure 3. But differ from the OLS, the 2SLS estimate is negative but insignificant.
4.2.2 The Openness and Sacrifice Ratio Given θ ∈ (0, 1), and the model as below:
SCi = Xi0β(θ) + εi,θ,
where SCi is the sacrifice ratio, and Xi is the independent variable including trade openness, mean inflation, and the variability of aggregate demand. As the cross-sectional analysis between trade openness and inflation, we use pop-ulation as an instrument to check the empirical work and use the bootstrap method to estimate the stand error of QR estimates which repeat 1000 times.
Romer (1993) argues that trade openness causes inflation to fall by reducing the output-inflation tradeoff, and the past literature studies put many doubts on the negative relationship between trade openness and output-inflation trade-off (or sacrifice ratio). The research of Ball (1994) and Temple (2002) show that trade openness has a negative but insignificant effect on the sacrifice ra-tio, and their empirical results cause Ball (1994) to doubt the arguments of Romer (1993). Temple (2002) also argues that the relationship between trade openness and inflation is something of a puzzle, and he argues that it becomes a little harder to explain the negative relationship between trade openness and inflation in terms of time consistency models. Differing from Ball (1994) and
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Temple (2002), Danels, Nourzad, and VanHoose (2005), Danels and VanHoose (2008), and Badinger (2009) have the opinion that the effect of trade openness on sacrifice ratio is positive.
As mentioned above, the researchers used a different data set to apply to their empirical work, and they showed a variety of empirical results. The literature has a common point, and they all reveal the mean effect of trade openness on the sacrifice ratio. To go a step further in researching this topic, we made a breakthrough which applied the QR method to describe the differ-ent conditional quantiles. Table 7 shows that the QR estimates are all positive, which is the same as Danels, Nourzad, and VanHoose (2005), Danels and Van-Hoose (2008), and Badinger (2009). The positive effect of trade openness is stronger from the 0.1 quantile to the 0.6 quantile, but the positive effect of trade openness is weaker from the 0.7 quantile to the 0.9 quantile and the quantile 0.9 estimate is insignificant. In addition, we used population as an instrument variable to deal with the endogeneity of openness. Figure 4 shows that the positive IVQR estimates are higher when the quantile is higher, and the 0.9 quantile estimate of IVQR is 9.18 × 10−3 which is larger than the 0.9 quantile estimate of QR, 2.01 × 10−3. According to the empirical results, it shows that they are different from the findings from Romer (1993). We doubt the mechanism in which trade openness is influenced by inflation in reducing the output-inflation tradeoff. Our empirical results show the positive effect of trade openness on the sacrifice ratio which is the same as Danels, Nourzad, and VanHoose (2005), Danels and VanHoose (2009), and Badinger (2009), and this supports the model provided by Danels and VanHoose (2006). The model of Danels and VanHoose (2006) shows that greater trade openness reduces the pricing power of domestic firms in an economy characterized by monopolistic competition, and this lowers the output effects of unexpected price increases through a monetary expansion. As a consequence, even though greater
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ness increases the sacrifice ratio in the model of Danels and VanHoose (2006), the trade openness also has a negative effect on the inflation rate arising from discretionary monetary policy.