bayesian econometrics

It is commonly known that the exchange rate is one of the most important macroeconomic variables whose changes strongly influence a country's balance of payments and international competitiveness. Exchange rate fluctuations affect the production costs of companies whose products have an import component. The manufacturing sector is also affected by the foreign exchange market and its fluctuations, as it interacts with the global economy through the export of products and the import of means of production. Economic policy makers should know how changes in the nominal exchange rate will affect prices at the sectoral level. This paper aims to examine the impact of the exchange rate on manufacturing prices both at the general level and at the sub-sector level.

The methodology used in this study is based on Bayesian vector auto-regression models. The data are seasonal and cover the period from spring 1996 to summer 2023. In this study, the BVAR model was used for 22 manufacturing subsectors at the two-digit ISIC code level and for the entire manufacturing sector, resulting in a total of 23 models. Before estimations, the data were tested to determine whether they had a unit root or not. In addition, before estimating the VAR model, the appropriate number of axis intercepts should be determined for the model. The likelihood ratio test was used for this purpose. First, the LR test was used to sequentially test the null hypothesis that the interval between the equations was not significant and the counter-hypothesis that at least one of the coefficients was non-zero. After selecting the variables and estimating the models, the pass-through of the exchange rates to the different prices was examined by analyzing the shock-response functions. Given the different effects of the exchange rate pass-through on the chain of price indices, the question arises as how the increase in the exchange rate affects the individual price indices as a whole and what percentage share this increase has in the individual price indices. They reflect the exchange rate pass-through. To answer this question, a method introduced by Farooqi (2006) was used. After estimating the BVAR model, the shock response functions were defined cumulatively. Then, the size of the cumulative effect caused by the exchange rate change on each price index was compared with the size of the cumulative effect of the currency shock on the exchange rate.

The results show that the price indices for imported and exported goods at the level of total manufacturing do not react in the same way as an exchange rate shock. The price indices of imported goods (76.5 percent) and exported goods (32.3 percent) show the highest increase in the first year compared to the other price indices. The effect of the exchange rate shock on the price index of manufactured goods in the first months is very similar to the effect of the price of imported goods. It reaches its peak in the first year, but, after that, the effect of the exchange rate on the prices of manufactured goods gradually disappears by the end of the second year. This suggests that the impact of the exchange rate on domestic manufacturing prices is incomplete. The results also show that the "manufacture of coke, refined petroleum products and nuclear fuel", "manufacture of medical, precision and optical instruments and watches", and "manufacture of radio, television and communication equipment" industries react most strongly to the shock. By contrast, the sectors showing the least reaction to the exchange rate shock are "manufacture of textiles", "tanning and dressing of leather, luggage, handbags, saddlery, harness and footwear", "manufacture of wearing apparel, dressing and dyeing of fur", and "manufacture of tobacco products”.

Conclusions and policy implications: 

According to the results of the study, it is recommended that the policy of stabilizing the foreign exchange market be continued. This is because the increase in the exchange rate leads to an increase in production costs and affects various industries in the country. Finally, it increases the price of products. Therefore, the continued stabilization of the foreign exchange market will reduce the inflation rate.

In recent decades, goods smuggling has become a significant negative phenomenon in various countries, particularly developing ones. Given its unique geographical circumstances, Iran has consistently witnessed smuggling growth alongside legal trade. The prerequisite for making appropriate decisions to combat smuggling is understanding the factors influencing goods smuggling. This study aims to examine the impact of potential factors affecting goods smuggling in Iran during the period 1996-2021.

This quantitative, correlational study employs a Bayesian Model Averaging (BMA) approach to overcome model uncertainty in data analysis and investigate all factors potentially influencing goods smuggling. The methodology allows for comprehensive analysis of multiple variables while accounting for statistical uncertainty.

The research identified nine variables with over 50% probability of influence among twenty-two potential factors. Positive relationships with smuggling were found for unemployment rate, inflation, economic inequality, official/parallel exchange rate differential, taxes, government size, poverty, tariffs, marginal propensity to consume, subsidies, trade prohibitions, tourism, transportation, mortality, and communications. Inverse relationships were observed for income, economic growth, trade openness, religion, industry, education index, and exchange rate regime.

The study suggests policymakers should focus on supporting domestic health product manufacturing, reducing current expenditures, backing leading industries, narrowing the official/parallel exchange rate gap, decreasing international trade restrictions, and raising public awareness about proper use of communication technologies. These measures would contribute significantly to reducing goods smuggling in Iran.

Today, smuggling of goods is one of the basic economic problems in all countries of the world, especially in developing countries. In the meantime, due to its special geographical location and due to the fact that it has always been the bridge of trade between different countries, Iran has always seen an increasing growth of smuggling. Based on this, many policymakers are always looking for solutions to deal with smuggling. On the other hand, income is one of the main causes of development, and its low level can lead to many problems, such as increasing crime, deterioration of health, and so on. One of the economic crimes that low income can increase is smuggling. Because if people cannot earn enough income through legal ways, they are more likely to turn to illegal ways such as smuggling. Therefore, the aim of this study was to investigate the effect of income, among other factors, on smuggling of goods in Iran during the period of 1375-1401 by using Bayesian Model Averaging (BMA) and Ordinary Least Squares (OLS) methods. The results indicate that income has a negative and significant relationship with smuggling. In addition, trade openness and added value of industrial sector also have a negative and significant relationship with smuggling. In contrast, the variables of difference between official and unofficial exchange rates, taxes, government size, tourism, mortality and communication have a positive and significant relationship with goods smuggling in Iran.