key: cord-0877394-qwtwbja0 authors: Ongan, Serdar; Gocer, Ismet title: Japan-US bilateral commodity-level trade and trade policy-related uncertainty under the COVID-19 pandemic: the nonlinear ARDL model date: 2021-09-13 journal: Econ Change Restruct DOI: 10.1007/s10644-021-09351-7 sha: 5ac198561b8d45e1aa8eaf3cb11593d0be6ed01e doc_id: 877394 cord_uid: qwtwbja0 This study examines whether trade policy-related uncertainties, scaled by TPU index, and the COVID-19 pandemic affect Japan’s bilateral commodity trade balance with the U.S concerning 60 industries. To this end, the nonlinear ARDL (autoregressive distributed lag) model is applied. Empirical findings of the disaggregated data model indicate that while changes in Japan’s TPU index have significant, either improving or worsening, impacts on this balance concerning 23 industries, changes in US TPU index have impact on only 32 industries. Additionally, based on the behaviors of Japanese and US consumers through their import demands, it can be interpreted that neither of them is uncertainty-sensitive to each other’s products and they continue to buy these products even if both countries’ TPU indexes keep rising. Another expected empirical finding is that the COVID-19 pandemic worsens the trade balances of Japan for different industries shown in the tables. Economic Change and Restructuring The EPU index was widely used in many empirical studies to investigate the impacts of economic policy-related uncertainties on different micro-macroeconomic levels of variables, such as foreign direct investments (Hsieh et al. 2019; Canh et al. 2020) , exchange rates (Beckmann and Czudaj 2017; Liming et al. 2020) , Bitcoin prices (Demir et al. 2018; Wang et al. 2020) , interest rates (Ashraf and Shen 2019) , GDP (Ghirelli et al. 2019; Huang and Luk 2020) , stock returns (Li et al. 2015; Yin et al. 2017; Chen and Chiang 2020) and demand for money (Ivanovski and Churchill 2019; Bahmani-Oskoosee and Nayeri 2020) . However, the impacts of the EPU index on global-national level trade flows were not examined as much as the macroeconomic variables presented above. A few scholars have tested these impacts on trade flows. For instance, Han et al. (2016) applied the global vector autoregressive (GVAR) model for China and found that US and Japan's TPU indexes have negative impacts on China's export. Tam Sun (2018) applied the same methodology for China and the U.S and found that the EPU indexes of these countries have significant effects on global trade flows. Constantinescu et al. (2015) used a panel model for 18 countries and found that an increase in the EPU index significantly reduces the growth of trade flows. Beckmann and Czudaj (2017) applied the vector autoregression (VAR) model for some countries and found that the EPU indexes play determining roles in trade flows transmitted by exchange rates. Shin et al. (2018) applied the VAR model for South Korea and found strong correlations between the EPU index and the current account balance of this country. Wei (2019) used the VAR model for China and found that the global EPU index has negative impacts on Chinese real export. Adedoyin et al. (2020) used the export-led growth hypothesis (ELGH) for Malaysia and found that the EPU index has moderately negative impacts on export flows for this country. Jia et a. (2020) applied a gravity model approach for 20 countries. They found that exports of countries are negatively associated with the EPU indexes. However, unlike the EPU index, very little is known about whether changes in the TPU index affect trade flows in trade models. Therefore, we believe that this study, which uses the TPU index, will bridge this gap in relevant literature. The empirical methodology of this study consists of two steps. In the first step, we decompose the TPU indexes of both countries into their increases ( +) and decreases (-) in the following partial sum process: (1) where TPU + and TPU − are partial sums of increases ( +) and decreases (-) of the TPU indexes. Hence, we obtain the TPU US + t , TPU US − t for the U.S and TPU JPN + t , TPU JPN − t for Japan. In the second step, we apply the nonlinear ARDL (autoregressive distributed lag) model of Shin et al. (2014) The variables in the model above are defined as follows: BTB JPN_US it : Japan's bilateral trade balance with the U.S, defined as the ratio of Japan's imports ( m ) from the U.S and Japan's exports ( x ) to the U.S as a ratio of m∕x . This means that BTB JPN_US it = m∕x. This ratio was proposed by Haynes and Stone (1982) and utilized by Bahmani-Oskoosee and Brooks (1999) . Dependent variable of this study is based on the form of bilateral balance which shows the relative strength of a country's trade performance as a whole. Consequently, this form variable can make more sense than taking the variables export and import individually on a country's trade performance. REXR JPY_USD t ∶ the CPI adjusted real exchange rate between the Japanese YEN (JPY) and the USD. It is defined as D Covid is the COVID-19 pandemic and defined as a Dummy variable that takes value 1 after February 2020 as the first month for the worldwide pandemic cases recorded. The expected sign of 1 is to be positive since depreciations in JPY will improve BTB JPN_US it . The positive sign here denotes the same direction impact of 1 on BTB JPN_US it . The expected signs of 2 and 3 are to be positive and negative, respectively. This means that while increases in Japan's income will worsen BTB JPN_US it due to increases in Japan's import from the U.S (it denotes same direction impact), increases in US income will improve BTB JPN_US it due to increases in US import from Japan (it denotes different direction impact). Finally, the expected signs of 4 , 5 , 6 (3) and 7 are to be either positive or negative. This means that there is not any specific expectation for them about how they interact with BTB JPN_US it . The expected sign of 8 is to be positive. This means that increases in COVID-19 cases will worsen. BTB JPN_US it due to decreases in Japan's export to the U.S. The model of this study was run for both aggregated and disaggregated data, separately. The disaggregated data model includes 60 (3-digits) industrial commodities traded between Japan and the U.S. The rationale of using disaggregated data is to avoid aggregation biases, which can cause misleading results. This means that disaggregated data can discover potentially concealed yet existing relationships between dependent and independent variables, which the aggregated data model cannot detect. Monthly commodity flows were obtained from the US Census Bureau. The nominal exchange rates, CPIs and GDPs series were obtained from the Federal Reserve Bank of St. Louis. The TPU index series of the US and Japan were obtained from https:// www. polic yunce rtain ty. com. The sample period is 1996M1-2021M5. Following the basic model in Eq. 3, we apply the nonlinear ARDL model for both aggregated and disaggregated data in Eq. 4. The advantage of using this nonlinear model is that it is applicable regardless of whether the underlying regressors are I(0), purely I(1) or mutually cointegrated (Pesaran and Pesaran, 1997) . In Eq. 4, we determine the long-run impacts of changes in TPU JPN + Descriptive statistics of the variables are provided in Table 1 . The low standard deviation indicates that the data of the variables are clustered closely around the mean. Table 2 reports the estimates of normalized long-run coefficients and diagnostics of the nonlinear ARDL model for both aggregated and disaggregated data. Test results in Table 2 will be evaluated for aggregated and disaggregated (industries) data models, separately. The letters "W" and "I" in this table and text imply that the relevant independent variable in the relevant industry worsens and improves BTB JPN_US it , respectively. Furthermore, the letters "S" and "A" (at last two columns) correspond to symmetry and asymmetry determined by the Wald (4) is not statistically significant. This can be interpreted as meaning that neither country's consumers, through their import demands, are exchange rate-sensitive to each other's products. While rises in Japan's income This can be interpreted as meaning that US consumers, through their import demands, are income-sensitive to Japanese products and they buy these products while their income levels rise. However, Japanese consumers are not income-sensitive to US products. Test results in Table 2 Based on the behaviors of Japanese consumers, through import demands, this can be interpreted as meaning that Japanese consumers are not uncertainty-sensitive to US products and they continue to buy such products, while Japan's TPU index rises ( TPU JPN + t ). Similarly, they are uncertainty-sensitive to US products and they reduce their purchases of these products while TPU index falls ( TPU JPN − t ). In regards to changes in the US TPU index, while rises in the US TPU index ( Based on the behaviors of US consumers, through their import demands, this can be interpreted as meaning that US consumers are not uncertainty-sensitive to Japanese products and they continue to buy such products while the US TPU index rises ( TPU US + t ). Similarly, US consumers are uncertainty-sensitive to Japanese products and they reduce their purchases of these products while the US TPU index falls ( TPU US − t ). Aggregated data model finds no impacts of the COVID-19 on BTB JPN_US it since the coefficient of L D Covid is insignificant. Empirical results of the disaggregated data model (industries) are totally different than empirical results of the aggregated data model. While aggregated data model finds no impacts of exchange rate on BTB JPN_US it , the disaggregated data Table 2 The nonlinear ARDL model estimation results (normalized long-run coefficient) (1998) , the disaggregated data finds that this pandemic worsens this balance for 10 industries coded 334, 431, 515, 625, 629, 712, 714, 782, 874, and 971 . Based on the behaviors of Japanese consumers, through their import demands, it can be interpreted that they are not uncertainty-sensitive to US products coded 516, 598, 625, 714, 747, 764, 773, 785, and 898 , while Japan's TPU index rises. However, they are uncertainty-sensitive to US products coded 081, 411, 699, 741, 744, 762, 778, 792, 931, and 971 , while Japan's TPU index rises. This can be interpreted that Japan continues to import mainly inelastic US products such as organic chemicals (516), miscellaneous chemical products (598), and telecommunication equipment that are used as intermediate goods in this country. Similarly, Japan does not import mainly elastic US products such as feeding stuff for animals (081), animal oils and fats (411), and gold, nonmonetary (971). In the same vein, US consumers are uncertainty-sensitive to Japanese products coded 232, 431, 741, 784, 786, 792, 931, and 971 , while the US TPU index rises. However, they are not uncertaintysensitive to Japanese products coded 111, 421, 541, 695, 748, 772, 774, 785, 898, and 899 , while the US TPU index rises. In the evaluation of overall test results in Table 2 , we could not find any impact of either the US TPU index or Japan's TPU index on Japan's bilateral trade balance with the U.S BTB JPN_US it for 18 out of 60 industries. Furthermore, both rises and falls in Japan's TPU index ( TPU JPN + t , TPU JPN − t ) have significant improving or worsening impacts on this balance for a total of 30 industries. Similarly, both rises and falls in the US TPU index ( TPU US + t , TPU US − t ) have significant improving or worsening impacts on this balance for a total of 24 industries. In the comparison of US and Japan's TPU indexes, this can be interpreted as meaning that the impacts of Japan's TPU index on this balance are higher than those of the US TPU index. For the sake of economy, the determination of symmetry and asymmetry by the Wald test will be explained for only one industry, namely the one coded 781. While rises ( TPU JPN + t ) and falls ( TPU JPN − t ) in Japan's TPU index have different magnitude and different direction impacts (which denotes asymmetry) on BTB JPN_US it , rises ( TPU US + t ) and falls ( TPU US − t ) in the US TPU index have the same magnitude and same direction impacts (which denotes Symmetry) on it. The equations of − 5 ∕ 1 = − 6 ∕ 1 and − 7 ∕ 1 ≠ − 8 ∕ 1 correspond to symmetry and asymmetry, respectively. This study examines potential impacts of Japan's and US trade policy-related uncertainties, scaled by TPU indexes, on Japan's bilateral trade balance with the U.S concerning 60 industries. Furthermore, this study also considers-examines the impacts of the COVID-19 pandemic on this country's trade balance. The rationale of this consideration is based on that international trade volumes can be highly and directly affected by such a big pandemic. In this examination, we assume that aggregated trade data may conceal the true existing relations between this balance (dependent variable) and TPU indexes (independent variables) in our model. In order to discover such potentially existing relations and avoid this aggregation bias, we use disaggregated data. We also assume that increases and decreases in both countries' TPU indexes, considered separately, may have different (nonlinear) impacts on Japan's bilateral trade balance with the U.S. In order to examine these separate impacts, we apply the nonlinear ARDL model, which technically enables such a separation. Empirical findings of this study strongly verify that our assumptions are true. The disaggregated data model clearly finds which independent variable in which industry improves or worsens this balance aggregated data cannot find. Furthermore, the nonlinear model clearly reveals different impacts of increases or decreases of TPU indexes on this balance. The main empirical finding of this study is that trade policy-related uncertainties of both countries (TPU indexes) have significant impacts on Japan's bilateral trade balance for 42 out of 60 industries, improving or worsening this balance (shown in Table 2 ). Additionally, based on the behaviors of Japanese and US consumers, through their import demands, it can be interpreted that Japanese consumers are not uncertainty-sensitive to US products and they continue to buy these products even Japan's TPU index keeps rising. Similarly, US consumers are also not uncertainty-sensitive to Japanese products and they continue to buy these products even the US TPU index keeps rising (findings from the aggregated data model). Another expected finding is that the COVID-19 pandemic worsens the trade balance of Japan for different industries such as oil (not crude), rubber tires and accessories, nonelectric engines and motors, special purpose motor vehicles, and organo-inorganic-heterocyclic compounds. This can be interpreted that Japan is not the COVID-19-sensitive to mainly inelastic US products. We believe that the overall findings of this study will provide both countries' policymakers with crucial information about how each industry responds to changing trade policy uncertainties and pandemics. Hence, they will be able to create individual industry-level uncertainty-sensitivity and the COVID-19 affected maps-tables, which will help them manage their bilateral trade balances proactively. In this context, the findings of this study indicate the need for future empirical studies concerning other countries for having such maps-tables globally and bilaterally. The export-led growth in Malaysia: Does economic policy uncertainty and geopolitical risks matter? Economic policy uncertainty and banks' loan pricing Bilateral J-curve between US and her trading partners Policy uncertainty and the demand for money in the United Kingdom: are the effects asymmetric? 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