The papers in this symposium use Monte Carlo simulations to demonstrate the consequences of estimating time series models with variables that are of different orders of integration. In this summary, I do the following: very briefly outline what we learn from the papers; identify an apparent contradiction that might increase, rather than decrease, confusion around the concept of a balanced time series model; suggest a resolution; and identify a few areas of research that could further increase our understanding of how variables with different dynamics might be combined. In doing these things, I suggest there is still a lack of clarity around how a research practitioner demonstrates balance, and demonstrates what Pickup and Kellstedt (2021) call I(0) balance.

China has become the largest importer of roundwood and lumber products in recent years. In this study, the degree of competition among major supplying countries in China's import market is measured through the inverse residual demand elasticity over 1995–2015. Time-series properties of the data are considered through the autoregressive distributed lag model. The analysis reveals that market power exists in roundwood and lumber markets for a few supplying countries. The suppliers in the lumber import market have more market power than those in the roundwood market. Individual countries with substantial market power have more fluctuations in trading volumes.

The effects of the exchange rate and the income and money supply of the United States and its major trading partners on the U.S. agricultural trade balance are examined using an autoregressive distributed lag (ARDL) model. Results suggest that the exchange rate is the key determinant of the short- and long-run behavior of the trade balance. It is also found that the income and money supply in both the United States and the trading partners have significant impacts on U.S. agricultural trade in both the short and long run.

This study examines the short- and long-run effects of changes in macroeconomic variables—agricultural commodity prices, interest rates and exchange rates—on the U.S. farm income. For this purpose, we adopt an autoregressive distributed lag (ARDL) approach to cointegration with quarterly data for 1989–2008. Results show that the exchange rate plays a crucial role in determining the long-ran behavior of U.S. farm income, but has little effect in the short-run. We also find that the commodity price and interest rate have been significant determinants of U.S. farm income in both the short- and long-run over the past two decades.