Maximum likelihood parameter estimation of the continuous time linear stochastic state space model is considered on the basis of large N discrete time data using a structural equation modeling (SEM) program. Random subject effects are allowed to be part of the model. The exact discrete model (EDM) is employed which links the discrete time model parameters to the underlying continuous time model parameters by means of nonlinear restrictions. The EDM is generalized to cover not only time-invariant parameters but also the cases of stepwise time-varying (piecewise time-invariant) parameters and parameters varying continuously over time according to a general polynomial scheme. The identification of the continuous time parameters is discussed and an educational example is presented.

This paper examines the impact of exports and its main determinants on the financial performance of the Romanian wine industry. We draw on a dataset consisting of mixed firm-level (i.e., 207 companies) data, Google Trends data, and regional variables covering the period from 2009 to 2017. We show that Romanian wine exports, at the firm level, are positively affected by regional wine yields (especially in the case of red wine varieties), temperature, and firm agglomeration, and negatively impacted by firm size. We also find a close positive correlation between financial performance and exports. (JEL Classifications: F61, L66, C23)

This study analyzes the participation decision and income impacts of fair-trade coffee certification on small-scale coffee producers in the Araku valley in India using panel data for 183 households and endogenous-switching and quantile regression methods. The results show that fair trade certification has a positive effect on income; the income of certified farmers is 17 percent higher on average than the income of uncertified coffee producers. Furthermore, fair trade certification has a “bottom of the pyramid” effect in that the largest income gains accrue to farmers in the poorer quantiles.

An import demand model, augmented with third-country effect variables, is developed to examine the effects of strong U.S. dollar, volatility of the U.S. dollar, and competition among the exporting countries on the shares of U.S. wheat in Asian markets. In the empirical model, the dependent variable is the market shares of U.S. wheat. Explanatory variables include wheat prices of exporting countries, exchange rates between the importing and exporting countries, and volatilities of the exchange rates. Panel estimation results show that the U.S. currency value and volatility, Australian wheat price, and the volatilities of Canadian and Australian currency values have significant effects on U.S. market shares.