Volume 13, Issue 1

We explore the behavior of the divorce rates in 17 OECD countries for the period 1960–2010. Many studies have found persistence in divorce rates after changes in divorce laws. We formulate a nonlinear model to explain the behavior of divorce rates, over time, after a large change in divorce rate, including changes in divorce laws.We argue that the stationary smooth transition regression (STR) approach is useful in analyzing divorce rates. We reject the persistence result and, in addition, provide evidence on asymmetries in divorce rates. This suggests that one should be careful when interpreting the effects of the changes in divorce rates (especially changes in the divorce and family laws) on divorce rates.

In their seminal work, Lettau, M., and S. Ludvigson, 2001, “Consumption, Aggregate Wealth, and Expected Stock Returns.” The Journal of Finance 56 (3): 815–49. https://doi.org/10.1111/0022-1082.00347, demonstrated that there exists a long-run relationship between consumption, asset holdings, and labor income. They denoted this relationship as cay and showed it to be quite successful in predicting the behavior of real stock returns. Their estimation procedure assumes that consumption, asset wealth, and labor income are first-order integrated ( I (1), nonstationary) and that their linear combination forms a zero-order integrated ( I (0), stationary) series. This paper proposes a more general framework in the estimation of the cay model by allowing both the series and the long-run equilibrium to be fractionally integrated. We use the recently developed Fractionally Cointegrated VAR (FCVAR) approach to estimate the cay model. Results show that: (i) the series are nonstationary but mean-reverting processes, (ii) there exists a long-run equilibrium between consumption, asset wealth, and labor income, (iii) this long-run relationship is a stationary fractionally integrated process, and (iv) the estimated cay using the FCVAR approach shows the same desirable forecasting properties as its predecessor.

This study aims to contribute in the area of foreign exchange forecasting. Exchange rate plays an essential role for the economic policy of a country. Due to the floating exchange rate regime, and ever-changing economic conditions, analysts have observed significant volatility in the exchange rates. However, exchange rate forecasting has been a challenging task before the analysts over the years. Various stakeholders such as the central bank, government, and investors try to maximize the returns and minimize the risk in their decision-making using exchange rate forecasting. The study aims to propose a novel ensemble technique to forecast daily exchange rates for the three most traded currency pairs (EUR/USD, GBP/USD, and JPY/USD). The ensemble technique combines the linear and non-linear time-series forecasting techniques (mean forecast, ARIMA, and neural network) with their most optimal weights. We have taken the data of more than seven years, and the results indicate that the proposed methodology could be an effective technique to forecast better as compared to the component models separately. The study has crucial economic and academic implications. The results derived from this study would be useful for policymakers, regulators, investors, speculators, and arbitrageurs.

This paper analyzes the time-varying impact of macroeconomic forces on the synchronization in housing movements across all the U.S. states. Using a Bayesian modeling approach, the house price movements are decomposed into national, regional and state-specific factors. We then analyze the time-varying impact of macroeconomic forces on these national and regional factors. Evidence suggests that in several Western and Eastern states the house price variations are dominated by the national factor, whereas the regional factor dominates the Southern and Midwestern markets. These factors are found to have a time-varying relationship with most macroeconomic indicators with particularly pronounced time-variation caused by national house prices, inflation rate and consumer sentiments.