Exchange Rates and Dominant Macro Fundamental

Various exchange rates are driven by different macro variables. This paper uses a measure of exchange rate informativeness about future macro fundamentals to identify the dominant macroeconomic fundamental of 120 currencies. Exchange rate informativeness is defined as the share of future fundamental shocks in the variance of exchange rate changes. The distribution of exchange rate informativeness shows that the median exchange rate informativeness about the dominant macro fundamental is 0.11. Moreover, the exchange rate of most high-inflation countries is driven by expectations of the price differential. Expectations of the relative output drive the currency of countries with high export concentration.

On the Validity of Purchasing Power Parity: The Case of Sierra Leone

This paper attempts to empirically validate the Purchasing Power Parity (PPP) theory in the context of Sierra Leone. To achieve this objective, cointegration and error correction techniques were utilized to account for both long and short-run dynamics over the period 2007Q1 to 2019Q1. The Engel-Granger cointegration technique was utilized to ascertain the long-run relationship between the exchange rate and the price differential between Sierra Leone and the United States of America, while the redundant variable test was used to attain the parsimonious short-run error correction model. The results indicated a cointegrating relationship, while the coefficient on the price differential was greater than one (1), reflecting that the PPP does not hold for Sierra Leone. Moreover, the short-run results showed a rejection of the theory and rather endorses the presence of depreciation inertia, where past depreciation of the exchange rate is a major determinant of its current depreciating trend.

Parametric and Non-Parametric Statistical Methods in the Assessment of the Effect of Property Attributes on Prices

One of the basic problems in the comparison-based property valuation process is to determine the influence of property attributes on their price differential. Due to the qualitative character of the majority of property attributes as well as to the distributions of both prices and attributes, their effect on the price differential is increasingly often assessed by means of non-parametric statistical methods. As a tool for determining the effect of attributes on prices, many authors propose parametric methods, in particular multiple regression models. The study presents a comparison of the results of property market attribute weight estimation obtained by means of the Spearman rank correlation coefficient with the ceteris paribus adjustment and the multiple regression model based on a set of transactions with built-up land property. In both of the analyzed methods, qualitative variables were modeled with the use of the Osgood semantic differential scale. The results of the analysis show the equivalence of the applied methods. Property attribute weights calculated using the method based on the rank correlation coefficient with the ceteris paribus adjustment and the multiple regression model, both with the same level of relevance, showed almost identical values. This indicates that both parametric and non-parametric methods can be used to estimate weights.