How COVID-19 Could Change the Economics of the Plastic Recycling Sector

The price of oil has a great influence on prices of recycled plastics and, therefore, plastic recycling efforts. Here, we analyze the effects of the ongoing COVID-19 pandemic on crude oil price and how this, in turn, is likely to affect the degree of plastic recycling that takes place. Impulse response functions and variance decompositions, calculated from the structural vector autoregression, suggest that changes in crude oil prices are key drivers of the price of recycled plastics. The findings highlight that because plastics are made from the by-products of oil, falling oil prices increase the cost of recycling. Therefore, the price of recycled plastics should be supported using taxes while encouraging sustained behavioral changes among consumers and producers to selectively collect and recycle personal protective equipment so that they do not clog our landfills or end up in our water bodies as plastic waste.

Does the Incomplete Exchange Rate Pass-through Exist in Sudan?

The purpose of this paper is to examine the nominal exchange rate pass-through to domestic prices in Sudan from 1978–2017. An autoregressive distributed lag (ARDL) approach to cointegration is employed. The analysis is based on impulse response functions (IRFs) and forecast error variance decompositions (FEVDs). The dynamics of the cointegrated system can be investigated via the variance decompositions and IRFs. The findings confirm that the degree of exchange rate pass-through in Sudan is incomplete, and the empirical results also show that the domestic price index is predominantly caused by foreign price in both the short and long runs, in addition to the import price index and the nominal exchange rate; the exchange rate shock has a negative effect on the domestic price. Furthermore, FEVDs analysis illustrates that the variation in domestic price is primarily determined by the import prices, while changes in the exchange rate are primarily determined by the exchange rate itself.

The impact of inflation on Islamic banks’ home financing risk: Before and during the COVID-19 outbreak

The COVID-19 outbreak has had a severe impact on nearly all industries, including Islamic banking, which plays a significant role but is exposed to higher risk. This study aims to evaluate the credit risk that Islamic banks in Indonesia have been exposed to related to home financing before and during the COVID-19 outbreak. Panel data are employed covering the period January 2016 to September 2020 on a monthly basis. The data were analyzed using a dynamic panel approach to present a distinct picture of Sharia-compliant property financing before and during the COVID-19 outbreak. In general, the findings show that the macroeconomic variable reflected by regional inflation has had a different influence in the two periods, with Islamic banks having had much more exposure to macroeconomic risk, specifically in home financing, during the epidemic. In addition, the different influences are also shown by the study results, which show that provinces on Java Island face less risk exposure than those outside Java. In terms of impulse response factors and variance decompositions’ result, before the outbreak, the response of home financing risk to inflation tended to be more stable. However, during the outbreak, the movement has tended to fluctuate more, especially outside Java Island. The same result for variance decompositions shows a similar trend, with inflation tending to have a larger impact during the outbreak. AcknowledgmentsWe are grateful to the Direktorat Penelitian dan Pengabdian Masyarakat (DPPM) Universitas Islam Indonesia No. 001/Dir/DPPM/70/Pen.Unggulan/XII/2020 for support and providing a research grant for the study.

Identification and inference with ranking restrictions

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

Impact of Macroeconomic Variables on Housing Prices in Saudi Arabia: A VAR Approach

This paper examines how housing prices are determined by macroeconomic factors in Saudi Arabia, namely, Gross Domestic Product Per capita (GDPP), Consumer Prices Index (CPI), and Unemployment Rate (UNEMP). Quarterly data for a period (2014q1 – 2019q4) were collected from publications of Saudi Arabian Monetary Authority (SAMA). Vector Autoregression Analysis (VAR) is employed to capture the dynamic effect of the variables on housing prices. Granger Causality, Variance Decomposition and Impulse response function are also used. The results show that housing prices are insignificantly and positively related to GDPP, whereas it is negatively related to both (CPI & UNEMP). Only CPI has a significant relationship. The three variables, jointly, have Granger causality on HPI. Variance decompositions show that CPI is the variable with the highest explanatory power over the variation of housing prices, followed by GDPP and the UNEMP respectively indicating that CPI is the most influential determinants for housing prices.