Exchange rate parities and Taylor rule deviations

This paper investigates the PPP and UIP conditions by taking into account possible nonlinearities as well as the role of Taylor rule deviations under alternative monetary policy frameworks. The analysis is conducted using monthly data from January 1993 to December 2020 for five inflation-targeting countries (the UK, Canada, Australia, New Zealand and Sweden) and three non-targeting ones (the USA, the Euro Area and Switzerland). Both a benchmark linear VECM and a nonlinear Threshold VECM are estimated; the latter includes Taylor rule deviations as the threshold variable. The results can be summarized as follows. First, the nonlinear specification provides much stronger evidence for the PPP and UIP conditions, the estimated adjustment speed towards equilibrium being twice as fast. Second, Taylor rule deviations play an important role: the adjustment speed is twice as fast when deviations are small and the credibility of the central bank is higher. Third, inflation targeting tends to generate a higher degree of credibility for the monetary authorities, thereby reducing deviations of the exchange rate from the PPP- and UIP-implied equilibrium.

Use of protocol-driven medication refills by pharmacists decreases rheumatologist in-basket work and improves rheumatologist satisfaction

Burn-out among US physicians has been on the rise in the past few decades. Similarly, rheumatologists in the Geisinger Health System have experienced professional dissatisfaction through significant administrative burden and in-basket work. We embedded pharmacists into our rheumatology team in 2019 with the aim of reallocating medication refills to pharmacists, trained professionals in this domain, to help reduce physician workload and burn-out and increase satisfaction. Protocol-driven medication refill parameters per the American College of Rheumatology guidelines and new refill workflows for disease-modifying antirheumatic drugs (DMARDs) and non-DMARDs were created for use by our rheumatology pharmacists. Monthly data on medication refill volume and time saved for rheumatologists were collected from 1 January 2019 to 31 March 2021. Statistical analysis was completed via Shewhart p-charts. The volume of refills by rheumatologists decreased by 73% and the time saved per month for all the rheumatologists increased to 41.5 hours within 6 months. Physicians’ feedback was obtained via anonymous electronic surveys preintervention and postintervention. The statistical difference between the presurveys and postsurveys was calculated via two-tailed unpaired t-testing. It demonstrated reduced burn-out and improved workplace satisfaction. This study showed that the integration of rheumatology pharmacists into our practice can help improve the work life of the rheumatologists. It is important for physicians’ well-being to practice at the top of their scope and achieve work–life balance.

Cointegration and Causality Analysis of Portuguese Tourism and Air Quality

This chapter aims to fill the knowledge gap regarding the relationship between tourism and air quality, specifically in the Portuguese tourism industry, with a focus on tourist nationality. It examines whether this relationship differs according to tourist origin. This study uses an air pollutant, PM10, with a strong impact on human health that has been highly neglected in the literature. Despite the great use of CO2 in assessing the causal relationship between tourism and the environment, this is not the best indicator of air quality (AQ). This chapter presents results by applying vector autoregressive models (VAR) with monthly data for the period of 2007-2017, considering the nationality of tourists that visit Portugal. Results suggest that PM10 levels and tourism are negatively correlated (in the Pearson sense) with a link between them in the long run. This relationship is confirmed by the four methodologies tested. The negative relation in Pearson and cointegration results suggests that tourism can be affected by AQ in Portugal and may lead to better AQ.

New software package for archival of synoptic And upper air meteorological data

The functions of a software package of 6 programmes developed for retrieving, decoding quality control and formatting of surface and upper air coded data have been presented here in brief. Intelligent use has been made of Fortran- 77 fact1ltles to make these programmes extremely efficient. Global data for surface and upper air received on GTS for an entire day is sorted, decoded & formatted after quality control in about three and a half minutes (CPU time) on VAX 8810 system. The programmes do the management of files and can also be used for decoding the monthly data files of hard copy data. For coding of data, FGGE code has been used with very minor modifications. The results of quality control checks and number of reports received hour wise for each synoptic hour for each WMO block are monitored. Information from both is displayed on the terminal in tabular form and also recorded in disk for monthly archival.

Perbandingan Data Harian dan Data Bulanan pada Suhu Permukaan Laut di Samudera Hindia Menggunakan Partial Least Square-Structural Equation Modelling

Sea surface temperature (SST) is one of the features of climate variability that has a significant role in human activities. This study aims to predict and determine whether weather and climate variables with their measuring indicators can predict changes in SST by comparing daily and monthly data. This study uses a partial least square-structural equation modeling (PLS-SEM) approach which can predict the causality relationship between exogenous latent variables and endogenous latent variables. The results obtained from this study are, from the nine indicators used there are only 6 significant indicators with a loading factor value 0.7, namely sea surface temperature (oC) as a measure of latent variables SST changes, wind speed (m/s) and humidity relative (%) as a measure of the latent variable of weather, and air temperature (oC), short-wave solar radiation (w/m2) for daily data, and long-wave solar radiation (w/m2) for monthly data as a measure of climate latent variable. Inner model obtained on daily data: SST change (η) = -0.285 weather + 0.650 climate + and on monthly data SST change (η) = -0.330 weather + 0.793 climate +. In monthly data, weather and climate latent variables and their measuring indicators have a greater influence on changes in SST with the coefficient values in the model obtained being greater than in daily data. Latent variables that have a significant effect on changes in SST are weather and climate. This shows that if there is an increase or decrease in weather and climate it can cause significant changes to the SST. The value of the criteria on the outer model and inner model on daily and monthly data obtained better results on monthly data. The presence of more missing data in daily data can be one of the causes of this happening.

FORECASTING THE CONSUMER PRICE INDEX IN YOGYAKARTA BY USING THE DOUBLE EXPONENTIAL SMOOTHING METHOD

The Consumer Price Index (CPI) is an indicator that is often used to measure the inflation rate in an area, or can be interpreted as a comparison between the prices of a commodity package from a group of goods or services consumed by households over a certain period time. The spread of COVID-19 throughout the world affects the economy in Indonesia, especially Yogyakarta. Forecasting CPI data during the COVID-19 pandemic has the benefit of being an illustration of data collection in the CPI of D.I Yogyakarta Province in the predicted period. This is useful as a comparison with the original data at the time of data collection and publication, as well as a consideration in making policies and improving the economy. Researchers use the Double Exponential Smoothing (DES) method to predict the CPI of Yogyakarta D.I Province, which aims to determine the best forecasting model and forecasting results. This method is rarely used in research on CPI data forecasting in Yogyakarta. The data in this study are monthly data from March 2020 to August 2021. The highest CPI in Yogyakarta occurred in August 2021 at 107.21 or 107.2, while the lowest CPI in Yogyakarta occurred in April 2020 at 105.15 or 105.2. The average CPI in Yogyakarta per month is 106.1. The Mean Absolute Percentage Error (MAPE) value obtained from the DES method is 0.1308443%, so that the accuracy of the model is 99.869%. Forecasting with the DES method is quite well used in forecasting the CPI data of Yogyakarta in September 2020 - November 2021. The results of CPI forecasting in Yogyakarta using the DES method were 107.2602, 107.3104, and 107.3606 from September-November.

PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING HOLT DAN FUZZY TIME SERIES CHEN UNTUK PERAMALAN HARGA PALADIUM

Palladium is one of the precious metal commodities with the best performance since 3 years ago. Palladium has many benefits, including being used in the electronics, medical, jewelry and chemical industries. The benefits of palladium in the chemical field are that it can help speed up chemical reactions, filter out toxic gases in exhaust gases, and convert the gas into safer substances, so palladium is usually used as a catalyst for cars. Forecasting is a process of processing past data and projected for future interest using several mathematical models. The model used in this study is the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods. The process of forecasting palladium prices using monthly data from January 2011 to December 2020 with the Double Exponential Smoothing Holt method and the Fuzzy Time Series Chen method will be carried out in this study to describe the performance of the two methods. Based on the results of the analysis, it can be concluded that the Double Exponential Smoothing Holt and Fuzzy Time Series Chen methods have equally good performance with sMAPE values of 6.21% for Double Exponential Smoothing Holt and 9.554% for Fuzzy Time Series Chen. Forecasting for the next 3 periods using these two methods generally produces forecasting values that are close to the actual data.

Terrorism and Tourism

The booming tourism sector in Turkey has resulted in major economic gains in terms of direct revenues to both government and private sectors alike. Turkey had more than 45 million visits in 2018, and top inbound arrivals were from Russia and European Union (EU) members, such as Germany, the United Kingdom, and Bulgaria, among others (Organization for Economic Co-operation and Development [OECD], 2020). However, terrorism is becoming a challenge to tourism development. This study explores terrorism–tourism dynamics in Turkey. The short- and long-run impacts of terror attacks on tourism revenues were examined within the framework of an autoregressive lag (ARDL) model using monthly data for the period between 2012 and 2018. The empirical findings did not support terrorism's effects on tourism revenues. However, in the long run, terror-related casualties and fatalities on tourism revenues had different effects. The findings affirm that the casualty rate has a stronger impact on terrorism–tourism dynamics in Turkey because a 1% increase in reported injuries from terror attacks hampers revenues by approximately 0.1%. Hence, adequate and continuous support for general security establishments is imperative while strengthening commitments to the international cooperation on the war against terrorism to proactively contain the undesirable impacts of terrorism in the Turkish tourism industry,

Investigating the Climate-Growth Response of Scots Pine (Pinus sylvestris L.) in Northern Poland

Research Highlights: This study used a 99-year time-series of daily climatic data to determine the climate-growth relationship for Scots Pine (Pinus sylvestris L.) growing in Northern Poland. The use of daily climatic data improved the calculated climatic response of the trees. Background and Objectives: It was hypothesised that daily temperature and precipitation data would more precisely identify climate–growth relationships than monthly data. We compared our results to a previous study conducted in the 1990s that utilised monthly precipitation and temperature data. Materials and Methods: The chronology construction and data analyses were performed using CooRecorder, CDendro and R packages (dplR, treeclim, dendrotools). Forty-nine cores from 31 trees were included in the final chronology. Results: The precipitation and temperature of March had the strongest influence upon ring-widths. Despite a statistically significant correlation between monthly temperature and ring-widths, reduction of error (RE) and coefficient of efficiency (CE) statistics confirmed that daily data better describe the effect of climate on tree rings width than monthly data. Conclusions: At this site, the growing season of Scots pine has changed with the observed association with precipitation now starting as early as February–March and extending to June–July.

INVESTOR SENTIMENT CONNECTEDNESS: EVIDENCE FROM LINEAR AND NONLINEAR CAUSALITY APPROACHES

This paper provides a novel perspective in determining the Granger causality of sentiment across the US, Latin America, Eurozone, Japan and Asia (excluding Japan), based on monthly data covering the period of January 2003–November 2017. Using a survey-based sentiment index of “sentix”, our results suggest strong evidence of nonlinearity and structural breaks making the use of linear causality models unreliable. Using a kernel-based multivariate nonlinear causality test, we find that causality runs from Eurozone to the US, Asia and Japan, with Japan also causing the Eurozone sentiment, and Latin America causing the Japanese sentiment. Interestingly, when we apply rolling estimations to detect time-varying causality for the cases of Eurozone and the US, Eurozone and Asia, Eurozone and Japan and Latin America and Japan, the results suggest evidence of bidirectional spillovers during certain months of the recent global financial crisis, and thereafter. Overall, our findings indicate that the sentiments of Japan, Asia and the US are related quite strongly with that of the Eurozone, as well as the sentiments of Japan and Latin America.