Quarterly Frequency Research Articles

Inflation forecasting in turbulent times

AbstractIn the recent years many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with Student t-distributed innovations or with stochastic volatility. Whereas inflation, industrial production, as well as oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency setup using the forward-filtering–backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries that extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of the mixed-frequency BVAR model, we compare our inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score.

Spatial assessment of water quality in Mananga River in Talisay City, Cebu, Philippines

This paper focuses on the degradation of water quality in the Mananga River and aims to analyze spatial changes for the recommendation for rehabilitation. The use of ArcGIS and JMP software allows for the assessment of water quality and the identification of the causes of the contaminants coming from probable sources that are near the river. These activities are mainly rural areas and businesses that could have affected the degradation of the quality of the water in the river. By generating spatial distribution maps and conducting rapid profiling, this paper provides recommendations for rehabilitation. The significance of this paper lies in contributing to decision-making processes and the formulation of protective laws that can help in stop the degradation of the water quality of the river. Limitation includes the quarterly frequency of the water monitoring of the river. The results of this paper suggest that the Mananga River does not meet the requirements for DENR Administrative Order 2016-08 for type A rivers. The spatial distribution maps generated were able to visualize and identify the anthropogenic activities that may be affecting the water quality of the river. The DO showed a value from 0.0011 mg/L – 18.800 mg/L, BOD has a value ranging from 1.000 mg/L to 81.500 mg/L, 4.000 mg/L – 325.980 mg/L for TSS, and pH showed values from 7.400 to 8.800. These findings emphasize the need for government intervention and the development of automated river monitoring systems to facilitate testing and rehabilitation efforts.

U.S. macroeconomic news and low-frequency changes in bond yields in Canada, Sweden and the U.K.

This paper investigates the importance of U.S. macroeconomic news in driving low-frequency fluctuations in the term structure of interest rates in Canada, Sweden, and the U.K. We follow two complementary approaches: First, we apply a regression-based framework that aggregates the impact of daily macroeconomic news on bond yields to a lower quarterly frequency. Next, we estimate a macro-finance affine term structure model linking the daily news to lower-frequency changes in bond yields and its expectations and term premia components. Both approaches show that U.S. macroeconomic news is an important source of lower-frequency quarterly fluctuations in bond yields in these open economies, and even more important than their respective domestic macroeconomic news. Furthermore, the macro-finance model shows that U.S. macroeconomic news is particularly important in explaining low-frequency changes in the expectation components of the nominal, real, and break-even inflation rates.

Multi-year Epidemiology of Nontuberculous Mycobacteria Across a Diverse Healthcare System

Background: Nontuberculous mycobacteria (NTM) are ubiquitous potential pathogens implicated in healthcare-associated outbreaks. There is a paucity of studies describing transmission risk in the healthcare setting. To estimate the potential healthcare-associated transmission of NTM, we characterized the frequency of NTM clinical isolates across our multi-facility healthcare system. Method: We performed a retrospective review of all clinical NTM isolates at 21 healthcare facilities in a large health system between January 2019 through June 2023 (inclusion criteria: all first unique species). We analyzed the quarterly frequency of isolates for each species, by facility. We identified higher-than-expected species frequencies, which was defined as a quarterly frequency ≥50% higher than the average quarterly frequency for that facility, for the entire study period (analysis omitted for any hospital with an average quarterly frequency 10 unique patient isolates in any 12-month period or >2 in a single month except for M. abscessus at Hospital A. The quarterly frequency of the three most common species among hospitals with ≥2 unique isolates per 12-month period are displayed in figure 2. An increase of 50% from the average quarterly infection count occurred 34 times for M. avium complex across 10 hospitals, 14 times for M. gordonae across 2 hospitals and 14 times for M. lentiflavum across 2 hospitals (Figure 3). Conclusion: A diverse group of NTM were isolated across our healthcare system over the study period, most commonly M. avium complex, M. gordonae, and M. lentiflavum, each with hospital-specific temporal frequencies that suggest the potential for undetected outbreaks, while frequencies of less commonly isolated species were rarely suggestive of potential undetected outbreaks. Further epidemiologic investigation of in-hospital transmission routes, with whole genome sequencing to determine genetic relatedness, is necessary to identify undetected outbreaks.

Can Words Speak Louder Than Actions? Using Top Management Teams’ Language to Predict Myopic Marketing Spending

Myopic marketing spending—curtailing marketing and research and development expenses to boost earnings—damages firms’ long-term value. Despite this, top management teams are often myopic, and by the time investors or boards detect such short-termism, it is too late to react or intervene. This research introduces a novel prediction method by analyzing the language top management teams use in earnings calls, specifically focusing on marketing and earnings emphasis, to predict future instances of myopic marketing spending. Through linguistic dependency parsing of almost 11 million sentences extracted from nearly 25,000 quarterly earnings call transcripts of 1,197 firms between 2008 and 2019, the authors demonstrate that the proposed approach can predict myopic marketing spending at a quarterly frequency for up to one year in advance. They find that an increase of one standard deviation in earnings emphasis is associated with a 23.68% increase in the likelihood of future myopic marketing spending. Investments based on the proposed approach produce 1.61% additional annual abnormal returns compared with models that exclusively use known predictors of myopic marketing spending, while offering earlier foresight and more frequent opportunities for intervention. This reduces information asymmetry for investors and boards of directors.

Constructing quarterly Chinese time series usable for macroeconomic analysis

During episodes such as the global financial crisis and the Covid-19 pandemic, China experienced notable fluctuations in its GDP growth and key expenditure components. To explore the primary sources of these fluctuations, we construct a comprehensive dataset of GDP and its components in both nominal and real terms at a quarterly frequency. Applying two SVAR models to this dataset, we uncover the principal drivers of China's economic fluctuations across different episodes. In particular, our findings reveal the stark and enduring impacts of consumption-constrained shocks on GDP and all of its components, especially household consumption, both during and in the aftermath of the COVID-19 pandemic.

Vutrisiran: a new drug in the treatment landscape of hereditary transthyretin amyloid polyneuropathy

ABSTRACT Introduction Hereditary transthyretin (ATTRv) amyloidosis is a progressive, fatal disorder caused by mutations in the transthyretin (TTR) gene leading to deposition of the misfolded protein in amyloid fibrils. The main phenotypes are peripheral neuropathy (PN) and cardiomyopathy (CM). Areas covered Gene silencing therapy, by dramatically reducing liver production of TTR, has transformed ATTRv-PN patient care in the last decade. In this drug discovery case history, the authors discuss the treatment history of ATTRv-PN and focus on the latest siRNA therapy: vutrisiran. Vutrisiran is chemically enhanced and N-acetylgalactosamin-conjugated, allowing increased stability and specific liver delivery. HELIOS-A, a phase III, multicenter randomized study, tested vutrisiran in ATTRv-PN and showed significant improvement in neuropathy impairment, disability, quality of life (QoL), gait speed, and nutritional status. Tolerance was acceptable, no safety signals were raised. Expert opinion Vutrisiran offers a new treatment option for patients with ATTRv-PN. Vutrisian’s easier delivery and administration route, at a quarterly frequency, as well as the absence of premedication, are major improvements to reduce patients’ disease burden and improve their QoL. Its place in the therapeutic strategy is to be determined, considering affordability.

Dynamics of the Relationship between Inflation and Interest Rates: Testing For the Fisher Hypothesis with Structural Break(S) and Parameter Stability

In the present study, the Fisher hypothesis that considers a one-to-one and unidirectional relationship between inflation and interest rates was tested using a quarterly frequency dataset for E-7 countries. Due to the fact that parameter constancy was not established in the study, Arai & Kurozumu and Kejriwal test techniques were used and it was found that Fisher effect was valid in weak and strong forms for country groups excluding Russia. At the same time, due to the fact that coefficient indicator modulus observed in slope parameters after structural changes had negative values, contractionary monetary policies in these economies where inflation targeting strategies were implemented resulted in weakening of the relationship between inflation and interest rate variables.

United States’ 2050 carbon neutrality: Myth or reality? Evaluating the impact of high-tech industries and green electricity

Despite its economic prosperity, the United States has encountered challenges in promoting ecological quality. Therefore, it is imperative for the United States to acknowledge that maintaining its economic accomplishments requires concurrent efforts to safeguard its ecological quality. To solve this challenge, this study aims to scrutinize the impact of green electricity, globalization (in terms of trade and finance), and high technology on the United States' load capacity factor. Employing quarterly frequency data spanning from 1990 to 2020, this research utilizes cutting-edge quantile-based econometric techniques, including wavelet quantile regression, which effectively captures interrelationships across various periods and quantiles. Additionally, it employs quantile causality and quantile-on-quantile regression methods, robust in handling nonlinearity, to assess the ecological repercussions of these variables. The overarching findings reveal that, high tech, financial development, economic growth, and globalization (both in trade and finance) lessen ecological quality. Conversely, green electricity consumption consistently promotes ecological quality. Furthermore, the quantile causality results unveil evidence of feedback causality between the load capacity factor and its drivers, underpinning the need for policy formulation based on these findings.

CSP in 40 years of scientific publications

In 2024, Cadernos de Saúde Pública (CSP) celebrates 40 years of uninterrupted publications. This article analyzes the trajectory of the journal and projects its future considering contemporary challenges in scientific publishing in the field of Public Health. This study was based on the analysis of main editorial policy milestones and the identification of the most popular topics. Three periods were recognized. In the "beginning" (1985 to 1990), the journal had restricted circulation, with a quarterly frequency. The word "epidemiology", used in a descriptive context, is highlighted. Then, in the period of "consolidation" (1991 to October 2012), CSP was indexed in the Index Medicus-MEDLINE database and had its online collection published by the Scientific Electronic Library Online (SciELO), expanding the reach of articles. In this period, the words that characterize the field of policy, planning, and management were clearly seen, and terms related to the epidemiological method emerged. The period of "diversity" (November 2012 to November 2023) introduced the requirement of an external editor to the Oswaldo Cruz Foundation's staff among the three co-editors-in-chief, aiming to ensure editorial independence. New activities and a similar frequency among the five most common terms highlight such "diversity". Several issues are observed in scientific publishing in 2024: non-commercial Open Science, artificial intelligence, appreciation of science, scientific dissemination, among others. Addressing new challenges in an ethical and transparent manner will allow future advances, maintaining CSP's credibility among authors and readers and its commitment to improvements in the living and health conditions of populations.

Do Private Transfers and Foreign Direct Investment Crowd out Official Transfers in Ghana?

The study analyzes the relationship between private transfers, foreign direct investment (FDI) and official transfers in Ghana. The research relies on the quarterly frequency time series data from the Ghana Statistical Service for the period 2003-2022. Phillips-Perron (PP) test and the Engle-Granger approach were used to investigate the properties of the data concerning unit roots and cointegration respectively. The ordinary least squares (OLS) regression technique and Granger causality test are used as methods of analysis. The research findings have shown that when private transfers and foreign direct investment increase, they are more likely to reduce official transfers. Furthermore, the study has confirmed that FDI and private transfer granger cause official transfers. So, the result appears to support the hypothesis that private transfers and foreign direct investment crowd out official transfers in Ghana. To boost commerce and interaction between Ghanaians and the outside world, stable and environmentally friendly conditions must be established.

A machine learning approach to construct quarterly data on intangible investment for Eurozone

We develop a novel approach to construct quarterly time series data for annually measured intangible investment variables. We accomplish this by using machine learning methods to explore the relationship between these variables and key macroeconomic time series available on a quarterly frequency. The proposed approach offers some advantages over other econometric techniques. Specifically, it does not require any ex-ante assumptions for the link between the quarterly time series and their annual counterpart, while minimizing the need for computationally expensive algorithms and necessitating almost no data pre-processing. To demonstrate the usefulness of the constructed data, we present some business cycles facts for the intangible economies of Eurozone and estimate a dynamic factor model.

Multifrequency on-off modulation and slow light characterization of the patterned black phosphorus metamaterial based on dual plasmon-induced transparency.

We present a monolayer patterned black phosphorus (BP) metamaterial for generating a tunable dual plasmon-induced transparency (PIT). We have derived the expression for the theoretical transmittance by introducing the coupled mode theory (CMT), and the calculated results of the expression highly overlap with the simulation results. The quarterly frequency synchronous switch with two different operating bands is designed by the carrier density and scattering rate on the dual PIT modulation effect. Two parameters were selected as important markers to show the performance of the optical switch: the modulation depth (MD) and the insertion loss (IL). The theoretical analysis of this structure shows that the higher modulation depth (5.45d B<M D<12.06d B) and lower insertion loss (0.60d B<I L<0.22d B) of these switches are of good application. In addition, we found the slow light properties of the structure were excellent with a group index of up to 219. This work provides a theoretical basis to prepare multifrequency optical switch and optical buffer devices.

From Quarterly to Monthly Turnover Figures Using Nowcasting Methods

Abstract Short-term business statistics at Statistics Netherlands are largely based on Value Added Tax (VAT) administrations. Companies may decide to file their tax return on a monthly, quarterly, or annual basis. Most companies file their tax return quarterly. So far, these VAT based short-term business statistics are published with a quarterly frequency as well. In this article we compare different methods to compile monthly figures, even though a major part of these data is observed quarterly. The methods considered to produce a monthly indicator must address two issues. The first issue is to combine a high- and low-frequency series into a single high-frequency series, while both series measure the same phenomenon of the target population. The appropriate method that is designed for this purpose is usually referred to as “benchmarking”. The second issue is a missing data problem, because the first and second month of a quarter are published before the corresponding quarterly data is available. A “nowcast” method can be used to estimate these months. The literature on mixed frequency models provides solutions for both problems, sometimes by dealing with them simultaneously. In this article we combine different benchmarking and nowcasting models and evaluate combinations. Our evaluation distinguishes between relatively stable periods and periods during and after a crisis because different approaches might be optimal under these two conditions. We find that during stable periods the so-called Bridge models perform slightly better than the alternatives considered. Until about fifteen months after a crisis, the models that rely heavier on historic patterns such as the Bridge, MIDAS and structural time series models are outperformed by more straightforward (S)ARIMA approaches.

The Disaggregated Impact of Financial Development on the Effectiveness of Monetary Policy in Nigeria

The fast development in the financial system has attracted researchers’ interest in studying its implication on the economy, though empirical evidence is highly limited on disaggregated levels. This study is undertaken to examine the impact of disaggregated financial development on the effectiveness of monetary policy in Nigeria. The study used Autoregressive Distributed Lag Model to capture the data-generating process as well as both short-run and long-run relationships. The scope of analysis ranged from 2000 quarter 1 to 2021 quarter 4 to circumvent the effect of regime changes, as the chosen time horizon represents the period of the uninterrupted civilian regime in Nigeria. The data are sourced from the Central Bank of Nigeria’s and the International Monetary Fund’s statistical databases. Moreso, quarterly frequency data are used to reflect the short-run nature of the monetary policy. The finding reveals financial market development enhances the effectiveness of monetary policy in terms of achieving both its primary and secondary objectives while financial institutions development does not. Given the findings, it is recommended that the Government together with the Central Bank of Nigeria should design policies that would enhance the efficiency of the financial market, particularly markets infrastructure and technology-based products to reduce information asymmetry and transactional costs to ease the way of doing business.

The impacts of globalization, renewable energy, and agriculture on CO2 emissions in India: Contextual evidence using a novel composite carbon emission-related atmospheric quality index

At the 26th Conference of Parties in Glasgow, India declared the environmental agendas of turning carbon neutral by 2070 and substantially lowering its carbon intensity level. Thus, it can be assumed that India is committed to improving its atmospheric health over the next couple of decades. Thus, strategizing plans for limiting carbon emission-related environmental pollution has gone on to become a critically important objective of the Indian government. Against this backdrop, this current study aims to examine the factors influencing atmospheric quality in India. As opposed to the previously adopted approaches in the existing studies, a composite atmospheric quality index is estimated using data concerning different carbon dioxide emission-related atmospheric quality indicators (total carbon dioxide emissions, carbon footprints, and carbon intensity levels). Furthermore, the analysis of the possible non-linear association between renewable energy use and atmospheric pollution makes an additional contribution to the environmental literature. Using quarterly frequency data from 1990q1 to 2018q4 and employing advanced econometric techniques suitable for handling structural break issues, the empirical results reveal that globalization through economic and social channels, agricultural output expansion, and greater population density trigger atmospheric pollution in the long run. Besides, the relationship between renewable energy consumption and atmospheric quality is found to exhibit an inverted U-shape. In this regard, the threshold share of renewable energy in the total final energy consumption level of India is predicted at around 45.75% which is comparatively higher than India’s current renewable energy consumption share. Furthermore, urbanization is evidenced to stimulate the deterioration of India's atmospheric quality, both in the short- and long-run. In line with these major findings, some policy-oriented recommendations are made.

The ENSO cycle and forecastability of global inflation and output growth: Evidence from standard and mixed‐frequency multivariate singular spectrum analyses

AbstractIn this paper, the role of the El Niño‐Southern Oscillation (ENSO), measured by the Equatorial Southern Oscillation Index (EQSOI), is used to formally forecast the inflation and GDP growth rates of the United States, advanced (excluding the United States) and emerging countries, as well as the world economy (barring the United States). We rely on univariate and multivariate singular spectrum analyses (SSA), as well as mixed‐frequency version of the latter since the EQSOI is monthly, while GDP is available only at quarterly frequency unlike monthly inflation rates. We find statistically significant evidence of the ability of the EQSOI in forecasting inflation and GDP growth rates of the four economic blocs, though there are exceptions in terms of forecasting gains associated with inflation rate of emerging economies and the growth rate of the United States. Our results have important implications for policymakers.

Causality analysis of the impacts of petroleum use, economic growth, and technological innovation on carbon emissions in Bangladesh

Bangladesh has traditionally relied on fossil fuels for meeting its energy demand whereby this South Asian nation has not been able to safeguard its environment from greenhouse gas emission-related adversities. Moreover, by ratifying several international environmental agreements, especially the Paris Accord and the Sustainable Development Goals, the government of Bangladesh has expressed its solidarity in abating greenhouse gas emissions through the deployment of relevant environmental policies. Hence, this study assesses the impacts of petroleum consumption, economic growth, and technological innovation on carbon emissions in Bangladesh using quarterly frequency data from 1972Q1 to 2020Q4. Overall, apart from confirming the cointegrating relationships among the variables, the regression findings reveal that higher petroleum consumption and economic growth stimulate environmental degradation by boosting carbon dioxide emissions while technological innovation reinstates environmental well-being by curbing the emission figures. Additionally, technological innovation is seen to moderate the relationship between petroleum consumption and carbon emissions by jointly reducing the emission with petroleum consumption. Lastly, the causality analysis shows that petroleum consumption, economic growth, and technological innovation causally influence carbon emissions. Based on these key findings, it is recommended that Bangladesh mitigates its petroleum dependency, blends environmental objectives into economic growth policies, and develops its technological stock.

MACRO FACTORS IN THE RETURNS ON CRYPTOCURRENCIES

This study investigates the relationship between expected returns on cryptocurrencies and macroeconomic fundamentals. Investors employ a lot of macroeconomic indicators for their investment decision, and hence adopting a few macroeconomic indicators is not sufficient in capturing a change in economic states. Moreover, due to aggregation, macroeconomic indicators are not measured precisely. To overcome these problems, we employ a dynamic factor model and extract common factors from a large number of macroeconomic indicators. We find that the common factors are strongly linked to the cryptocurrency expected returns at a quarterly frequency, while we do not observe this relationship using macroeconomic indicators such as inflation and money supply. This suggests that macroeconomic information matters in a longer term, which contrasts with the previous literature that explores a short-term relationship. The cryptocurrency prices are not determined by macroeconomic fundamentals in a short-term period since speculators impact the prices. However, in a long-term period, the prices are more linked to macroeconomic fundamentals.

The effect of marketing and R&amp;D expenditures on firm profitability and stock return: Evidence from BIST

This study aims to determine the effects of R&amp;D and marketing expenditures of companies that force marketing and finance to act together on stock return, return on assets, and return on equity. To this end, the quarterly frequency data of nine companies that were continuously traded in the BIST Technology Index between March 2009 and December 2020 were examined with panel-data analysis. In line with the purpose of the research, analyzes were carried out in three different models. First of all, we determined which tests should be performed on the models based on the cross-sectional dependence, homogeneity/heterogeneity, and panel unit root test results obtained for the established models. The results of panel least squares test carried out to determine the effect of R&amp;D and marketing expenditures on stock return showed that the effect of R&amp;D expenditures on stock return was not statistically significant while marketing expenditures had a positive and significant effect on stock return. Analyzes should be continued with cointegration tests according to the characteristics of the two models established to determine the effect of R&amp;D and marketing expenditures on return on assets and return on equity. The results implied a positive and significant relationship between R&amp;D expenditures and return on both assets and equity. While no statistically significant relationship was found between marketing expenditures and return on assets, there was a positive and significant relationship between marketing expenditures and return on equity.