Journal articles on the topic 'Inflation (Finance) Australia Econometric models'

This paper assesses the presence and importance of the neo-Fisher effect in postwar data. It formulates and estimates an empirical and a New Keynesian model driven by stationary and nonstationary monetary and real shocks. In accordance with conventional wisdom, temporary increases in the nominal interest rate are estimated to cause decreases in inflation and output. The main finding of the paper is that permanent monetary shocks that increase the nominal interest rate and inflation in the long run cause increases in interest rates, inflation, and output in the short run and explain about 45 percent of inflation changes. (JEL E12, E23, E31, E43, E52)

AbstractOil price showed sharp fluctuations in recent years which revived the interest in its effect on inflation. In this paper, we discuss the relationship between oil price and inflation in Spain, at national and regional levels, and making the distinction between energy and non-energy inflation. To this end, we fit econometric models to measure the effect of oil price shocks on inflation and to predict them under different scenarios. Our results show that almost half of the volatility of changes in total inflation is explained by changes in oil price. As could be expected, the energy component of inflation drives this effect. We also find that, under the most likely scenarios, 1-year ahead total inflation will be moderate, with relevant differences across regions.

The paper estimates the trajectory of trend inflation for Russia. A discussion of inflation measures used in macroeconomic models is presented, as well as the analysis of theoretical and empirical models that include a trend inflation indicator for monetary policy analysis. The paper also provides an overview of the use of trend inflation by monetary authorities of developed countries and emerging markets. Based on the methodology for assessing trend inflation with the help of models of unobservable components, the Kalman filter and including structural factors, the trajectory of trend inflation for the Russian economy from the beginning of 2001 to the end of 2021 is obtained. In addition, the paper provides a historical decomposition of cyclical inflation into structural shocks, as well as the decomposition of the variance of its forecast error into the considered shocks for 24 months in advance. The results of the econometric analysis testify in favor of a gradual decrease in the trend inflation rate to 4% per year after the 2015 crisis and further stabilization at this level. The situation in 2022 can be analyzed by analogy with the period of 2014—2015, one can expect a similar scenario for trend and actual inflation to return to pre-crisis levels in 2022 within one year (by March 2023).

Abstract Econometric modeling and exponential smoothing techniques are two quantitative forecasting methods with good results in practice, but the objective of the research was to find out which of the two techniques are better for short run predictions. Therefore, for inflation, unemployment and interest rate in the Czech Republic various accuracy indicators were calculated for the predictions based on these methods. Short run forecasts on a horizon of 3 months were made for December 2011-February 2012, the econometric models being updated. For the Czech Republic, the exponential smoothing techniques provided more accurate forecasts than the econometric models (VAR(2) models, ARMA procedure and models with lagged variables). One explication for the better performance of smoothing techniques would be that in the chosen countries the short run predictions were more influenced by the recent evolution of the indicators.

Purpose This study aims to address the issue of prediction of inflation differences for an economy that considers either fixing its exchange rate or joining a currency union. In this setting, individual countries have limited control over their inflation, and anticipating the possible course of domestic inflation relative to inflation abroad becomes an important input in policy-making. In this context, the author compares simple forecast heuristics and econometric modeling. Design/methodology/approach The study compares two basically different approaches. The first approach of forecasting consists of simple heuristics. Various heuristics are considered that differ with respect to the economic reasoning that goes into quantifying the forecast rules. The simplest such forecasting heuristic suggests that the average over all available observations of inflation differentials should be taken as a predictor for the future. Bringing more economic insight to bear suggests a further heuristic according to which historical inflation differentials should be adjusted for changes in the nominal exchange rate. A further variant of this approach suggests that a forecast should exclusively rely on data from earlier times under a pegged exchange rate. A fundamentally different approach to prediction builds on dynamic econometric models estimated by using all available historical data independent of the currency regime. Findings The author studies three small member countries of the Eurozone, i.e. Finland, Luxembourg and Portugal. For the evaluation of the various forecasting strategies, he performs out-of-sample predictions over a horizon of five years. The comparison of the four different forecasting strategies documents that the variant of the forecast heuristic that draws on data from earlier experiences under fixed exchange rates performs better than the forecast based on the estimated econometric model. Practical implications The findings of this study provide helpful guidelines for countries considering either joining a currency union or fixing their exchange rate. The author shows that a simple forecasting heuristic gives sound advice for assessing the likely course of inflation. Originality/value This study describes how economic theory can guide the selection of historical data for assessing likely future developments. The analysis shows that using a simple heuristic based on historical analogy can lead to better forecasts than the analytically more sophisticated approach of econometric modeling.

The debt neutrality hypothesis which has been a source of major controversies in the theory of public finance, and macroeconomics has at the same time generated a vast literature on the implications of budgetary deficits and public debt on various subsectors/ variables of the economy, such as inflation, interest rates, current account deficit, etc. Tax discounting has been one of the fields of research associated with debt neutrality. The econometric estimation of some of the standard models of taxdiscounting has shown that consumer response to fiscal policy in Pakistan reflects neither the extreme Barro-like rational anticipation of future tax liabilities nor the Buchanan-type extreme fiscal myopia. It broadly follows a middle path between these extremes. The controversy relating to debt neutrality is quite old in economic theory. However, due to its serious and far-reaching implications for the formulation of fiscal policy and macroeconomic management, the issues of debt neutrality have assumed a foremost position in economic theoretisation and empirical testing. This controversy is based on two important questions: (a) Who bears the burden of the debt? (b) Should debt be used to finance public expenditure? The first question centres on whether the debt can be shifted forward in time, while the second question explores whether taxation is equivalent to debt in its effects on the national economy.

Count data, including zero counts arise in a wide variety of application, hence models for counts have become widely popular in many fields. In the statistics field, one may define the count data as that type of observation which takes only the non-negative integers value. Sometimes researchers may Counts more zeros than the expected. Excess zero can be defined as Zero-Inflation. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the zero-inflated Poisson (ZIP) model and the negative binomial model. In this paper, the models, Poisson, Negative Binomial, ZIP, and ZINB were been used to analyze rainfall data.

This study aims at investigating the impact of Covid-19 pandemic and Global Financial Crisis (GFC) on the performance of selected listed firms in Jordan. To achieve this objective, the study uses panel data for twenty firms over the period 2001-2020, obtained from Amman Stock Exchange database. All the study variables were found to be stationary at level, therefore, fixed and random effect models were applied to estimate two econometric equations with two performance indices. The results have revealed that both inflation and GFC have insignificant effect on the profitability of these firms, while debt ratio has a significant negative impact on their performance. The outcomes have also demonstrated the significant negative impact of Covid-19 pandemic on the profitability of such firms. Based on these results, the study has introduced some recommendations that may help in mitigating the adverse consequences of Covid-19 pandemic and in improving the profitability of Jordanian firms.

Purpose The purpose of this paper is to estimate the Tanzanian shadow economy (SE) from 2003 to 2015 and test the statistical relationships between the SE and its potential causes and indicators. Design/methodology/approach The econometric analysis is based on a multiple indicators multiple causes (MIMIC) model. To calibrate the SE from the estimates, the authors adopt the value of 55.4 percentage of the SE to official GDP from the literature for the base year 2005. Findings The SE ranges from 52 to 61 per cent of official GDP and slightly decreases from 2013 to 2015. Increase in inflation, unemployment and government spending were the main drivers of the SE dynamics. Research limitations/implications Given the challenges facing estimation of the SE (e.g. small sample size, exogenous estimate to calibrate the model, meaning of the latent variable), quantification of SE should be considered to be rough measures. Practical implications To lower the size of the SE, the government needs to keep inflation and unemployment stable over time, to reduce government spending because it creates pressure on tax collection due to the limited tax base. Originality/value This is the first study specifically focused on Tanzanian SE based on the MIMIC approach. Existing estimates of Tanzanian SE are calculated by monetary models or apply a common MIMIC specification to the worldwide context.

The aim of this paper is to evaluate the importance of housing price. We compare the evolution of three different types of housing prices (list, sale and appraisal prices). The objective is to see the marketing and financial consequences of using each type of housing price. To do this, a dataset of a real estate company and its financial intermediary with all of these types of housing prices is used. We estimate econometric models in which the dependent variables are: price (appraisal, selling or list), mark-up, loan to value and foreclosures. The results show evidence of the consequences of using a specific housing price in terms of inflation calculation, financial assets, and collateral valuation and mortgage default, among others.

This paper draws an empirical reassessment of the finance-growth nexus by performing a panel data econometric analysis for all 28 European Union countries over 27 years from 1990 to 2016. Since the mid-1980s, the financial system has experienced a strong liberalisation and deregulation by preventing its beneficial effects on the real economy. This phenomenon, typically called financialisation, points to a negative view of finance and contradicts the well-entrenched hypothesis on the finance-growth nexus. We estimate both linear and non-linear growth models by incorporating seven proxies of finance (money supply, domestic credit, financial value added, short-term interest rate, long-term interest rate, stock market volume traded and stock market capitalisation) and five control variables (the lagged growth rate of the real per capita gross domestic product, the inflation rate, the general government consumption, the degree of trade openness and the education level of the population). Our results show that finance has impaired economic growth in the EU countries, both in the precrisis period and in the crisis and post-crisis periods. The enormous growth of domestic credit and of the financial value added have been restraining the economic growth of the EU countries since 1990 and particularly up until the Great Recession. This implies the need to reduce the prominence of finance, i.e. socalled definancialisation, in the coming years in order to avoid the potential new ?secular stagnation? in the current age of financialisation.

The article discusses the key factors of the ruble exchange rate dynamics, analyzes the features of Russian currency market in the context of inflation targeting and the application of the budget rule. The basic theoretical approaches to modeling the dynamics of real and nominal exchange rates are presented, including behavioral models of the exchange rate, the monetary model of the exchange rate, and the hypothesis of uncovered interest parity. The most important factors of long-term and short-term dynamics of the exchange rate are revealed. The results of an econometric evaluation of the models of the real and nominal ruble exchange rates using dynamic least squares method (DOLS) are presented. It is shown that the key factors shaping the dynamics of the nominal ruble exchange rate are the terms of trade, the interest rate spread, the VIX volatility index, and the operations of the Russian Ministry of Finance under the budget rule. The long-term trajectory of the real exchange rate is formed by the terms of trade conditions, the Balassa—Samuelson effect, the dynamics of net foreign assets of the private sector.

PurposeThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. The purpose of this paper is to examine the long-run relationship between house prices, housing supply and demand, and to estimate the effects of the two types of demand (i.e. owner-occupier and investor) on house prices.Design/methodology/approachThe econometric techniques for cointegration with vector error correction models are used to specify the proposed models, where the housing markets in the Australian states and territories illustrate the models.FindingsThe results highlight the regional long-run equilibrium and associated patterns in house prices, the level of new housing supply, owner-occupier demand for housing and investor demand for housing. Different types of markets were identified.Practical implicationsThe findings suggest that policies that depress the investment demand can effectively prevent the housing bubble from further building up in the Australian states. The empirical findings shed light in the strategy of maintaining levels of housing affordability in regions where owner-occupiers have been priced out of the housing market.Originality/valueThere has been declining home ownership and increased acceptance of long-term renting in many western countries including Australia; this has created a problem when examining housing markets as there are dual demand and include both owner-occupiers and investors. This research has given to the relationship between supply and dual demand, which includes owner-occupation and investment, for housing and the influence on house prices.

28 Counts data models cope with the response variable counts, where the number of times that a certain event occurs in a fixed point is called count data, its observations consists of non-negative integers values {0,1,2,…}. Because of the nature of count data, the response variables are usually considered doing not follow normal distribution. Therefore, linear regression is not an appropriate method to analysis count data due to the skewed distribution. Hence, using linear regression model to analysis count data is likely to bias the results, under these limitations, Poisson regression model and “Negative binomial regression” are likely the appropriate models to analysis count data. Sometimes researchers may Counts more zeros than the expected. Count data with many Zeros leads to a concept called “Zero-inflation”. Data with abundant zeros are especially popular in health, marketing, finance, econometric, ecology, statistics quality control, geographical, and environmental fields when counting the occurrence of certain behavioral and natural events, such as frequency of alcohol use, take drugs, number of cigarettes smoked, the occurrence of earthquakes, rainfall, and etc. Some models have been used to analyzing count data such as the “zero- altered Poisson” (ZAP) model and the “negative binomial” model. In this paper, the models, Poisson, Negative Binomial, ZAP, and ZANB were been used to analyze rainfall data.

The economic global and regional crises have been occurring with greater frequency and duration, leading to economic policy makers see the need to finance budget deficits not only through internal and external financing, but through the issuance primary money, putting at risk inflation. In the specific case of the Pacific Alliance, the group's main objective is to stimulate growth and economic development of the countries of the region. In this regard and considering that the four countries of the Pacific Alliance have significant fiscal deficits, it is pertinent to establish whether inflation is affected by the ways in which governments are funding these budget deficits. The methodology used was the estimation of an econometric model with standardized variables for each of the four countries and finally a pooled data model was estimated. It can be concluded according to the results of the models estimated that the ways to finance the fiscal deficit in Chile, Colombia, Mexico and Peru have no effect on inflation levels.ResumenLas crisis económicas a nivel mundial y regional se han venido presentando con mayor frecuencia y duración, conduciendo a que los hacedores de política económica vean la necesidad de financiar los déficits presupuestarios no solo a través del financiamiento interno y externo, sino a través de la emisión primaria de dinero, colocando en riesgo la inflación. En el caso específico de la Alianza del Pacífico, este grupo tiene como objetivo principal estimular el crecimiento y desarrollo económico de los países que la integran. En este sentido y teniendo en cuenta que los cuatro países que integran la Alianza del Pacífico presentan déficits fiscales significativos, es pertinente establecer si la inflación se ve afectada por las formas en que los gobiernos están financiando dichos déficits presupuestarios. La metodología utilizada fue la estimación de un modelo econométrico con variables estandarizadas para cada uno de los cuatro países y finalmente se estimó un modelo de datos agrupados. Se puede concluir de acuerdo a los resultados de los modelos estimados que la formas de financiar el déficit fiscal en Chile, Colombia, México y Perú no tienen efectos en los niveles inflacionarios.ResumoAs crises econômicas globais e regionais estão ocorrendo com maior frequência e duração, levando os decisores políticos econômicos a considerar a necessidade de financiar déficits orçamentários, não só através de financiamento interno e externo, mas através da emissão de dinheiro primário, colocando em risco a inflação. No caso específico da Aliança do Pacífico, o principal objetivo do grupo é estimular o crescimento e o desenvolvimento econômico dos países da região. A este respeito e considerando que os quatro países da Aliança do Pacífico têm déficits fiscais significativos, é pertinente estabelecer se a inflação é afetada pela forma como os governos estão financiando esses déficits orçamentários. A metodologia utilizada foi a estimativa de um modelo econométrico com variáveis padronizadas para cada um dos quatro países e, finalmente, um modelo de dados agrupados foi estimado. Pode ser concluído de acordo com os resultados dos modelos estimados de que as formas de financiar o déficit fiscal no Chile, na Colômbia, no México e no Peru não têm efeito sobre os níveis de inflação.

The research aims to examine the unexpected changes in stock prices due to external shocks given to the macroeconomic variables to forecast future stock market returns. The study applies two econometric models such as «Variance Decomposition» (VDC) and «Impulse Response Function» (IRF) for examining the exogenous shocks in macroeconomic variables respond to changes in stock prices. Monthly time series data of five significant macroeconomic variables Real Exchange Rate, Interest Rate, Consumer Price Index (CPI), Crude Oil Prices, and Trade Openness, taken as independent variables and BSE SENSEX as a dependent variable. The research period is from Jan 2009 to Dec 2019. The study has taken the responsibility to reveal a few strong evidences for changes in stock prices due to exogenous shocks in Exchange Rate, Trade Openness, Inflation, and Interest rate along with crude oil prices. According to the results, changes in the stock market are due to external factors like changes in dividend policy or capital loss, and some changes in the stock market are due to its own innovative shocks. This study suggests to reduce unexpected changes in stock prices frequently, companies should control capital loss and focus on stable return/dividend policies. There are divergent views in the literature review in the context of measures of these variables, however no research has been done on exogenous shocks in macroeconomic variables to BSE SENSEX for the Indian stock market with this particular data set and duration.

Purpose – The purpose of this paper is to identify whether any financial integration exists among ASEAN+5 members and some East Asian countries, including China, Japan, Korea, Hong Kong, and Taiwan, through interest rate, exchange rate, level of prices, and real output. Design/methodology/approach – Therefore, the authors intend to identify any long-term relationship among these variables utilizing the data in the most efficient manner via panel cointegration and panel unit root tests. The study likewise uses a panel-based vector error correction (panel-vec) model for comparison and also short-run relationship analysis. The long-run relationship is estimated using dynamic ordinary least square technique and a panel multi-layer perceptron (MLP) neural network. Findings – For the ten countries under consideration, the empirical result supports the long-run equilibrium relationship among real output, exchange rate, interest rate, and level of prices, and that the cointegration relationship implies unidirectional causality from exchange rate to real output. This result is favorable to a model that contains real output as a dependent variable and exchange rate, interest rate, and level of prices as explanatory variables. Panel-vec results indicate no evidence of short-run causality from exchange rate to real output. Furthermore, the comparison result of long-run equation estimation shows the superiority of neural networks over econometric models. Originality/value – This paper adds to the literature by examining the financial cointegration using a panel model that contains real exchange rate, interest rate, real output, and inflation rate in ASEAN+5. Additionally this paper applied the MLP neural network to yield a robust estimation of the long-run equation obtained among the variables.

Purpose This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms. Design/methodology/approach The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity. Findings The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC. Originality/value The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.

Foreign Direct Investment (FDI) can bring in much needed capital, particularly to developing countries, help improve manufacturing and trade sectors, bring in more efficient technologies, increase local production and exports, create jobs and develop local skills, and bring about improvements in infrastructure and overall be a contributor to sustainable economic growth. With all these desirable features, it becomes relevant to ascertain the factors which attract FDI to an economy or a group of adjacent economies. This paper explores the determinants of FDI in ten sub-Saharan economies: Liberia, Sierra Leone, Ivory Coast, Ghana, Nigeria, Mali, Mauritania, Niger, Cameroun, and Senegal. After an extensive literature review of theories and empirical research, using a set of cross-sectional data over the period 1990–2017, two econometric models are estimated with FDI/GDP (the ratio of Foreign Direct Investment to Gross Domestic Product) as the dependent variable, and with inflation, exchange rate changes, openness, economy size (GDP), income levels (GNI/capita (Gross National Income) per capita), and infrastructure as the independent variables. Over the period, higher inflows of FDI in relation to GDP appear to be have been attracted to the markets with better infrastructure, smaller markets, and lower income levels, with higher openness and depreciation in the exchange rate, though the coefficients of the last two variables are not significant. These results show the type of FDI attracted to investments in this region and are evaluated from theoretical and practical viewpoints. FDI is an important source of finance for developing economies. On average, between 2013 and 2017, FDI accounted for 39 percent of external finance for developing economies. Policy guidelines are formulated for the enhancement of FDI inflows and further economic development in this region. Such a study of this region has not been made in the recent past.

Purpose The deterministic role of various macroeconomic fundamentals on the attractiveness of countries to inflow of FDI is well documented in literature. The role of market size, infrastructural development, inflation and exchange rates differential have been supported as determinants of FDI direction. However, no documented study has benefited from diverse measures of institutional adequacy as presented in this study. The paper aims to discuss these issues. Design/methodology/approach The paper adopts various econometric approaches that include descriptive statistics, fixed effects models, LM test of independence, feasible generalised least squares regression and SUR estimations. Findings This study unveils the specific impacts and explanatory power of each of the variables along country lines, and the author compares the results of some emerging markets in Asia, Eastern Europe, and South America to some selected countries in Africa. Using data set from various sources over a period of 44 years in a seemingly unrelated regression environment, this study suggests that poor technological capability, inadequate political system, weak productivity gains are major deterrents to the attractiveness of African countries to inflow of FDI. Research limitations/implications The major limitation of this study revolves around availability of usable data, which compels the researcher to limit the focus and the span of time series. Practical implications The study suggests the need to improve institutional quality in emerging economies, especially countries in Africa in order to enhance their attractiveness to FDI inflow. More importantly, the study found that low capital productivity gains hinder the attractiveness of African emerging markets to FDI inflow. Social implications To alleviate poverty, attraction of FDI is considered important, and the improvement of institutional functionality in that regard is found to be important. The need to augment technological improvement is considered very important and critical. Originality/value This serves to confirm that the article entitled “The Machination of Foreign Direct Investment Flow to Emerging Markets – A focus on Africa” is my own original work, envisaged to contribute to the debate about the role of macroeconomic fundamentals, especially capital productivity gains as determinants of a country’s attractiveness to inflow of foreign capital in academic literature. All the sources used and consulted have been fully acknowledged by a way of complete referencing. The author hereby agrees to the terms and conditions as stipulated by the publisher and the editorial board of this prestigious journal.

Not so long ago, defining green energy was generally straightforward: renewables. It may not have been quite that simple, but the development of agreed-upon definitions based on science has become much more complex and contentious, even within the past year. It’s not just a highbrow debate about semantics. The standardization of criteria or a widely accepted taxonomy is critical as the focus increases on not only greenwashing, but on the actual processes and technologies enabling what were thought of as at least “greener” energy. The hammering out of definitions is needed to keep the energy transition moving forward globally. This scrutiny affects the options for companies seeking alternatives in carbon markets where the price of permits for emitting a tonne of CO2 is escalating. In early February, the price of CO2 permits in the EU reached a record high above 96 Euros ($109)/tonne CO2. Reuters reported that the carbon price has risen more than 200% since the start of 2021, partly due to high natural gas prices and the switch made to coal by some power generators. This resulted in higher emissions and increased the demand for permits. In January, the EU Platform on Sustainable Finance, comprising members from utilities, banks, nongovernmental organizations, and corporations, rejected the EU Commission’s draft sustainable finance rules which proposed labeling nuclear power and natural gas as green transition fuels. Nuclear projects permitted until 2045 were to be classified as green, but only if countries can safely dispose of the radioactive waste. Gas was to be included until 2030 with emissions thresholds specified. The EU Platform concluded that even if a gas plant stays under the emissions threshold, it “is not green at any point in its life.” Nuclear energy was acknowledged as already being part of the transitioning energy system and having near to zero greenhouse-gas emissions, but it would not meet the taxonomy’s requirement to “do not significant harm” to the environment because of the toxic waste that cannot be recycled or reused. The EU Commission’s taxonomy will be sent to the European Parliament and Council for review. Blue hydrogen was questioned as a transition fuel by a peer-reviewed study published in August 2021 in Energy Science & Engineering by coauthors from Cornell and Stanford universities. They wrote, “Far from being low-carbon, greenhouse-gas emissions from the production of blue hydrogen are quite high, particularly due to the release of fugitive methane. … Perhaps surprisingly, the greenhouse-gas footprint of blue hydrogen is more than 20% greater than burning natural gas or coal for heat and some 60% greater than burning diesel oil for heat, again with our default assumptions.” They added, “Our analysis assumes that captured carbon dioxide can be stored indefinitely, an optimistic and unproven assumption. Even if true though, the use of blue hydrogen appears difficult to justify on climate grounds.” In a study published last month in the Proceedings of the National Academy of Sciences, researchers at the University of Wisconsin-Madison combined econometric analyses, land use observations, and biophysical models to estimate the realized effects of the US Environmental Protection Agency’s Renewable Fuel Standard (RFS) mandate to partially replace petroleum-based fuels with biofuels. They found that the RFS increased corn prices by 30% and the prices of other crops by 20%, which, in turn, expanded US corn cultivation by 8.7% and total cropland by 2.4% in the years following the policy’s enactment (2008 to 2016). “These changes increased annual nationwide fertilizer use by 3 to 8%, increased water-quality degradants by 3 to 5%, and caused enough domestic land use change emissions such that the carbon intensity of corn ethanol produced under the RFS is no less than gasoline and likely at least 24% higher. These tradeoffs must be weighed alongside the benefits of biofuels as decision makers consider the future of renewable energy policies and the potential for fuels like corn ethanol to meet climate mitigation goals.” The move toward energy transition has been pivotal for our industry and many others. It could be argued that no country, business, or individual will remain unaffected by the changes in progress and yet to come. “Transition” is defined as “the process or a period of changing from one state or condition to another.” And this process will take time, effort, technology, buy-in, scientific study and verification … and consensus, which may be the most challenging piece of all. A significant announcement demonstrating the application and acceptance of a scientific taxonomy was Santos Ltd.’s recent booking of 100 million metric tons of CO2 storage capacity in the Cooper Basin in South Australia. The company believes it represents the industry’s first-ever booking to be made under SPE’s CO2 Storage Resource Management System.

PurposeThis paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.Design/methodology/approachBoth a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.FindingsThe results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.Originality/valueThe focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

Purpose Many studies of corruption and money laundering (ML) have been conducted throughout the previous few decades. The impact of corruption and ML on economic growth, banking performance and corporate financial performance has been the focus of various research. The present study aims to investigate the relationship between ML, corruption and inflation. Design/methodology/approach This study used the panel data of five South Asian countries from 2013 to 2019 (Pakistan, India, Bangladesh, Sri Lanka and Nepal). Further, fixed effect (FE) and random effect (RE) econometric regression models are used to analyze the data. Additionally, generalized methods of moment (GMM) technique is used to check the results robustness. Findings This study discovered that corruption and ML have a significant and positive link with inflation in five South Asian nations using the corruption perception index and the anti-money laundering (AML) index. Practical implications This research advises that government authorities strengthen anti-corruption and AML laws enforcement. Originality/value To the best of the authors’ knowledge, this is the first paper that explains the linkage between corruption, ML and inflation in five south Asian nations.

PurposeThis paper aims to empirically investigate the factors attracting foreign direct investment (FDI) inflows into emerging economies.Design/methodology/approachThis study uses secondary data from the World Bank and the Global State of Democracy Indices of 16 West African countries (WACs) over the period from 1989 to 2018. Fixed- and random-effects econometric regression models are used to assess the nexus between 12 macroeconomic indicators (including political risk and cultural factors) and FDI inflows into WACs.FindingsThe critical drivers of FDI inflows into WACs are the richness of natural resources, market size or gross domestic product (GDP), imports and exports of goods and services, trade openness and the currency's strength as measured by the exchange rate. The result also reveals that French-speaking countries attract more FDI than other English-speaking countries. The previously cited determinants of FDI, such as infrastructural development, inflation, tax and political stability, are insignificant in determining FDI inflows into WACs.Originality/valueThis study uncovers the critical drivers explaining the FDI inflows into WACs, where FDI accounts for 39% of external finance. The study's contribution is that Francophone WACs attract more FDI than Anglophone WACs. The most important drivers of FDI are abundant natural resources, GDP, imports, exports, trade openness and exchange rate.

PurposeFama–French model (FFM) has been successful in helping to predict the financial markets, but investors have been interested in creating more sophisticated models to better predict the performance of the stock market. The objective of the extended version is to create a more robust econometric model to better predict the performance of the Mexican Stock Market.Design/methodology/approachThe study divides the Mexican Stock Market into six different portfolios. The criteria to build those portfolios are the same one used in Fama–French (1992). The study comprises 78 stocks listed in the Mexican Stock Market that are analyzed monthly during 1997–2018. The study analyzes the period before and after the 2008–2009 financial crisis to identify whether there are important changes. The estimation applies the traditional and an extended version of the FFM that include macroeconomic variables such as country risk, economic activity, inflation rate, and exchange rate and some financial variables recommended in the literature.FindingsResults indicate that classic FFM variables are statistically significant in most cases, but relevant macroeconomic variables such as the interest rate, exchange rate and country risk stand out for being weakly relevant in most of the portfolios. However, it is noticed that some of these macroeconomic variables became relevant for different portfolios only after the 2008–2009 crisis, especially in portfolios which include small market capitalization firms.Research limitations/implicationsThe study includes the stocks listed in the Mexican Stock Market. One limitation is the small number of stocks available, which reduces the possibility of creating well diversified portfolios. This study includes 78 stocks. The stocks removed from the sample are from firms that were not listed during six consecutive months or whose market capitalization did not change in the same period. Outlier data were removed from the sample to capture in better way the general performance of the stock market.Practical implicationsThe objective of the extended version is to create a more robust econometric model than the traditional model. It is expected that such estimations can be helpful to investors to make better decisions when they try to predict performance in the stock market.Social implicationsAn extended version of the FFM can be helpful to investors to make better decisions when they try to predict performance in the stock market.Originality/valueTo the best of our knowledge there are no more studies in the literature of the Mexican financial market that apply the same methodology.

Riassunto. – 1) Si considera, in primo luogo, il procedimento di misura di una grandezza in ambito tecnologico nel quale è possibile accertare il valore vero della grandezza a meno di un errore aleatorio trascurabile rispetto a detto valore e quindi acquisire, ad un certo istante, in modo sostanzialmente esatto, lo stato di funzionamento di un processo. Per le grandezze economiche, tipicamente di natura monetaria, si tratta di accertare, per un determinato periodo, l’ammontare totale o quello medio di una popolazione e si devono utilizzare, per la loro stima, il risultato di indagini campionarie. L’errore medio di misura risulta quindi incrementato a causa della variabilità dovuta al campionamento. L’utilizzazione di schemi di campionamento efficienti assicura, però, che l’errore di misura di un totale o di una media aritmetica, rimane in pratica trascurabile rispetto al valore vero che può localizzarsi con sufficiente accuratezza. Viene illustrato questo aspetto con riferimento alla spesa famigliare annua media.2) L’obiettivo diviene diverso se si è interessati non ad accertare una situazione in un determinato periodo di tempo, ma si vogliono delle previsioni per periodi futuri in base ad un modello economico e/o econometrico. In questo caso l’errore di misura e di campionamento può risultare incrementato in modo non direttamente valutabile a causa della non esatta conoscenza del modello interpretativo o anche per l’impossibilità di realizzare gli interventi correttivi sul sistema economico suggeriti dal modello. Un caso tipico viene ravvisato nella previsione della misura dell’inflazione.3) Si osserva che la condizione di equilibrio di una economia di mercato può collegarsi alle scelte dei soggetti economici, cioè, in definitiva, alle scelte di individui che operano in accordo ad un’entità difficilmente definibile in termini generali e pertanto difficilmente “misurabile” indicata, come Utilità. Sorge il quesito, suggerito ad esempio dalla teoria cinetica dei gas, che, come per un gas ha scarso interesse, ad esempio, la velocità di una singola molecola ma lo ha invece il valore medio dell’energia cinetica delle molecole, espresso dalla temperatura, così può avere scarso interesse generale la misura dell’utilità percepita da un singolo consumatore, e si può invece pensare che abbia interesse qualche indicatore globale, che, − argomento da approfondire−, si ritiene possa collegarsi ai prezzi di mercato.4) Di fronte ad una pluralità di alternative soggette ad incertezza, che è una situazione tipica del “decisore” in ambito finanziario e d’impresa, la teoria di von Neumann-Morgenstern-Savage − che è stata soggetta a valutazioni critiche, revisioni e complementi − può essere utile per orientare verso le scelte che massimizzano l’utilità media, rispetto alle distribuzioni di probabilità ipotizzate, per le possibili conseguenze delle decisioni.Nelle ricerche di mercato, poi, la formulazione della nozione di utilità diviene più concreta e le tecniche psicometriche spesso utilizzate, consentono di individuare il “prodotto” più utile. Si è infine ricordato come è disponibile una metodologia statistico-probabilistica per la quantizzazione dei concetti, della quale si deve ancora approfondire l’impiego per la valutazione dell’utilità.***Abstract. − 1) As starting point the measurement procedure of a given quantity – representing a typical problem in the technological area – is considered allowing one to ascertain the true value of the quantity except for a zero mean random error which in practice often may be assumed to have a unessential root mean squared error or repeatability standard deviation. As for the quantities used in economic and social sciences – e. g..the economic quantities are often of monetary nature – with regard to a given finite population of elements, in general of human beings, a typical problem is of establishing the total amount of a chosen quantitative characteristic and /or the arithmetic mean, which have to be estimated on the basis of a sample survey. It follows that, in general, the random component of a single observation has a standard deviation greater than the one caused by repeatability owing to the presence of sampling variability. The use of efficient sample designs and a large sample size, however, ensure that the random errors of the estimates of the total amount and of the corresponding arithmetic mean may become unimportant with respect to their true values located through pertinent confidence intervals. An illustration is presented regarding the yearly mean expenditure of an Italian family.2) The investigation purpose becomes more complex when we not only want to know the real situation in a given span of time but also when we want to forecast future trends according to an economic or an econometric model. In this case the global random error, due to measurement and sampling variability, may be affected by a systematic bias, which is not easy to be evaluated because of the lack of a complete and exact knowledge of the model. As an example the econometric models of inflation is quoted.3) One can note that the equilibrium of a market economic system is connected to people’s preferences and choices according to an abstract entity named Utility, which is difficult to be defined in general terms and thus to be measured. Correspondingly, as in the gas kinetic theory the velocity of a single molecule can have no direct interest while on the contrary it does the mean value of the molecular kinetic energy expressed by temperature, likewise we can say that, in general, the Utility perceived by a single individual has little theoretical interest while a global Utility indicator of goods − which seems naturally linked to the market prices and deserves further investigation − could have a certain interest, e.g. for the aim of comparison4) In the area of finance and enterprise management a decision maker is typically confronted with preference between decisions in the face of uncertainty. In this context the theory of von Neumann - Morgenstern - Savage − which has been the subject of some criticism, revision and completion − may at times appear to be useful. More precisely, assuming a set of possible monetary consequences and some alternative probability distributions on it, the theory directs a decision maker towards the choice which maximizes the expected value or mean Utility within the values corresponding to the alternative probability distributions.In marketing research the notion of Utility assumes a more concrete character and the psychometric techniques, which are often used, allow one to point out the more useful product.. Finally the available statistical – probabilistic theory is mentioned which permits the quantization of abstract notions or concepts and whose appropriateness and use, with respect to economic utility evaluation, have still to be examined attentively.