Measuring financial contagion: a directed acyclic graphs and error correction model

Europe is the most important source of tourism for Morocco.The purpose of this paper is to investigate the short and long-run determinants of air travel demand from European Union to Morocco.In order to analyze the air travel demand, we used the relevant macroeconomic variables, such as the origin countries' income, i.e.Europe and the real exchange rate between the origin and host country.Annual data from the two countries, covering the period 1970-2012, are used.The ADF unit root test was used to examine the degree of variables integration.The Johansen maximum likelihood procedure was used to determine the number of co-integrating vectors in the VAR model.An error correction model was estimated to explain the air travel demand determinants between Morocco and the EU both in short and long-run.The estimated error correction model provides strong evidence that European GDP, real exchange rate fluctuations and regulatory environment are the main factors affecting air travel demand in Morocco.

The non-alcoholic beverage market in the U.S. is a multi-billion dollar industry growing steadily over the past decade. Also, non-alcoholic beverages are among the most heavily advertised food and beverage groups in the United States. Several studies pertaining to non-alcoholic beverages including the incorporation of advertising effects have been conducted, but most of these have centered attention on milk consumption. Some studies have considered demand interrelationships for several beverages including advertising effects in systems-wide analyses. In our analysis, we develop and employ a unique monthly time-series data set derived from Nielsen Homsescan panels for household purchases of non-alcoholic beverages over the period from January 1998 through December 2009. This data set is subjected to the use of vector autoregression (VAR) and polynomial distributed lags (PDL) to examine own-advertising and cross-advertising effects for non-alcoholic beverages. Contemporaneous causal structures among advertising expenditures, quantities and prices of various non-alcoholic beverages also are studied using artificial intelligence approaches such as directed acyclic graphs (DAGs). Once the VAR and PDL (through augmenting the AIDS model) are estimated, we can ascertain own- and cross-advertising effects of various beverages. Impulse response functions gleaned from the use of the VAR approach would show us the impacts of advertising expenditures of various beverages to a one-time-only shock. The direction and the strength of the impulse would speak to the effect of the adverting expenditure of one beverage on others. Error variance decompositions help us determine the magnitude of the contribution of advertising expenditures of several beverages on a given beverage. Similarly, own- and cross-adverting expenditure elasticities generated through the PDL help us determine the negative and positive spillover effects of advertising expenditure of one beverage on several others. The contemporaneous causal structure pertaining to advertising expenditures, quantities and prices of various beverages help identify endogenous, exogenous and/or weakly exogenous factors. Ultimately, the better model to ascertain the effect of advertising expenditures of various non-alcoholic beverages is identified through the study of out-of-sample forecasts generated though both models.

Effects of measurement errors on the analysis of error correction models (ECMs) of vector processes observed with measurement errors were studied in Hong et al. (2016. Analysis of cointegrated models with measurement errors. Journal of Statistical Computation and Simulation. 2016;86:623–639). It was found that statistically undesirable effects on the analysis attributable to endogeneity in the ECM induced by measurement errors, even in their simplest form. Therefore, we first propose a method using instrumental variables (IV) and derive the asymptotic distributions of the reduced rank estimator that eliminate the undesirable effect of endogeneity. Then, we propose a reduced rank maximum likelihood (ML) estimation using a moving-average term to deal with endogeneity. These methods yield estimators that are consistent and asymptotically unbiased. The first method with IV is simple to use computationally and yields good initial estimate for the second ML method. We investigate the effects of the measurement errors on the proposed methods through a Monte Carlo simulation study.

This study aims to examine the impact of cereals production and cross-border trade on food security in Nigeria between 1986 and 2017. The Autoregressive Distributed Lag (ARDL) econometric technique was employed to analyze the time series secondary data sourced from World Bank Development Indicators, 2017 in order to explain the relationship between food security and the cereal production and cross-border trade. The findings of this study reveal that both in short-run and long-run estimation of error correction model (ECM), cereal domestic production, cereal exports and cereal imports have significant impact on the food security. Accordingly, cereal domestic production and cereal imports have a positive sign, which implies that an increase in cereal domestic production and cereal imports influence food security. While, cereal exports have a negative sign, which suggest that Nigeria as a nation is experiencing deficit in cereal production and therefore, the little quantity produced and exported posit a treat to food security. Based on the findings of this study, it is therefore recommended that policy formulation such as government subsidies in the agricultural sector and any other intervention programmes that will encourage an increase in domestic cereal production as well as policy formulation that will not totally discourage cereal importation should be implemented since its impact on food security is positive. This can be achieved through trade liberalization. On one hand, in as much as cereal export have significant impact on food security; government should be mindful that policy formulation on cereal exports should take into consideration the level of domestic cereal production and consequently its negative impact on food security.

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than the KFM. It seems that the ECM outperforms the VAR and the KFM, since the ECM allows for dynamic updating via an error correction term.

Background/Aim: Identifying confounders based on causal directed acyclic graphs (DAGs) is usually based solely on expert knowledge, although epidemiological studies provide observational data. We applied data-based structure learning algorithms for the specific purpose of identifying confounders, investigating the research question whether particulate matter with an aerodynamic diameter ≤2.5 µg/m3 (PM2.5) affects progression of atherosclerosis, measured as yearly change in carotid intima media thickness (ΔcIMT). Methods: We used 5-year follow-up data of the population-based Heinz Nixdorf Recall Study. Due to computational limitations, potentially relevant variables were restricted to continuously measured data and hence included age, BMI, smoking, blood pressure, HbA1c, LDL/HDL, education, income, neighborhood unemployment (nSES) rate, physical activity and alcohol consumption. Prior knowledge was based on causal chain assumptions regarding major causes of ischaemic heart disease1, and translated to prohibited arcs, i.e. given conditions (e.g. age) are unaffected by lifestyle variables (e.g. smoking) and lifestyle variables are unaffected by subclinical medical determinants (e.g. laboratory measures). We applied a revised structure learning algorithm to learn a causal DAG, including the score-based hill-climbing algorithm, a bootstrap-check to validate the conditional independence structure between variables, and a clustering of minimal sufficient adjustment set (MSAS)-equivalent subsets within the Markov equivalence-class. Results: Based on 2279 participants, the revised structure learning resulted in one MSAS-equivalent cluster, including one DAG of 59 unambiguous arcs. The only biasing path from PM2.5 to ΔcIMT emanated from nSES. Suggested adjustment consisted of four confounder-equivalent MSAS ({nSES}; {age, physical activity, blood pressure}; {age, alcohol consumption, BMI, physical activity, smoking}; {age, BMI, education, income, physical activity, smoking}), yielding an identical exposure effect estimate of 0.002 mm (95%-CI: -0.002, 0.007) increase in cIMT per year per 5 µg/m3 increase in PM2.5. Conclusions: Data-based structure learning returned one unambiguous DAG widely matching general believes. Identical exposure effect estimates of confounder-equivalent MSAS underline a stable (causal) structure.

Japanese society faces a low fertility rate and population decline, and the declining population brings about a shortage of labor forces. Therefore, female labor participation is attracting attention. However, cross-sectional data show a positive relationship between the total fertility rate (TFR) and female labor participation in recent years, while the time series data suggest a negative relationship. This may be a puzzle waiting to be solved. This chapter empirically interprets the dynamic relationship between the TFR, female wage, and labor participation from the time series perspective, using Japan’s experience. Generally, the VAR model is used to confirm the dynamic relationship among relevant variables; however, it is not easy to use the VAR model because the variables have a unit root. This study verifies the dynamic relationship using the error correction model (ECM) and Bayesian vector autoregression (BVAR) models. Therefore, both ECM and BVAR models show a similar result. They show that female wages negatively impact the fertility rate, and a similar result was obtained from both ECM and BVAR models. Women’s wages are an opportunity cost of having children, and we reconfirmed this fact in Japan’s experience. Additionally, this means that the compatible situation for women between labor provision and rearing children has been insufficient in Japan. Furthermore, adding the government benefits to young families to the BVAR model shows that this positively affects the TFR by calculating impulse response function. This can be interpreted as the government benefits reducing the opportunity cost of children. Hence, the intervention by the government to improve the TFR is appropriate.

This study estimates the nature of the relationship of entrepreneurship and business confidence on youth unemployment in the Philippines over the 2001-2017 period. The paper employed a range of cointegrating regression models, namely, autoregressive distributed lag (ARDL) bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models, dynamic OLS, fully modified OLS, and canonical cointegrating regression (CCR) estimation techniques. The Granger causality based on error correction model (ECM) was also performed to determine the causal link of entrepreneurship and business confidence on youth unemployment. The ARDL bounds testing approach, Johansen-Juselius (JJ) and Engle-Granger (EG) cointegration models confirmed the existence of long-run equilibrium relationship of entrepreneurship and business confidence on youth unemployment. The long-run coefficients from JJ and dynamic OLS show significant long-run and positive relationship of entrepreneurship and business confidence on youth unemployment. While results of the long-run coefficients from fully modified OLS and canonical cointegrating regression (CCR) found that only entrepreneurship has significant and positive relationship with youth unemployment in the long-run. The Granger causality based on error correction model (ECM) estimates show evidence of long-run causal relationship of entrepreneurship and business confidence on youth unemployment. In the short-run, increases in entrepreneurship and business confidence causes youth unemployment to decrease.

Proof of Concept Example for Use of Simulation to Allow Data Pooling Despite Privacy Restrictions.

The objective of causal discovery is to uncover the causal relationships among natural phenomena or human behaviors, thus establishing the basis for subsequent prediction and inference. Traditional ways to reveal the causal structure between variables, such as interventions or random trials, are often deemed costly or impractical. With the diversity of means of data acquisition and abundant data, approaches that directly learn causal structure from observational data are attracting more interest in academia. Unfortunately, learning a directed acyclic graph (DAG) from samples of multiple variables has been proven to be a challenging problem. Recent proposals leverage the continuous optimization method to discover the structure of the target DAG from observational data using an acyclic constraint function. Although it has an elegant mathematical form, its optimization process is prone to numerical explosion or the disappearance of high-order gradients of constraint conditions, making the convergence conditions unstable and the result deviating from the true structure. To tackle these issues, we first combine the existing work to give a series of algebraic equivalent conditions for a DAG and its corresponding brief proofs, then leverage the properties of the M-matrix to derive a new acyclic constraint function. Based on the utilization of this function and linear structural equation models, we propose a novel causal discovery algorithm called LeCaSiM, which employs continuous optimization. Our algorithm exploits the spectral radius of the adjacency matrix to dynamically adjust the coefficients of the matrix polynomial, effectively solving the above problems. We conduct extensive experiments on both synthetic and real-world datasets, the results show our algorithm outperforms the existing algorithms on both computation time and accuracy under the same settings.

Development and application of an evidence-based directed acyclic graph to evaluate the associations between metal mixtures and cardiometabolic outcomes.

Cointegration, error-correction, and joint efficiency in forward and futures markets for major foreign currencies

Increasing threat of password leakage from compromised password hashes demands a resource consuming password-hashing algorithm to prevent the precomputation of the password hashes. A class of password-hashing schemes (PHS) provides such a defense by making the design Memory hard. This ensures that any reduction in the memory consumed by the algorithm leads to an exponential increase in its runtime. The security offered by a memory-hard PHS design is measured in terms of its time–memory trade-off (TMTO) defense. Another important measure for a good PHS is its efficiency in utilizing all the available memory as quickly as possible, and fast running time when more than the required memory is available. In this work, we present a simple technique to analyze TMTO for a password-hashing scheme which can be represented as a directed acyclic graph (DAG). The nodes of the DAG correspond to the storage required by the algorithm and the edges correspond to the flow of the execution. Our proposed technique provides expected runtimes at varied levels of available storage utilizing the DAG representation of the algorithm. We show the effectiveness of our proposed technique by applying it on three designs from the “Password Hashing Competition" (PHC)—Argon2-Version 1.2.1 (the PHC winner), Catena-Version 3.2 and Rig-Version 2. Our analysis shows that Argon2i is not providing expected memory hardness which is also highlighted in a recent work by Corrigan-Gibbs et al. We analyze these PHS for performance under various settings of time and memory complexities. Our experimental results show (i) simple DAGs for PHS are efficient but not memory hard, (ii) complex DAGs for PHS are memory hard but less efficient, and (iii) combination of two simple graphs in the representation of a DAG for PHS achieves both memory hardness and efficiency.

Mapping the Federal Reserve's Reaction Function with Directed Acyclic Graphs

<p>Remittance flows into low-income and fragile states represent a lifeline that supports households as well as provides much-needed tax revenue (IMF - Sayeh & Ralp Chami; 2021).<strong> Purpose</strong>: on the basis of this record, the study sought to examine the dynamic modelling of the influence of diaspora cash remittance as measured by Personal Remittances Received (PRR); Exchange Rate (EXCHR); and Outward Remittances Flows (ORF) on macroeconomic stability in Nigeria as measured by Unemployment Rate (UNEMPR) and Gross Domestic Product per capita growth (GDPpcgr) using quarterly time-series data sourced from World Bank and World Development Indicators for the periods 2010Q1-2020Q4.<strong> Technique/Approach</strong>: the study was disintegrated to apply Engle-Granger two-step method of error correction model (ECM) together with Granger Causality test to analyse the causal effect of Personal Remittances Received; Exchange Rate; Outward Remittances Flows on Unemployment Rate, as well as, utilize vector autoregressive (VAR) technique to investigate the dynamic influence of Personal Remittances Received; Exchange Rate and Outward Remittances Flows on gross domestic product per capita growth.<strong> Findings</strong>: the result of Granger Causality test upheld the hypothesis of PRR does not involve Granger causality of UNEMPR and the hypothesis that UNEMPR does not involve Granger causality of PRR. However, unidirectional causality runs from EXCHR to UNEMPR; as well as, from ORF to UNEMPR. The result of the error correction model (ECM) demonstrated that personal remittances received and exchange rate have insignificant and significant short-term and long-term effects on increasing the rate of unemployment. Whereas, outward remittances flow significantly minimized the rate of unemployment. More so, the results of the vector autoregressive (VAR) technique portrayed that the past realization of GDPpcgr was associated with a significant increase of 0.84 percent in GDPpcgr. Whilst, the past realization of Personal Remittances Received and outward remittances flows were related to an insignificant increase in GDPpcgr respectively; also, the past realization of exchange rate retarded the GDPpcgr. Furthermore, variance decomposition forecast of 4-period horizon reported that 100 percent of deviation in GDPpcgr is explicated by GDPpcgr in the short-term, indicating that personal remittances received, exchange rate, and outward remittances flows had no strong exogenous influence on predicting GDPpcgr in the future. Whereas, in the long-term forecasted error in GDPpcgr produced about 97.57 percent; while, Personal Remittances Received, exchange rate and outward remittances flow had a weak influence on predicting GDPpcgr in the future.<strong> Concluding Remark</strong>: the study concluded that it is an imperative for policy makers to design more policy measures that will encourage, facilitate a smooth and efficient inflow of cash remittance from abroad into the country so as to stabilize the economy.</p><p> </p><p><strong>JEL: </strong>E01; E20; E24<strong></strong></p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0965/a.php" alt="Hit counter" /></p>