Autoregressive Error Research Articles

External Financial Liberalization and Macroeconomic Performance in Emerging Countries: An Empirical Evaluation of the Brazilian Case

ABSTRACTThis article evaluates the effects of external financial liberalization on Brazilian macroeconomic performance from 1995 to 2016. Its main contributions are to assess the influence of the global financial cycle on the level of external financial liberalization and to analyse the short‐ and long‐run macroeconomic effects of such liberalization on the performance of a peripheral economy in the global currency hierarchy. Methodologically, the article employs the Markov‐Switching Vector Autoregressive and Vector Error Correction models. The results show that the global financial cycle directly affects cross‐border financial flows and frames the impact of external financial liberalization on the macroeconomic performance. The article concludes that external financial liberalization has negative macroeconomic effects in the short run and generates a trade‐off between stability and growth in the long run.

EEG autoregressive modeling analysis: A diagnostic tool for patients with epilepsy without epileptiform discharges

ObjectiveNumerous types of nonepileptic paroxysmal events, such as syncopes and psychogenic nonepileptic seizures, may imitate epileptic seizures and lead to diagnostic difficulty. Such misdiagnoses may lead to inappropriate treatment in patients that can considerably affect their lives. Electroencephalogram (EEG) is a commonly used tool in assisting diagnosis of epilepsy. Although the appearance of epileptiform discharges (EDs) in EEG recordings is specific for epilepsy diagnosis, only 25%–56% of patients with epilepsy show EDs in their first EEG examination. MethodsIn this study, we developed an autoregressive (AR) model prediction error–based EEG classification method to distinguish EEG signals between controls and patients with epilepsy without EDs. Twenty-three patients with generalized epilepsy without EDs in their EEG recordings and 23 age-matched controls were enrolled. Their EEG recordings were classified using AR model prediction error–based EEG features. ResultsAmong different classification methods, XGBoost achieved the highest performance in terms of accuracy and true positive rate. The results showed that the accuracy, area under the curve, true positive rate, and true negative rate were 85.17%, 87.54%, 89.98%, and 81.81%, respectively. ConclusionsOur proposed method can help neurologists in the early diagnosis of epilepsy in patients without EDs and might help in differentiating between nonepileptic paroxysmal events and epilepsy. SignificanceEEG AR model prediction errors could be used as an alternative diagnostic marker of epilepsy.

The Dynamics of Inflation in Ethiopia: Time Series Approach

One of the prime objectives of governments is achieving stable macroeconomic condition. This objective requires that prices be kept to a reasonably stable level. High and persistent inflation introduces uncertainties into the economy and may lead to slowdown of economic growth by discouraging domestic as well as foreign investments. It may also cause balance of payments problems by eroding a country’s competitive advantage. Moreover, because it hits the poor the most it needs to be tackled. This study aims at understanding the forces behind the current inflationary process in Ethiopia. In order to achieve the stated objective a synthesis model of monetarist and cost-push inflation theories is estimated using vector autoregressive (VAR) and single equation error correction models. The estimated models enable to understand the short run and the long run inflation dynamics in Ethiopia between 1980 and 2017.The result shows that in the long run real money supply. Real GDP growth real effective exchange rate and Budget deficit have significantly affect inflation. But budget deficit and real GDP is not found the expected sign rather. The short run the change in real GDP growth and change real money supply significantly affect inflation. However the change real effective exchange rate and budget deficit are insignificant. The study suggests that adopting restrictive monetary and fiscal policy. Have essential tools to curb inflationary problem of Ethiopia. Keywords: inflation, ECM,GDP,VAR DOI: 10.7176/JESD/11-11-01 Publication date: June 30th 2020

Forest canopy height co‐determines taxonomic and functional richness, but not functional dispersion of mammals and birds globally

AbstractAimsTaller forest canopies may harbour higher biodiversity by providing more and varied resources. No previous studies have assessed whether forest canopy height shapes the taxonomic and functional diversity of terrestrial vertebrates at global and regional scales. Here, we examine the roles of forest canopy height and other environmental variables in shaping global and regional patterns of species richness and functional diversity of mammals and birds.LocationGlobal.Time periodPresent day.Major taxa studiedTerrestrial mammals and birds.MethodsGlobal forest canopy height data at 1 km spatial resolution were used to measure forest vertical structure. Species richness, functional richness and functional dispersion of mammals and birds were calculated using range maps and trait data. Spatial simultaneous autoregressive error models were used to evaluate associations between species richness and functional diversity and their predictors, including mean canopy height, standard deviation of canopy height, net primary productivity, current climate and historical climate stability, topography and human activities.ResultsMean canopy height emerged as one of two predictors most associated with species richness of mammals and birds as well as mammal functional richness. However, mean canopy height had little explanatory power for functional dispersion. Mean annual temperature and net primary productivity contributed most to explain global‐scale mammal and bird functional dispersion. At the regional scale, mean canopy height, net primary productivity and mean annual temperature were the variables most associated with the species richness and functional diversity of mammals and birds.Main conclusionsForest canopy height is an important predictor of species richness and functional diversity of terrestrial vertebrates at both global and regional scales, at a similar overall level to productivity and temperature. Our study highlights the crucial role of the complex vertical structure in shaping the global and regional patterns of vertebrate diversity.

Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors

The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN).

External Reserves and Selected Key Macroeconomic Variables in Nigeria: An Empirical Analysis (2000-2018)

The paper determines empirically the interactive influence of external reserves and selected key macroeconomic variables in Nigeria using an autoregressive distributive lag (ARDL) model, cointegration and error correction model anticipated by Pesaran, Shin and Smith (2001) with quarterly data between 2000 and 2018 sourced from Central Bank statistics portal on data warehouse pro platform at https://cbnstatistics.datawarehousepro.com. The paper applied the Augmented Dickey-Fuller (ADF) unit root in testing variables stationarity. The Cumulative sum (CUSUM) as well as the Cumulative sum of square (CUSUMSQ) display some recursive outstanding schemes of the external reserve function that remain within the 5% critical positions, and therefore gave an indication of steady external reserve purpose for Nigeria during the study period. The key variables trade openness that captured the total imports and exports by way of proportion of gross domestic product (GDP), exchange rate, direct investment, portfolio investment, oil price, consumer price index, interest rates have correct signs and the ARDL regression analysis indicates that the descriptive variables elucidate and accounted for 99% disparities in external reserves model. The bounds cointegration test exhibited that the variables are cointegrated. The paper demonstrated several empirical supports for the theoretical implications. Precisely, the log of direct investment, portfolio investment, trade openness and interest rate have positive effect, statistically significant and contributes to the external reserves position in Nigeria on the short- run. Jel. Classification Numbers: F21, F32, F34 DOI: 10.7176/JESD/11-10-08 Publication date: May 31 st 2020

MP52: Effectiveness of an outpatient parenteral antibiotic therapy clinic for adults with non-purulent cellulitis

Introduction: Emergency department (ED) patients with cellulitis that are treated with intravenous (IV) antibiotics may be eligible for outpatient parenteral antibiotic therapy (OPAT). The primary objective of this study was to determine whether the implementation of an OPAT clinic results in decreased hospitalization and return ED visits for patients treated with IV antibiotics. Methods: We conducted a before-after implementation study involving adults (age >=18 years) that presented to two tertiary care EDs with cellulitis and were treated with IV antibiotics. The intervention was referral to an infectious disease physician within one week of the index ED visit at the newly created OPAT clinic. The primary outcomes were hospital admission and return ED visits within 14 days. Secondary outcomes were treatment failure (admission after 48 hours of OPAT) and adverse events (e.g. vomiting, diarrhea). We conducted an interrupted time series analysis from January to December both pre-intervention (2013) and post-intervention (2015), with 24 monthly data points. The year of clinic implementation (2014) was considered a transition period. A segmented non-linear regression autoregressive error model was used to aggregate the monthly data to evaluate the effectiveness of the intervention. Results: A total of 1,666 patients met inclusion criteria: 858 pre-intervention (mean age 59 years, 53.1% male) and 808 post-intervention (mean age 62 years, 54.5% male). Hospitalization rates were not significantly higher one year after clinic implementation (p = 0.53) although there was a non-statistically significant gradual increase of 0.8% per month (95%CI -0.3% to 1.9%). One year after introduction of the OPAT clinic, return ED visits were significantly lower (change in intercept -24.4%, 95%CI -34.2% to -14.6%; p < 0.001), followed by an additional drop of 1.4% per month (95%CI -2.1% to -0.6%; p = 0.002). By the end of the study, return visits were 40.7% lower (95%CI 25.6% to 55.9%) than if the intervention had not been introduced. Treatment failure rates were <2% and adverse events were <5% in both groups. Conclusion: Implementation of an OPAT clinic significantly reduced return ED visits for cellulitis, which is critically important given the current ED overcrowding crisis. There was no significant change in hospital admission rates. There were low rates of treatment failures and adverse events. An OPAT clinic should be considered to reduce ED crowding while maintaining safe patient care.

Nearest neighbor balanced block designs for autoregressive errors

In this paper we study the problem of finding neighbor optimal designs for a general correlation structure. We give universal optimality conditions for nearest-neighbor (NN) balanced block designs when observations on the same block are modeled by an autoregressive AR(m) process with arbitrary order m. The cases \(m=1,2\) have been studied by Grondona and Cressie (Sankhyā Indian J Stat Ser A 55(2):267–284, 1993) for AR(2) and by Gill and Shukla (Biometrika 72(3):539–544, 1985a, Commun Stat Theory Methods 14(9):2181–2197, 1985b) and Kunert (Biometrika 74(4):717–724, 1987) for AR(1); we extend these results to the cases \(m \ge 3\).

Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models

Major players in maritime business such as shipping lines, charterers, shippers, and others rely on container freight rate forecasts for operational decision-making. The absence of a formal forward market in container shipping necessitates reliance on forecasts, also for hedging purposes. To identify better performing forecasting approaches, we compare three models, namely autoregressive integrated moving average (ARIMA), vector autoregressive (VAR) or vector error correction (VEC), and artificial neural network (ANN) models. We examine the China Containerized Freight Index (CCFI) as a collection of weekly freight rates published by the Shanghai Shipping Exchange (SSE) for four major trade routes. We find that, overall, VAR/VEC models outperform ARIMA and ANN in training-sample forecasts, but ARIMA outperforms VAR and ANN taking test-samples. At route level, we observe two exceptions to this. ARIMA performs better for the Far East to Mediterranean route, in the training-sample, and the VEC model does the same in the Far East to US East Coast route in the test-sample. Hence, we advise industry players to use ARIMA for forecasting container freight rates for major trade routes ex-China, except for VEC in the case of the Far East to US East Coast route.

Comparison of nonlinear models for the description of carbon mineralization in soils treated with pig slurry

One of the strategies to reduce environmental impacts caused by pig slurry is its application to soils for agricultural productions. Carbon mineralization curves can be used to determine the best periods for the use of organic matter for an adequate management of soils and growing plants. The objective of this study was to evaluate the fit of nonlinear models for soil carbon mineralization. The experiment was conducted using a randomized block design with four replications and four treatments. The treatments consisted of monthly applications of pig slurry at rates of 0, 7.5, 15.0, and 30.0 m3 ha-1 ofpig slurry. Soil samples were collected and incubated for 26 days; then, seven observations of mineralized carbon volume were made over time. The description of the carbon mineralization followed the Stanford and Smith, Cabrera, and Juma models, considering the structure of autoregressive errors AR (1), when necessary; the fits were compared using the Akaike Information Criterion (AIC). The description of carbon mineralization in the treatments by nonlinear models was, in general, adequate. Juma was the most adequate model to describe the treatment with rate of 0. Stanford and Smith was the most adequate model to describe the treatments with rates of 7.5 and 15.0 m3 ha-1. Cabrera was the most adequate model to describe the treatment with rate of 30.0 m3 ha-1.

Business cycles in emerging markets: the role of liability dollarization and valuation effects

Understanding differences in business cycle phenomena between Emerging Market Economies (EMEs) and industrialized countries has been at the center of recent research on macroeconomic fluctuations. The purpose of this paper is to investigate the importance of certain credit market imperfections in different EMEs. To this end, we develop a small open economy Dynamic Stochastic General Equilibrium (DSGE) framework featuring both permanent and transitory productivity shocks, differentiated home and foreign goods, and endogenous exchange rate movements. Furthermore, our model incorporates liability dollarization as a particular form of financial frictions in EMEs. In this vein, we account for the fact that emerging markets traditionally have had difficulties in borrowing in domestic currency on international capital markets and thus allow for valuation effects in our analysis. We estimate our model using Bayesian techniques for a number of EMEs and thereby control for potential heterogeneity across countries. Contrary to previous studies in this strand of the literature, we include a (vector-)autoregressive measurement error component to capture off-model dynamics. Regarding business cycles in emerging markets, our main findings are that (i) even though we incorporate financial frictions in the framework, trend shocks are the main determinant of macroeconomic fluctuations, (ii) accounting for liability dollarization ameliorates the model fit, and (iii) valuation effects on average stabilize changes in the net foreign asset position.

INFLATION PERSISTENCE IN NORTH SUMATERA PROVINCE

The purpose of this study was to determine the persistence of inflation in major North Sumatera Province in 2007 until 2012 and value contributed Output Growth (GDP) of North Sumatera, Exchange Rate, Interest Rate and Error Correction Term about Inflation in North Sumatera. Data is used secondary data from general Consumer Price Index (CPI) from North Sumatera Province include Medan, Pematangsiantar, Sibolga and Padangsidempuan monthly of January 2007 until December 2012. And secondary data Consumer Price Index (CPI) of North Sumatera Province, Gross Domestic Product of Province Sumatera Utara, Exchange Rate and Interest Rate of BI Rate yearly of 1999 until 2012. Model is used model econometric with Autoregressive method and Error Correction Model. Result of this research with estimation of VAR model concludes that degree of persistence of 4 town from North Sumatera Province is low. Result of estimation of model ECM concludes that Interest Rate significantly affect to inflation rate, while Gross Domestic Product of North Sumatera Province and Exchange Rate not significantly affect to inflation rate. While ECT becomes significant correction to variable inflation rate. Where the form of error correction in the ECM suggests a long-term relationship between the variables inflation, GDP variable, the variable exchange rate and variable interest rate is comparable.

Linkages between External Reserves and Economic Performance in Nigeria, 1981-2018: Bounds Test and ARDL Approaches

Aims: This study seeks to explore a two-way relationship between Nigeria’s economic performance, measured by the GDP, and her stock of foreign reserves over time.
 Study Design: It uses secondary data - documented time series of Nigeria’s gross domestic product (GDP) and foreign exchange reserves (FER) – collected from various volumes of the Central Bank of Nigeria (CBN) Statistical Bulletin. The annual time series data cover a period of 38 years, from 1981-2018.
 Methodology: The time series properties of the variables were verified using the Augmented Dickey-Fuller (ADF) unit roots’ test procedure. Also, the Bounds test technique was used to test for cointegration while the autoregressive distributed-lag (ARDL) and error correction models were estimated to analyze short- and long-run relationships between the variables. Relevant diagnostic tests were carried out to validate the resultant model estimates.
 Results: Results of unit roots’ test reveal both GDP and foreign reserves as I(1) series. Bounds test for the GDP model revealed an observed F-statistic (.421) that is less than the critical lower bound F-statistic (4.94) at P=.05 and cointegrating relationship was not confirmed. However, Bounds test for the foreign reserves revealed an observed F-statistic (6.445) lager than the critical upper bound F-statistic (5.73) at P=.05 and cointegration was established leading to specification of a long-run error correction model (ECM). Result of ARDL model estimation shows that only one-year-lag of GDP was significant (P=.05) and positive in explaining variations in the current GDP. Previous year’s values of both GDP and foreign reserves have positive influence on the long-run foreign exchange with over 81.8% explanatory power. The adjustment coefficient of the error correction equation is highly significant (P=.001) with the desired negative sign, implying that previous periods’ errors are correctable by adjustments in the subsequent periods, and convergence is attainable. Granger-Causality test result revealed a unidirectional causality that runs from GDP to the external reserves.
 Conclusion: The study establishes a long-run relationship between stock of foreign reserves and economic performance in Nigeria. The finding corroborates the view that a booming economy has the propensity to attract foreign direct investment thereby boosting the stock of the country’s foreign reserves. To attract more FDI in the critical sectors of the Nigerian economy, the government should create enabling and investment-friendly environment, implement policies and programmes capable of amplify ease-of-doing-business, and boost investors’ confidence in the economy.

Sample size and power calculations for open cohort longitudinal cluster randomized trials.

When calculating sample size or power for stepped wedge or other types of longitudinal cluster randomized trials, it is critical that the planned sampling structure be accurately specified. One common assumption is that participants will provide measurements in each trial period, that is, a closed cohort, and another is that each participant provides only one measurement during the course of the trial. However some studies have an “open cohort” sampling structure, where participants may provide measurements in variable numbers of periods. To date, sample size calculations for longitudinal cluster randomized trials have not accommodated open cohorts. Feldman and McKinlay (1994) provided some guidance, stating that the participant‐level autocorrelation could be varied to account for the degree of overlap in different periods of the study, but did not indicate precisely how to do so. We present sample size and power formulas that allow for open cohorts and discuss the impact of the degree of “openness” on sample size and power. We consider designs where the number of participants in each cluster will be maintained throughout the trial, but individual participants may provide differing numbers of measurements. Our results are a unification of closed cohort and repeated cross‐sectional sample results of Hooper et al (2016), and indicate precisely how participant autocorrelation of Feldman and McKinlay should be varied to account for an open cohort sampling structure. We discuss different types of open cohort sampling schemes and how open cohort sampling structure impacts on power in the presence of decaying within‐cluster correlations and autoregressive participant‐level errors.

Variable selection and estimation for high‐dimensional spatial autoregressive models

AbstractSpatial regression models are important tools for many scientific disciplines including economics, business, and social science. In this article, we investigate postmodel selection estimators that apply least squares estimation to the model selected by penalized estimation in high‐dimensional regression models with spatial autoregressive errors. We show that by separating the model selection and estimation process, the postmodel selection estimator performs at least as well as the simultaneous variable selection and estimation method in terms of the rate of convergence. Moreover, under perfect model selection, the ℓ2 rate of convergence is the oracle rate of , compared with the convergence rate of in the general case. Here, n is the sample size and p, s are the model dimension and number of significant covariates, respectively. We further provide the convergence rate of the estimation error in the form of norm, and ideally the rate can reach as fast as .

Iterative estimation correcting for error auto-correlation in short panels, applied to lagged dependent variable models

An iterative estimation procedure incorporating error serial correlation in short panels is presented. Neglecting serial correlation might both result in inconsistent variance estimates and in inconsistent parameter estimates, for example in (structural) models containing (functions of) lagged dependent variables. Lagged disturbances and/or lagged innovations are treated as unobserved or latent variables, and their estimates are included as supplementary regressors. Iterating until convergence results in consistent estimates of all model parameters. In addition, the asymptotic distribution of the proposed estimator, which can accommodate auto-regressive errors without any assumptions regarding starting values is presented. The main advantage of the proposed method is the maximal reuse of existing (cross-sectional) code, which can simply be adapted to serially correlated disturbances. As such, this “quick fix” estimator seems to be a promising avenue for applied researchers suspecting serial correlation in their data, but only willing to perform a moderate amount of coding (i.e. the iterative loop & the adaptation of the variance estimation), without having to develop, code and debug a full own model. The proposed method is applied to panel data models with lagged dependent variables with and without fixed effects.

TIME-VARYING MARKET PRICE OF RISK AND AUTOREGRESSIVE ERROR STRUCTURE OF OIL PRICES

In this paper we investigate the inclusion of a time-varying market price of risk in oil price factor models. Additionally an autoregressive error structure is adopted to filter this property of financial series. We use the Schwartz and Smith model, which is well established in the literature on commodity prices. The analysis is easily extended to different types of factor models. The empirical application considered the future oil contracts traded on the NYMEX. We find that considering a time-varying market price of risk and the autoregressive structure improves the fit of the empirical data.

Exchange Rate Private Sector Debt Nexus: Lessons from Turkish Experience

External debt stock of the private sector is increased from 2002 to 2018 in Turkey. In this period, Turkish Lira depreciated against foreign currencies. This study is investigated the relationship between real exchange rate and private sector external debt during the period 2002-2018 in Turkey. Auxiliary variables included in the model are LIBOR, domestic interest rates and real GDP. Application methods of the study are Delayed Distributed Autoregressive Model (ARDL) and Error Correction Model (ECM). Results of the study shows that, respectively domestic interest rate, LIBOR and exchange rate effective variables on the private sector external borrowing in the long term. Beside, exchange rate’s effects on the private sector external debt is higher in the short term than long term. Other hand, there is no relationship between private sector external borrowing and domestic production in the short and long term.

Empirical likelihood and GMM for spatial models

We link the empirical likelihood (EL) and GMM for three major spatial models: spatial autoregressive model with spatial autoregressive disturbances (SARAR model), linear regression model with spatial autoregressive errors (SE model) and spatial autoregressive model (SAR model). It is shown that for every GMM estimator (GMME), there is an empirical likelihood (EL) estimator which has the same asymptotic variance as the GMME. Specifically, we show that there exists an EL estimator which is asymptotically efficient as the best GMME proposed by Liu et al. [Liu, X. D., L. F. Lee, and C. R. Bollinger. 2010. An efficient GMM estimator of spatial autoregressive models. Journal of Econometrics 159 (2):303–19] and the EL confidence regions for the parameters in above models can be constructed without the estimation of asymptotic variances.

Estimating the volume of dirty money in Iran

In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cases in which the variables are inaccurate, this method is used. Fuzzy set theory is a generalization of the set theory. The underground economy is important in estimating the amount of dirty money and has a positive effect on this amount. The effect of the underground economy was investigated using the vector autoregressive (VAR) and vector error correction (VECM) models.•In this article applied the fuzzy logic, to estimate the underground economy.•The method presented in this article can be useful for Researchers and managers in the monetary trend of economics.•The fuzzy method is the best way to estimate the size of the underground economy because it is a measure of uncertainty.