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Journal articles on the topic 'Distributed-lag model'

Author: Grafiati

Published: 4 June 2021

Last updated: 24 January 2023

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1

Ruiz Estrada, Mario Arturo, Evangelos Koutronas, and Ross Knippenberg. "The Mega Distributed Lag Model." Contemporary Economics 10, no. 2 (June 30, 2016): 113–22. http://dx.doi.org/10.5709/ce.1897-9254.203.

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2

Tsay, Ruey S. "Model Identification in Dynamic Regression (Distributed Lag) Models." Journal of Business & Economic Statistics 3, no. 3 (July 1985): 228. http://dx.doi.org/10.2307/1391593.

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Tsay, Ruey S. "Model Identification in Dynamic Regression (Distributed Lag) Models." Journal of Business & Economic Statistics 3, no. 3 (July 1985): 228–37. http://dx.doi.org/10.1080/07350015.1985.10509454.

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4

Swanepoel, Ezelda. "Auto-regressive Distributed Lag Model for long-run US household debt determinants." Investment Management and Financial Innovations 16, no. 3 (August 1, 2019): 40–48. http://dx.doi.org/10.21511/imfi.16(3).2019.05.

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US household debt increased on a yearly basis from 1987 to 2007. In addition, household debt in the USA nearly doubled between 2000 and 2007, from $5.6 trillion to $9 trillion. This came to an abrupt end in 2009 with the crash of the financial market. This paper employs the bound test and Auto-regressive Distributed Lag Model to determine the long-run relationship between US household debt and consumer prices, housing prices, the unemployment rate, and the lending rate. Unit root tests were conducted first to ascertain the stationarity of the variables. E-views 11 was used in the analysis of the data, which was obtained from Q1: 1990 to Q1: 2007 from the International Monetary Fund and the US FED. It was found that in the long run, there is a negative effect of consumer prices and unemployment on US household debt, while house prices and the lending rate would have a positive effect on household debt.

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LIHAWA, SRIRAPI H., RESMAWAN RESMAWAN, DEWI RAHMAWATY ISA, and LA ODE NASHAR. "DISTRIBUTED LAG MODEL PENGARUH JUMLAH UANG BEREDAR TERHADAP NILAI TUKAR RUPIAH MENGGUNAKAN METODE KOYCK DAN ALMON." Jambura Journal of Probability and Statistics 3, no. 1 (May 31, 2022): 39–45. http://dx.doi.org/10.34312/jjps.v3i1.11805.

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A regression model that contains the dependent variable which is influenced by the current independent variable, and is also influenced by the independent variable at the previous time is called a distributed lag model. Distributed lag model is a dynamic model in econometrics that is useful in empirical econometrics because it makes a static economic theory dynamic by taking into account the role of time explicitly. There are two distributed lag models, namely the infinite lag model and the finite lag model using the Koyck method and the Almon method in determining the estimated Distributed lag model. This study aims to determine the Distributed lag model for the effect of the money supply on the rupiah exchange rate and determine the best model based on the Koyck method and the Almon method. From the results of selecting the best model based on the SIC value and judging by the more precise R2 of the Koyck method, the resulting model ist = 7958 + 0.0002Xt + 0.000177Xt-1+ 0.000157Xt-2+ 0.000139Xt-3 + 0.0000123Xt-4

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Lukman, Adewale F., and Golam B. M. Kibria. "Almon-KL estimator for the distributed lag model." Arab Journal of Basic and Applied Sciences 28, no. 1 (January 1, 2021): 406–12. http://dx.doi.org/10.1080/25765299.2021.1989160.

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Tarasov, Vasily E., and Valentina V. Tarasova. "Harrod–Domar Growth Model with Memory and Distributed Lag." Axioms 8, no. 1 (January 15, 2019): 9. http://dx.doi.org/10.3390/axioms8010009.

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In this paper, we propose a macroeconomic growth model, in which we take into account memory with power-law fading and gamma distributed lag. This model is a generalization of the standard Harrod–Domar growth model. Fractional differential equations of this generalized model with memory and lag are suggested. For these equations, we obtain solutions, which describe the macroeconomic growth of national income with fading memory and distributed time-delay. The asymptotic behavior of these solutions is described.

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Lopo, Alexandre Boleira, Maria Helena Constantino Spyrides, Paulo Sérgio Lucio, and Javier Sigró. "UV Index Modeling by Autoregressive Distributed Lag (ADL Model)." Atmospheric and Climate Sciences 04, no. 02 (2014): 323–33. http://dx.doi.org/10.4236/acs.2014.42033.

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9

Hu, Wei, and Norman M. Wereley. "Distributed Rate-Dependent Elastoslide Model for Elastomeric Lag Dampers." Journal of Aircraft 44, no. 6 (November 2007): 1972–84. http://dx.doi.org/10.2514/1.26409.

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Virgantari, Fitria, and Wilda Rahayu. "PENDUGAAN PARAMETER MODEl DISTRIBUTED LAG POLA POLINOMIAL MENGGUNAKAN METODE ALMON." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 4 (December 1, 2021): 761–72. http://dx.doi.org/10.30598/barekengvol15iss4pp761-772.

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The distributed lag model is a regression model that describes the relationship between the dependent variable of a given period and the independent variables of a certain or previous periods. The model can be used to determine the impact of the independent variable to dependent variables over time and forecast time series data for the next periods. There are two forms of distributed lag model that have been widely proposed in the estimation of distributed lag regression model. The first form is proposed by Koyck and the second form by Almon. This paper aims to apply the Almon model to examine the effect of the ratio of BOPO (Operating Cost and Operating Income) to the ROA (Return on Asset) of a government bank based on quarterly data, to estimate its parameters, to examine the feasibility of the model, and to predict the next quarter. Results shows that distributed lag model is = 10.110 - 0.078 + 0.015 + 0.026 – 0.045 with Yt is ROA, and Xt is the ratio BOPO on the 1st quarter until the previous 3 quarters. The model is quite good according to the determination coefficient that is 0.75, no autocorrelation in the model, t test and F test are also significant. Based on the model, the value of ROA ratio next quarter predicted 4.63%. The decrease in profitability ROA ratio is due to an increase in interest expense while interest income can not compensate

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11

Ningrum, Dewi Kusuma, and Sugiyarto Surono. "Comparison the Error Rate of Autoregressive Distributed Lag (ARDL) and Vector Autoregressive (VAR) (Case study: Forecast of Export Quantities in DIY)." JURNAL EKSAKTA 18, no. 2 (September 27, 2018): 167–77. http://dx.doi.org/10.20885/eksakta.vol18.iss2.art8.

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Forecasting is estimating the size or number of something in the future. Regression model that enters current independent variable value, and lagged value is called distributed-lag model, if it enters one or more lagged value, it is called autoregressive. Koyck method is used for dynamic model which the lagged length is unknown, for the known lagged length it is used the Almon method. Vector Autoregressive (VAR) is a method that explains every variable in the model depend on the lag movement from the variable itself and all the others variable. This research aimed to explain the application of Autoregressive distributed-lag model and Vector Autoregressive (VAR) method for the forecasting for export amount in DIY. It takes export amount in DIY and inflation data, kurs, and Indonesias foreign exchange reserve. Forecasting formation: defining Koyck and Almon distributed-lag dynamic model, then the best model is chosen and distribution-lag dynamic forecasting is performed. After that it is performed stationary test, co-integration test, optimal lag examination, granger causality test, parameter estimation, VAR model stability, and performs forecasting with VAR method. The forecasting result shows MAPE value from ARDL method obtained is 0.475812%, while MAPE value from VAR method is 0.464473%. Thus it can be concluded that Vector Autoregressive (VAR) method is more effective to be used in case study of export amount in DIY forecasting. Keywords: Koyck; Almon; Lag; Autoregressive Distributed-Lag; Vector Autoregressive;

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12

Janssen, Willem. "Welfare Calculations in a Distributed Lag Model: Beans in Colombia." Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 40, no. 3 (November 1992): 459–74. http://dx.doi.org/10.1111/j.1744-7976.1992.tb03707.x.

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13

Bello, Ghalib A., Manish Arora, Christine Austin, Megan K. Horton, Robert O. Wright, and Chris Gennings. "Extending the Distributed Lag Model framework to handle chemical mixtures." Environmental Research 156 (July 2017): 253–64. http://dx.doi.org/10.1016/j.envres.2017.03.031.

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14

Pal, D., and S. K. Mitra. "Impact of price realization on India’s tea export: Evidence from Quantile Autoregressive Distributed Lag Model." Agricultural Economics (Zemědělská ekonomika) 61, No. 9 (June 6, 2016): 422–28. http://dx.doi.org/10.17221/209/2014-agricecon.

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15

Lal Shrestha, Srijan. "Particulate Air Pollution and Daily Mortality in Kathmandu Valley, Nepal: Associations and Distributed Lag." Open Atmospheric Science Journal 6, no. 1 (April 20, 2012): 62–70. http://dx.doi.org/10.2174/1874282301206010062.

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The distributed lag effect of ambient particulate air pollution that can be attributed to all cause mortality in Kathmandu valley, Nepal is estimated through generalized linear model (GLM) and generalized additive model (GAM) with autoregressive count dependent variable. Models are based upon daily time series data on mortality collected from the leading hospitals and exposure collected from the 6 six strategically dispersed fixed stations within the valley. The distributed lag effect is estimated by assigning appropriate weights governed by a mathematical model in which weights increased initially and decreased later forming a long tail. A comparative assessment revealed that autoregressive semiparametric GAM is a better fit compared to autoregressive GLM. Model fitting with autoregressive semi-parametric GAM showed that a 10 μg m rise in PM is associated with 2.57 % increase in all cause mortality accounted for 20 days lag effect which is about 2.3 times higher than observed for one day lag and demonstrates the existence of extended lag effect of ambient PM on all cause deaths. The confounding variables included in the model were parametric effects of seasonal differences measured by Fourier series terms, lag effect of mortality, and nonparametric effect of temperature represented by loess smoothing. The lag effects of ambient PM remained constant beyond 20 days.

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16

Lauridsen, Jørgen. "Spatial autoregressively distributed lag models: equivalent forms, estimation, and an illustrative commuting model." Annals of Regional Science 40, no. 2 (April 1, 2006): 297–311. http://dx.doi.org/10.1007/s00168-005-0044-4.

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17

Tansuchat, Roengchai, and Chia-Lin Chang. "Latent carbon emission pricing model for Thailand: A nonlinear autoregressive distributed lag model." Energy Reports 8 (November 2022): 768–75. http://dx.doi.org/10.1016/j.egyr.2022.05.187.

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18

Chikri, Hassan, Adil Moghar, Manar Kassou, and Faris Hamza. "New evidence from NARDL model on CO2 emissions: Case of Morocco." E3S Web of Conferences 234 (2021): 00026. http://dx.doi.org/10.1051/e3sconf/202123400026.

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The main objective of this study is to examine the effect of sickle energy consumption, renewable energy, and forest area on the emission of carbon dioxide (CO2) in Morocco. Many studies have abord this subject using a different approachs, most of which have used econometric models such as Vector Autoregressive (VAR) Error Correction Model (ECM) and Autoregressive Distributed Lag (ARDL). In this study, we opted for the Non-linear Autoregressive Distributed Lag (NARDL) model. The data used covers the period from 1990 to 2018 (annual data). The results of our model are significant and prove the asymmetric effects of the explanatory variables on CO2 emissions.

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19

Thurman, S. S., P. A. V. B. Swamy, and J. S. Mehta. "An examination of distributed lag model coefficients estimated with smoothness priors." Communications in Statistics - Theory and Methods 15, no. 6 (January 1986): 1723–49. http://dx.doi.org/10.1080/03610928608829216.

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20

Ekananda, Mahjus, and T. Suryanto. "The Autoregressive Distributed Lag Model to Analyze Soybean Prices in Indonesia." MATEC Web of Conferences 150 (2018): 05035. http://dx.doi.org/10.1051/matecconf/201815005035.

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The main objective of this study was to observe factors that affecting domestic soybean prices, including government intervention through BULOG. By using Bound Testing Cointegration method with ARDL approach. In the short term the world soybean price variables in the t-period and exchange rate affect the domestic soybean prices positively and significantly. The variable volume of soybean imports, GDP, and the role of BULOG as sole importer in the t-period does not affect the domestic soybean price significantly. In the long run, the t-period import tariff has a negative and significant effect.

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21

Guo, Chao-Yu, Xing-Yi Huang, Pei-Cheng Kuo, and Yi-Hau Chen. "Extensions of the distributed lag non-linear model (DLNM) to account for cumulative mortality." Environmental Science and Pollution Research 28, no. 29 (March 18, 2021): 38679–88. http://dx.doi.org/10.1007/s11356-021-13124-0.

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AbstractThe effects of meteorological factors on health outcomes have gained popularity due to climate change, resulting in a general rise in temperature and abnormal climatic extremes. Instead of the conventional cross-sectional analysis that focuses on the association between a predictor and the single dependent variable, the distributed lag non-linear model (DLNM) has been widely adopted to examine the effect of multiple lag environmental factors health outcome. We propose several novel strategies to model mortality with the effects of distributed lag temperature measures and the delayed effect of mortality. Several attempts are derived by various statistical concepts, such as summation, autoregressive, principal component analysis, baseline adjustment, and modeling the offset in the DLNM. Five strategies are evaluated by simulation studies based on permutation techniques. The longitudinal climate and daily mortality data in Taipei, Taiwan, from 2012 to 2016 were implemented to generate the null distribution. According to simulation results, only one strategy, named MVDLNM, could yield valid type I errors, while the other four strategies demonstrated much more inflated type I errors. With a real-life application, the MVDLNM that incorporates both the current and lag mortalities revealed a more significant association than the conventional model that only fits the current mortality. The results suggest that, in public health or environmental research, not only the exposure may post a delayed effect but also the outcome of interest could provide the lag association signals. The joint modeling of the lag exposure and the delayed outcome enhances the power to discover such a complex association structure. The new approach MVDLNM models lag outcomes within 10 days and lag exposures up to 1 month and provide valid results.

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22

Cranmer, Skyler J., Douglas R. Rice, and Randolph M. Siverson. "What To Do About Atheoretic Lags." Political Science Research and Methods 5, no. 4 (August 26, 2015): 641–65. http://dx.doi.org/10.1017/psrm.2015.36.

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We examine a problem that is confronted frequently by political science researchers seeking to model longitudinal data: what to do when one suspects a lag between the realization of a regressor and its effect on the outcome variable, but one has no theoretical reason to suspect a particular lag length. We examine the theoretical challenges posed by atheoretic lags, review existing methods for atheoretic lag analysis—most notably distributed lag specifications—and their shortcomings, and present an alternative approach for atheoretic lag analysis based on Bayesian model averaging (BMA). We demonstrate the use and utility of our approach with two examples: the litigant signal model in American politics and modernization theory in political economy. Our examples show the increasing difficulty of analyzing models with atheoretic lags as the set of possible specifications increases, and demonstrate the effectiveness of BMA for the modal type of specification in time-series cross-sectional applications.

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23

N, Saka, and Adegbembo T. F. "AN ASSESSMENT OF THE IMPACT OF THE CONSTRUCTION SECTOR ON THE GROSS DOMESTIC PRODUCT (GDP) OF NIGERIA." Journal of Surveying, Construction & Property 13, no. 1 (June 30, 2022): 42–65. http://dx.doi.org/10.22452/jscp.vol13no1.4.

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The construction sector makes significant contribution to employment, domestic capital formation and the Gross Domestic Product (GDP). However, the Nigerian Construction Sector (NCS) is beset by a number of challenges including over-dependent on foreign inputs, economic volatility, low linkages and poor project cost and time performance. The study investigates the impact of the construction sector on the GDP using a 47-year annualized Time Series Data (TSD) gotten from the United Nations Statistics Department (UNSD) database. The study employs econometric methodology which involves series of tests and procedures including tests for unit root and cointegration and Polynomial Distributed Lag (PDL) model. The summary of the estimates including the PDL indicate significant effect of the construction sector on the GDP only when the lag of the GDP is not included as one of the regressors. The study concludes that the effect of the construction sector on the GDP is not robust. Finally, the study recommends for a new national housing and transport infrastructure policy for the sustainable development of the Nigerian constructed infrastructure facilities. The study has demonstrated the relationship between the NCS and GDP. The study added to the body of knowledge by using time series data involving the use of distributed lag model (DLM), autoregressive distributed lag (ADL) model and polynomial distributed lag (PDL) model to assess the relationship.

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Silaev, A., G. Parshikova, and A. Perfilyev. "SYNTHESIS OF LAG AND INTEGRAL MODELS OF ECONOMIC AND ECOLOGICAL PROCESSES." East European Scientific Journal 1, no. 12(76) (January 25, 2022): 32–36. http://dx.doi.org/10.31618/essa.2782-1994.2021.1.76.194.

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In this paper, the authors propose to model the dynamic processes of economics and ecology not by differential, but by integral Fredholm (or Volterra) equations. Since with this approach, the overwhelming majority of simultaneous values of indicators representing exogenous and endogenous variables of the corresponding economic and mathematical models are considered, the authors attempt to synthesize distributed lag models (continuous and discrete) with Fredholm (Volterra) integral equations. The integral core, which is part of the transforming operator, transfers its accumulating action, transforming, in essence, an exogenous indicator into an endogenous one. Three schemes are proposed, according to which the resulting integral indicator is realized in relation to a stimulus with a lagging argument (lag). The synthesis of the distributed lag model and the model, the apparatus of which is the Fredholm and Volterra integral equations, will increase the reliability (probability of reliability) of the forecast estimates of the economic indicators of the models.

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25

Niewiadomska, Ewa, Małgorzata Kowalska, Adam Niewiadomski, Michał Skrzypek, and Michał A. Kowalski. "Assessment of Risk Hospitalization due to Acute Respiratory Incidents Related to Ozone Exposure in Silesian Voivodeship (Poland)." International Journal of Environmental Research and Public Health 17, no. 10 (May 20, 2020): 3591. http://dx.doi.org/10.3390/ijerph17103591.

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The main aim of this work is the estimation of health risks arising from exposure to ozone or other air pollutants by different statistical models taking into account delayed health effects. This paper presents the risk of hospitalization due to bronchitis and asthma exacerbation in adult inhabitants of Silesian Voivodeship from 1 January 2016 to 31 August 2017. Data were obtained from the daily register of hospitalizations for acute bronchitis (code J20–J21, International Classification of Diseases, Tenth Revision – ICD-10) and asthma (J45–J46) which is governed by the National Health Fund. Meteorological data and data on tropospheric ozone concentrations were obtained from the regional environmental monitoring database of the Provincial Inspector of Environmental Protection in Katowice. The paper includes descriptive and analytical statistical methods used in the estimation of health risk with a delayed effect: Almon Distributed Lag Model, the Poisson Distributed Lag Model, and Distributed Lag Non-Linear Model (DLNM). A significant relationship has only been confirmed by DLNM for bronchitis and a relatively short period (1–3 days) from exposure above the limit value (120 µg/m3). The relative risk value was RR = 1.15 (95% CI 1.03–1.28) for a 2-day lag. However, conclusive findings require the continuation of the study over longer observation periods.

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Sumobay, Arvin Paul B., and Arnulfo P. Supe. "Bayesian analysis of structural change in a distributed Lag Model (Koyck Scheme)." Discussiones Mathematicae Probability and Statistics 34, no. 1-2 (2014): 113. http://dx.doi.org/10.7151/dmps.1171.

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27

Lee, Kyung-Hee and Kyung Soo Kim. "A Study on Estimating Tourism Elasticities using Autoregressive Distributed Lag(ARDL) model." Management & Information Systems Review 36, no. 2 (June 2017): 81–92. http://dx.doi.org/10.29214/damis.2017.36.2.005.

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Benjamin, John D., G. Donald Jud, and A. Ason Okoruwa. "Forecasting the stock of retail space using the Koyck distributed lag model." Journal of Property Research 10, no. 3 (December 1993): 185–92. http://dx.doi.org/10.1080/09599919308724092.

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Laguna, Francisco, María Eugenia Grillet, José R. León, and Carenne Ludeña. "Modelling malaria incidence by an autoregressive distributed lag model with spatial component." Spatial and Spatio-temporal Epidemiology 22 (August 2017): 27–37. http://dx.doi.org/10.1016/j.sste.2017.05.001.

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Özbay, Nimet. "Two-Parameter Ridge Estimation for the Coefficients of Almon Distributed Lag Model." Iranian Journal of Science and Technology, Transactions A: Science 43, no. 4 (September 4, 2018): 1819–28. http://dx.doi.org/10.1007/s40995-018-0634-5.

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31

Yeo, Stephen J., and P. K. Trivedi. "PRACTITIONERS CORNER: On Using Ridge-Type Estimators For a Distributed Lag Model." Oxford Bulletin of Economics and Statistics 51, no. 1 (May 1, 2009): 85–90. http://dx.doi.org/10.1111/j.1468-0084.1989.mp51001006.x.

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32

Galvao JR., Antonio F., Gabriel Montes-Rojas, and Sung Y. Park. "Quantile Autoregressive Distributed Lag Model with an Application to House Price Returns*." Oxford Bulletin of Economics and Statistics 75, no. 2 (December 21, 2011): 307–21. http://dx.doi.org/10.1111/j.1468-0084.2011.00683.x.

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33

EL Boukari, Brahim, Khalid Hattaf, and Noura Yousfi. "A Discrete Model for HIV Infection with Distributed Delay." International Journal of Differential Equations 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/138094.

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We give a consistent discretization of a continuous model of HIV infection, with distributed time delays to express the lag between the times when the virus enters a cell and when the cell becomes infected. The global stability of the steady states of the model is determined and numerical simulations are presented to illustrate our theoretical results.

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Kuternin, Mikhail I., and Aleksandr A. Silaev. "OPTIMIZATION OF INVESTMENT PROCESSES FOR THE DEVELOPMENT OF THE OIL INDUSTRY." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 9/1, no. 129 (2022): 101–7. http://dx.doi.org/10.36871/ek.up.p.r.2022.09.01.010.

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The article develops a methodology of determining the optimal indicators of the investment program for the development of the region oil industry. The issues of assessing the effectiveness of capital investments in the exploration and development of oil fields, taking into account the delayed and distributed over time impact of such investments on the volume of crude oil production, were investigated. The methodological basis of the study is mathematical modeling of investment processes in the oil industry using distributed lag models. Much attention is paid to the study of the influence of random factors on the effectiveness of capital investments and the parameters of the optimal investment program. The article develops a methodology that allows excluding the vector of unobservable system parameters from the equations of the mathematical model and takes into account the change in the nature of the influence of random factors when replacing endogenous parameters of the model. The proposed model of the investment process shows how the efficiency of capital investments in the development of the region oil industry increases depending on the accepted structure of such investments lag. The conclusion indicates the possibilities for improving the efficiency of investments using the developed model. The authors propose to synthesize the global optimization method with the included distributed lag model, which is used according to the method improved in the article.

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Atem, Gabriel Garang. "Remittances and Financial Development in Kenya: An Autoregressive Distributed Lag Approach." African Journal of Economics and Sustainable Development 5, no. 1 (March 24, 2022): 95–108. http://dx.doi.org/10.52589/ajesd-tjtnptql.

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This study estimates an Autoregressive Distributed Lag (ARDL) econometric model between 1970 to 2018 to test the long-run effect of remittances on financial development in Kenya. It also interacts remittances with monetary policy and human capital to test their complementarity in facilitating financial development. The long-run model finds that remittances hurt financial development contradicting the theoretical view. A possible explanation is the substitutability hypothesis which states that remittances replace the demand for financial products. The long-run model’s results find that monetary policy complements remittances while human capital harms the complementarity role of remittances. More studies are required to isolate the cause of the negative externality of human capital in facilitating remittances to boost financial development. Surprisingly, openness and economic growth used as control variables have negative effects on financial development, which also need further study. The long-run equilibrium model adjusts at a speed of 51.8 percent to correct short-term disequilibrium after every two years. The study recommends that policymakers in Kenya should be cautious about the negative side effects of remittances on financial development. This study recommends that policymakers identify prudent monetary, exchange rate, trade, and fiscal policies to curb the side effects of remittances in the economy and broader development planning.

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36

Yan, Dawen, Guotai Chi, and Kin Keung Lai. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models." Mathematics 8, no. 8 (August 3, 2020): 1275. http://dx.doi.org/10.3390/math8081275.

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In this paper, we propose a new framework of a financial early warning system through combining the unconstrained distributed lag model (DLM) and widely used financial distress prediction models such as the logistic model and the support vector machine (SVM) for the purpose of improving the performance of an early warning system for listed companies in China. We introduce simultaneously the 3~5-period-lagged financial ratios and macroeconomic factors in the consecutive time windows t − 3, t − 4 and t − 5 to the prediction models; thus, the influence of the early continued changes within and outside the company on its financial condition is detected. Further, by introducing lasso penalty into the logistic-distributed lag and SVM-distributed lag frameworks, we implement feature selection and exclude the potentially redundant factors, considering that an original long list of accounting ratios is used in the financial distress prediction context. We conduct a series of comparison analyses to test the predicting performance of the models proposed by this study. The results show that our models outperform logistic, SVM, decision tree and neural network (NN) models in a single time window, which implies that the models incorporating indicator data in multiple time windows convey more information in terms of financial distress prediction when compared with the existing singe time window models.

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Asif, Muhammad, and Shamsul Haq. "Autoregressive Distributed Lag Transformation for Exchange Rate and Trade Balance." International Journal of Emerging Multidisciplinaries: Mathematics 1, no. 1 (January 14, 2022): 85–90. http://dx.doi.org/10.54938/ijemdm.2022.01.1.13.

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In this study the prime objective is to initiate autoregressive distributed lag transformation for exchange rate and trade balance to avoid the possible existence of multicollinearity among the explanatory variables and to analyze the variation in real exchange rate and its impact on trade balance in Pakistan. The Koyck model was used for studying the immediate impact and long run relationship between the variables by using time series annual data ranging from 1981 to 2021. The result showed that there is an inverse association between the trade balance and the exchange rate. It is evident from the results that the depreciation in exchange rate will bring upward movement in the trade balance.

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Mekonen, Endalkachew Kabtamu. "Agriculture Sector Growth and Inflation in Ethiopia: Evidence from Autoregressive Distributed Lag Model." Open Journal of Business and Management 08, no. 06 (2020): 2355–70. http://dx.doi.org/10.4236/ojbm.2020.86145.

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Babiera, Johniel E., and Anulfo P. Supe. "Detecting multiple structural breaks on a Koyck distributed lag model: A Bayesian approach." Applied Mathematical Sciences 9 (2015): 7307–15. http://dx.doi.org/10.12988/ams.2015.58529.

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Wu, Y. June, and J. David Fuller. "An Algorithm for the Multiperiod Market Equilibrium Model with Geometric Distributed Lag Demand." Operations Research 44, no. 6 (December 1996): 1002–12. http://dx.doi.org/10.1287/opre.44.6.1002.

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Nchor, Dennis, and Samuel Antwi Darkwah. "Inflation, Exchange Rates and Interest Rates in Ghana: an Autoregressive Distributed Lag Model." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63, no. 3 (2015): 969–77. http://dx.doi.org/10.11118/actaun201563030969.

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This paper investigates the impact of exchange rate movement and the nominal interest rate on inflation in Ghana. It also looks at the presence of the Fisher Effect and the International Fisher Effect scenarios. It makes use of an autoregressive distributed lag model and an unrestricted error correction model. Ordinary Least Squares regression methods were also employed to determine the presence of the Fischer Effect and the International Fisher Effect. The results from the study show that in the short run a percentage point increase in the level of depreciation of the Ghana cedi leads to an increase in the rate of inflation by 0.20%. A percentage point increase in the level of nominal interest rates however results in a decrease in inflation by 0.98%. Inflation increases by 1.33% for every percentage point increase in the nominal interest rate in the long run. An increase in inflation on the other hand increases the nominal interest rate by 0.51% which demonstrates the partial Fisher effect. A 1% increase in the interest rate differential leads to a depreciation of the Ghana cedi by approximately 1% which indicates the full International Fisher effect.

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Bentzen, Jan, and Tom Engsted. "A revival of the autoregressive distributed lag model in estimating energy demand relationships." Energy 26, no. 1 (January 2001): 45–55. http://dx.doi.org/10.1016/s0360-5442(00)00052-9.

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Tsay, Wen-Jen. "The long memory autoregressive distributed lag model and its application on Congressional approval." Electoral Studies 29, no. 1 (March 2010): 128–43. http://dx.doi.org/10.1016/j.electstud.2009.06.004.

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Banerjee, Anindya, John W. Galbraith, and Juan Dolado. "PRACTITIONER'S CORNER: Dynamic Specification and Linear Transformations of the Autoregressive-Distributed Lag Model*." Oxford Bulletin of Economics and Statistics 52, no. 1 (May 1, 2009): 95–104. http://dx.doi.org/10.1111/j.1468-0084.1990.mp52001007.x.

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Majid, Abdul, Muhammad Aslam, and Saima Altaf. "Improved Testing of Distributed Lag Model in Presence of Heteroscedasticity of Unknown Form." Nigerian Journal of Technological Research 12, no. 2 (October 11, 2017): 43. http://dx.doi.org/10.4314/njtr.v12i2.7.

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Frick, H. "A note on a dynamic adjustment equation for a Poisson distributed lag model." Empirical Economics 11, no. 1 (March 1986): 65–67. http://dx.doi.org/10.1007/bf01978146.

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AQIBAH, MAHMUDATUL, NI LUH PUTU SUCIPTAWATI, and I. WAYAN SUMARJAYA. "MODEL DINAMIS AUTOREGRESSIVE DISTRIBUTED LAG (STUDI KASUS: PENGARUH KURS DOLAR AMERIKA DAN INFLASI TERHADAP HARGA SAHAM TAHUN 2014-2018)." E-Jurnal Matematika 9, no. 4 (November 28, 2020): 240. http://dx.doi.org/10.24843/mtk.2020.v09.i04.p304.

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The aim of this research is to determine the dynamic model equation of autoregressive distributed lag by using koyck method, to find out the effect of log US dollar exchange rate and log inflation on log stock price in 20142018, and to forecast value of log stock price on January 2019August 2019. The data used in 20142018. The data was transformed into logarithm format. Time series plot of log US dollar exchange rate, log inflation, and log stock price suggest that the fluctuation in the data, for instance, both upward and downward trends, during the period. We obtained that the Koyck transformation could changed the lag distribution model into autoregressive distributed lag (ARDL) dynamic model. Furthermore, the log of US dollar exchange rate and log inflation have negative effect on log stock price in particular period. We measured forecasting accuracy using mean absolute prediction error (MAPE) and concluded that ARDL forecasting using Koyck model shows a significant increase in stock price.

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Miao, En Ming, Xin Wang, Ye Tai Fei, and Yan Yan. "Application of Autoregressive Distributed Lag (ADL) Model to Thermal Error Modeling on NC Machine Tools." Applied Mechanics and Materials 103 (September 2011): 9–14. http://dx.doi.org/10.4028/www.scientific.net/amm.103.9.

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Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

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Cheng, Ching-Hsue, and Ming-Chi Tsai. "An Intelligent Time Series Model Based on Hybrid Methodology for Forecasting Concentrations of Significant Air Pollutants." Atmosphere 13, no. 7 (July 2, 2022): 1055. http://dx.doi.org/10.3390/atmos13071055.

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Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality.

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Ahmad Ridha, Nurjannah, and Ratna Mutia. "Analisis Permintaan Uang di Indonesia: Pendekatan Autoegressive Distributed lag (Ardl)." Jurnal Samudra Ekonomika 5, no. 2 (September 30, 2021): 152–60. http://dx.doi.org/10.33059/jse.v5i2.4273.

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Penelitian ini mengkaji permintaan uang di Indonesia dengan menggunakan pendekatan autoregressive distributed lag (ARDL). Tujuan dari penelitian ini adalah untuk mengetahui fungsi permintaan uang di Indonesia dan faktor-faktor yang mempengaruhinya selama periode 1990:1-2019:4 dengan menggunakan data kuartal. Ada dua persamaan estimasi untuk uang sempit (M1) dan uang luas (M2). Hasil pengujian bounds testing menunjukkan bahwa ada hubungan jangka panjang yang stabil antara permintaan uang dan determinannya. Hasil persamaan pertama (M1) dalam jangka panjang variabel GDP dan inflasi bertanda positif sedangkan suku bunga dan nilai tukar bertanda negatif, dan semua variabel independen berpengaruh signifikan, yang ditunjukkan dengan nilai probabilitas kurang dari 0,05. Pada persamaan kedua (M2) dalam jangka panjang, variabel GDP dan nilai tukar menunjukkan arah positif sedangkan variabel inflasi memiliki arah negatif. Semua variabel independen berpengaruh signifikan terhadap permintaan uang di Indonesia kecuali variabel suku bunga. Nilai koefesien determinasi R2 untuk persamaan pertama sebesar 0,857 sedangkan persamaan kedua sebesar 0,807. Hasil pengujian CUSUM dan CUSUMQ untuk kedua model analisis stabil dalam jangka panjang.

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