Recent developments of the autoregressive distributed lag modelling framework (original) (raw)
Related papers
Autoregressive Distributed Lag (ARDL) cointegration technique: application and interpretation
Journal of Statistical and Econometric Methods, 2016
Economic analysis suggests that there is a long run relationship between variables under consideration as stipulated by theory. This means that the long run relationship properties are intact. In other words, the means and variances are constant and not depending on time. However, most empirical researches have shown that the constancy of the means and variances are not satisfied in analyzing time series variables. In the event of resolving this problem most cointegration techniques are wrongly applied, estimated, and interpreted. One of these techniques is the Autoregressive Distributed Lag (ARDL) cointegration technique or bound cointegration technique. Hence, this study reviews the issues surrounding the way cointegration techniques are applied, estimated and interpreted within the context of ARDL cointegration framework. The study shows that the adoption of the ARDL cointegration technique does not require pretests for unit roots unlike other techniques. Consequently, ARDL coint...
Quantile cointegration in the autoregressive distributed-lag modeling framework
Journal of Econometrics, 2015
Xiao (2009) develops a novel estimation technique for quantile cointegrated time series by extending Phillips and Hansen's (1990) semiparametric approach and Saikkonen's (1991) parametrically augmented approach. This paper extends Pesaran and Shin's (1998) autoregressive distributed-lag approach into quantile regression by jointly analysing short-run dynamics and long-run cointegrating relationships across a range of quantiles. We derive the asymptotic theory and provide a general package in which the model can be estimated and tested within and across quantiles. We further affirm our theoretical results by Monte Carlo simulations. The main utilities of this analysis are demonstrated through the empirical application to the dividend policy in the U.S.
On Autoregressive Distributed Lag, Cointegration and Error Correction Model
2012
This paper reviews the use of the traditional ARDL and the ARDL approach to cointegration for the analysis of short-run dynamic and long run relationship when series are difference stationary (series can be integrated of different orders). The two models were used to estimate the short-run dynamics and the long run relationships between selected Nigeria's macroeconomic series. The results compares favorably with the theory that the ARDL is equivalent to the short-run dynamics of the error correction model (the resultant model from the ARDL approach to cointegration).
Autocovariances and Autocorrelation Properties of Diagonal Vector Autoregressive and Multivariate Autoregressive Distributed Lag Models, 2024
The primary aim of this study was to conduct a comparative analysis of the performance of parsimonious models, specifically the Diagonal Vector Autoregressive (VAR) and Multivariate Autoregressive Distributed Lag (MARDL) Models, using their respective Autocovariance and Autocorrelation properties. This comparison was driven by the imposition of restrictions on parameters within the coefficient matrices, specifically limiting them to diagonal elements. To assess the efficacy of these novel multivariate lag models, we utilised data derived from key macroeconomic variables, including Nigeria's Gross Domestic Product (GDP), Crude Oil Petroleum (C/PET), Agriculture (AGRIC), and Telecommunication (TELECOM). The data was subjected to first-order differencing of the logarithm of the series to ensure stationarity. Subsequently, the models were estimated, and autocovariances and autocorrelations of the processes were derived for the analysis. The empirical findings revealed notable patterns, particularly the direct converse autocorrelation observed in both VAR and MARDL models. The negative autocorrelation identified in the macroeconomic variables suggests that periods of economic expansion were succeeded by contractions and vice versa. This implies a complementary relationship between the two models in effectively capturing the dynamics of multivariate lag variables. In conclusion, our study underscores the significance of considering the Diagonal Vector Autoregressive and Multivariate Autoregressive Distributed Lag Models with restricted parameters in the diagonal elements when modelling multivariate lag variables. These findings contribute to a nuanced understanding of the interplay between economic variables and provide valuable insights for researchers and practitioners in the field.
2014
Recently, Xiao (2009) develops a novel estimation technique for quantile cointegrated time series in a static regression by extending the semiparametric approach by Phillips and Hansen (1990) and the parametrically augmented approach by Saikkonen (1991). This paper aims to extend the autoregressive distributed-lag approach of Pesaran and Shin (1998) into the quantile regression framework. This QARDL extension enables us to jointly analyse the short-run dynamics and the long-run cointegrating relationship across a range of quantiles. We derive the asymptotic distribution of QARDL estimators, and provide a general package in which the model can be estimated and tested within and across quantiles. Monte Carlo simulation results provide strong support for theoretical predictions. The main utilities of QARDL are demonstrated through the empirical application to the dividend policy in the U.S.
PLOS ONE
Carbon dioxide (CO2) emissions have become a critical aspect of the economic and sustainable development indicators of every country. In Pakistan, where there is a substantial increase in the population, industrialization, and demand for electricity production from different resources, the fear of an increase in CO2 emissions cannot be ignored. This study explores the link that betwixt CO2 emissions with different significant economic indicators in Pakistan from 1960 to 2018 using the autoregressive distributed lag (ARDL) modelling technique. We implemented the covariance proportion, coefficient of determination, the Durbin Watson D statistics, analysis of variance (ANOVA), variance inflating factor (VIF), the Breusch-Pagan test, the Theil’s inequality, the root mean quare error (RMSE), the mean absolute percentage error (MAPE), and the mean absolute error (MAE) for the diagnostics, efficiency, and validity of our model. Our results showed a significant association between increased...
Australian Journal of Business and Management Research
This paper reviews the use of the traditional ARDL and the ARDL approach to cointegration for the analysis of short-run dynamic and long run relationship when series are difference stationary (series can be integrated of different orders). The two models were used to estimate the short-run dynamics and the long run relationships between selected Nigeria’s macroeconomic series. The results compares favorably with the theory that the ARDL is equivalent to the short-run dynamics of the error correction model (the resultant model from the ARDL approach to cointegration).
The Annals of Regional Science, 2006
A spatial generalization of the (from times-series special case well known) Autoregressively Distributed lag model is defined. Equivalent forms-a Spatial Error Correction model, a Spatial Bewley model and a Spatial Baardsen model-are considered. As none of these may be consitently estimated by Ordinary Least Squares, an Instrument Variable estimation procedure is investigated. Generate from n independent N(0,1) Generate x from n independent U(0,1) Create a random W using d and the above rule Row-standardize W (i.e. divide each element with rowsum) Calculate y = (I-W)-1 (i + x + Wx +) Perform IV estimation of SADL Store estimates, denoted by a 0 , a 1 , b 0 , and b 1 Store t values for the parametres Store the Wald test Calculate 5, 10 50, 90, 95 per cent deciles for each stored quantity Conclude the study by comparing these deciles to their theoretical counterparts.