Forecasting with Time Series Data and Distributed Lag Models

2013 ◽  
pp. 371-412
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Distributed Lag ◽  
Distributed Lag Models

scholarly journals Is Islamic Bank More Stable Than Conventional Bank? Evidence From Islamic Rural Banks in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 294
Author(s):  
Agus Widarjono ◽  
M. B. Hendrie Anto ◽  
Faaza Fakhrunnas
Keyword(s):  
Time Series ◽  
Financial Stability ◽  
Time Series Data ◽  
Series Data ◽  
Z Score ◽  
Profit Rate ◽  
Return On Assets ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Better Than

This study investigates whether Islamic rural banks perform better than conventional rural banks as their competitor in Indonesia. To measure Islamic rural banks' financial performance, we apply financial stability using Z-score and profitability using the return on assets. We use monthly time series data from January 2009 to December 2018. The dynamic regression of the Autoregressive Distributed Lag (ARDL) model is then employed. The results report that the Z-Score of Islamic rural banks is higher than the Z-Score of conventional rural banks. This finding shows that Islamic rural banks are less risky than conventional rural banks. However, the Islamic rural banks' financial stability is very vulnerable to changes in equity, output, and inflation than conventional rural banks. Although the Islamic rural banks' profit rate is lower compared to conventional rural banks, it is considered more stable. The profit of Islamic rural banks is affected by size, equity, domestic output, and inflation.

scholarly journals Exploring OECD SDGs pilot city's 50-year time series data and its Environmental Kuznets Curves

2019 ◽  
Author(s):  
Quan-Hoang Vuong ◽  
Tung Manh Ho ◽  
NGUYỄN Minh Hoàng
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Kuznets Curve ◽  
Infrastructure Development ◽  
Low Carbon ◽  
Industrial Firms ◽  
Distributed Lag ◽  
Size Growth ◽  
Paved Road

Can green growth policies help protect the environment while keeping the industry growing and infrastructure expanding? This study applies Auto-Regressive Distributed Lag (ARDL) method on the 50-years’ time series data, from 1967 to 2015, of Kitakyushu City, Japan, and found mixed evidence for Environmental Kuznets Curve (EKC) hypothesis. The analyses of NO2, Ox, falling dust particle, and SOx highlight a trilemma among the growth of industrial firms, infrastructure development, and reducing air-quality degradation. Nevertheless, for CO emission per capita, its logarithm has a general declining trend when plotted against both average firm size growth and paved road area expansion. This finding sheds light on the possibility of developing a regulatory framework that can harmonize a low-carbon society with industrial and infrastructure development.

scholarly journals Climate Change Driven Food Inflation in Ethiopia?

2018 ◽  
Vol 8 (1) ◽  
pp. 13-22
Author(s):  
Berhe Gebregewergs Hagos
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Average Price ◽  
Ceteris Paribus ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Model ◽  
Food Inflation

The research dealt with the relationships between temperature variability and price of food stuffs in Tigrai using 84 months collected time series data thereby applied a Univariate econometric tool and finite Distributed Lag Model in defining the variables and outcome of the study. As a result, the econometric regression analysis witnessed that a 1oC temperature rise contributed the average price of food stuffs such as barley price rose up by 80 percent, maize 186 percent, sorghum close to 275 percent, wheat 60 percent, and 170 percent in white Teff over the years, ceteris paribus.

scholarly journals Welfare Implication of Paddy Price Support Withdrawal from Malaysian Rice Sector: Partial Equilibrium Method Approach

2015 ◽  
Vol 48 (3-4) ◽  
pp. 45-52
Author(s):  
Haruna Suleiman Umar ◽  
Amin Mahir Abdullah ◽  
Mad Nasir Shamsudin ◽  
Zainal Abidin Mohamed
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Policy Option ◽  
Series Data ◽  
Farm Income ◽  
Price Support ◽  
Producer Price ◽  
Welfare Implication ◽  
Distributed Lag ◽  
Societal Welfare

Abstract The study was designed to analyze societal welfare implication of paddy price support withdrawal, as an alternative policy, from rice sector in Malaysia. Time series data (1980-2012) were collected and analyzed through different stages of analyses. The first stage of analysis involved time series econometric model namely, Auto Regressive Distributed Lag (ARDL), which was used in coefficients estimation. Estimated coefficients were subjected to, and passed the relevant diagnostic tests. The estimated elasticities were then used for the second stage of analysis- scenario simulation. Finally, the generated simulation results were further used in estimating the societal welfare changed through appropriate estimation technique. Results show producer welfare loss of about RM189 million, and RM198 million was saved as revenue. The net gain or societal welfare improvement was about RM9 million. Simulated results show up to 10% reduction in paddy producer price or farm income; this could serve as disincentive to rice producers. Since the country is concerned about achieving rice self-sufficiency and rice food security, necessary precautionary measures have to be instituted to prevent farmers exit from paddy farming, by putting a concerted effort towards channeling the trickle-down benefit of societal welfare improvement, resulting from policy option, to rice producers particularly the dominant smallholder group.

scholarly journals A novel cross-validation strategy for artificial neural networks using distributed-lag environmental factors

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244094
Author(s):  
Chao-Yu Guo ◽  
Tse-Wei Liu ◽  
Yi-Hau Chen
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Time Series Data ◽  
Series Data ◽  
Prediction Errors ◽  
Statistical Regression ◽  
Ann Model ◽  
Distributed Lag ◽  
Minimum Number ◽  
Artificial Neural

In recent years, machine learning methods have been applied to various prediction scenarios in time-series data. However, some processing procedures such as cross-validation (CV) that rearrange the order of the longitudinal data might ruin the seriality and lead to a potentially biased outcome. Regarding this issue, a recent study investigated how different types of CV methods influence the predictive errors in conventional time-series data. Here, we examine a more complex distributed lag nonlinear model (DLNM), which has been widely used to assess the cumulative impacts of past exposures on the current health outcome. This research extends the DLNM into an artificial neural network (ANN) and investigates how the ANN model reacts to various CV schemes that result in different predictive biases. We also propose a newly designed permutation ratio to evaluate the performance of the CV in the ANN. This ratio mimics the concept of the R-square in conventional statistical regression models. The results show that as the complexity of the ANN increases, the predicted outcome becomes more stable, and the bias shows a decreasing trend. Among the different settings of hyperparameters, the novel strategy, Leave One Block Out Cross-Validation (LOBO-CV), demonstrated much better results, and the lowest mean square error was observed. The hyperparameters of the ANN trained by the LOBO-CV yielded the minimum number of prediction errors. The newly proposed permutation ratio indicates that LOBO-CV can contribute up to 34% of the prediction accuracy.

Non-Indian aid and growth nexus in Bhutan

2015 ◽  
Vol 14 (2) ◽  
pp. 117-129
Author(s):  
Jigme Nidup
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Foreign Aid ◽  
Time Series Data ◽  
Labour Force ◽  
Unit Root Test ◽  
Series Data ◽  
Content Type ◽  
Distributed Lag ◽  
The Impact

Purpose – The purpose of this paper is to investigate the impact of Non-Indian foreign aid on economic growth. In addition, this paper also investigates the importance of governance, policy and democratic institution in fostering economic growth. Planned development activities in Bhutan are mostly funded through external assistance, particularly from India. Bhutan also receives assistance from other bilateral and multilateral countries besides India. Design/methodology/approach – This study adopts the autoregressive distributed lag approach to cointegration using time-series data from 1982 to 2012. To ensure stationarity of data, the unit root test is conducted. Necessary diagnostic tests are also performed to confirm that the model does not violate regression assumptions. Findings – Findings indicate that Non-Indian foreign aid, governance and democracy are detrimental to economic growth. Policy and investment is found insignificant determinant. However, labour force and technology are found fostering economic growth. Research limitations/implications – Less number of observations restrained detailed analysis like the use of interactive terms between aid and governance, aid and policy to see its actual impact. Data on Indian aid could not be sourced from any documents. Those available were found only for few years restricting time series analysis. Originality/value – This study explored the impact of various determinants on economic growth in Bhutan. These findings provide useful insights for policymakers in Bhutan to make necessary decisions. The analysis also suggests future ground for research to those scholars and researchers.

scholarly journals IMPACT OF OFFICIAL DEVELOPMENT ASSISTANCE ON SUSTAINABLE DEVELOPMENT OF PAKISTAN

2021 ◽  
pp. 11-21
Keyword(s):  
Time Series ◽  
Sustainable Development ◽  
Time Series Data ◽  
Development Assistance ◽  
Official Development Assistance ◽  
Foreign Assistance ◽  
Series Data ◽  
Distributed Lag ◽  
Long Run ◽  
Adf Test

This research paper aims to find out the relationship between Official Development Assistance and sustainable development in Pakistan. Time series data was taken for the period of 42 years (1976 -2017). Sustainable Development is a dependent variable for which proxy variable of Adjusted Net Savings has been deployed. ODA (% of GNI), Inflation, Per Capita GDP and Trade (GDP %) have been used as explanatory variables. Augmented Dickey-Fuller Test has been applied to examine the nature of the data as time series data may contain unit root problems. ADF test confirms mixed order of integration for the selected variables, hence Autoregressive Distributed Lag (ARDL) Approach was applied to find out the long-run relationship among the considered variables. Estimation of Error Correction Regression resulted in a significant long-run relationship between ODA and Sustainable Development. ECM Regression also signifies the negative and significant value of the speed of adjustment term confirming that the model is stable and convergent towards the equilibrium. Overall results of this study confirm a positive and highly significant relationship between ODA and the measure of sustainable development in Pakistan. Therefore it is recommended that attention should be given to drawing on foreign assistance and it should be subject to the transparent and efficient practices applied in the Aid Allocation. It significantly improves the overall welfare of Pakistan.

Determinants of foreign and domestic non-listed real estate fund flows in India

2020 ◽  
Vol 38 (6) ◽  
pp. 503-524
Author(s):  
Ashish Gupta ◽  
Graeme Newell ◽  
Deepak Bajaj ◽  
Satya Mandal
Keyword(s):  
Time Series ◽  
Real Estate ◽  
Time Series Data ◽  
Real Estate Market ◽  
Series Data ◽  
Fund Flows ◽  
Content Type ◽  
Empirical Results ◽  
Distributed Lag ◽  
Macroeconomic Determinants

PurposeReal estate forms an important part of any economy and the investment in real estate, in turn, is impacted by the macroeconomic environment of that country. The purpose of the present research is to examine macroeconomic determinants of foreign and domestic non-listed real estate fund (NREF) flows and to examine whether they are similar or different for an emerging economy like India.Design/methodology/approachThe long and short-run cointegration between the time-series variables is estimated using the autoregressive distributed lag (ARDL) bounds test and error correction model (ECM) using quarterly data across the 2005–2017 period. ARDL is a suitable method for short time-series data.FindingsThe empirical results indicate that domestic NREF flows are positively and significantly impacted by real GDP and performance of listed real estate stocks (i.e. BSE realty index). Whereas, foreign NREF flows are positively and significantly impacted by the exchange rate, performance of listed real estate stocks and domestic NREF flows.Practical implicationsThe empirical results have significant implications for academicians, policy makers and real estate market practitioners. In the context of these results, some interesting insights are gained that would help in the implementation of the policies aimed toward increasing the fund flows in the real estate sector, which in turn would have a significant trickle-down effect on the Indian economy.Originality/valueThe existing literature looks at macroeconomic and other drivers of foreign investment in international real estate investments. However, there are very few studies on the determinants of domestic real estate investment flows and on determinants of NREFs' investment flows; particularly in emerging markets. The present study, in contrast, evaluates simultaneously the macroeconomic determinants of the domestic and foreign NREFs' investment flows in India. The ARDL and ECM method used has been applied for the first time to the study of NREFs.

Detecting true relationships in time series data with different orders of integration

2021 ◽  
pp. 1-13
Author(s):  
Peter K. Enns ◽  
Carolina Moehlecke ◽  
Christopher Wlezien
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Time Series Data ◽  
Proper Time ◽  
Series Data ◽  
Type I ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Left And Right ◽  
True Relationship

Abstract It is fairly well-known that proper time series analysis requires that estimated equations be balanced. Numerous scholars mistake this to mean that one cannot mix orders of integration. Previous studies have clarified the distinction between equation balance and having different orders of integration, and shown that mixing orders of integration does not increase the risk of type I error when using the general error correction/autoregressive distributed lag (GECM/ADL) models, that is, so long as equations are balanced (and other modeling assumptions are met). This paper builds on that research to assess the consequences for type II error when employing those models. Specifically, we consider cases where a true relationship exists, the left- and right-hand sides of the equation mix orders of integration, and the equation still is balanced. Using the asymptotic case, we find that the different orders of integration do not preclude identification of the true relationship using the GECM/ADL. We then highlight that estimation is trickier in practice, over finite time, as data sometimes do not reveal the underlying process. But, simulations show that even in these cases, researchers will typically draw accurate inferences as long as they select their models based on the observed characteristics of the data and test to be sure that standard model assumptions are met. We conclude by considering the implications for researchers analyzing or conducting simulations with time series data.

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