Time series Econometrics Research Papers (original) (raw)

In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.

- by
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- Computer Science, Econometrics, Time Series, Time series Econometrics

The main purpose of this research paper is to explore and understand the nature of association and the possible existence of a short run and long run relationship between US Dollar, EURO, British Pound and Japanese Yen. To find out the relationship among currencies USD/INR, EUR/INR, GBP/INR and JPY/INR pairs are considered. The main idea is to know how these selected indicators are related to each other. The daily basis 2781 observations for all four variables from year 2007 to 2018 are taken into consideration. Data are collected from website of Reserve Bank of India. The stationarity of time series is checked and differentiated as per requirement. Johansen co-integration test to know the long run relationship between variables is used. The result shows that there is no co-integration equation among the variables. The short run relationship is examined with help of Vector Auto-regression (VAR) model and the short run relationship within different lags of variables has been identifi...

- by Rajesh Sadhwani
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- Management, Finance, Econometrics, Macroeconomics

Gold has a unique status in the economic world: a precious metal with wide uses and the measure of economic power of nations and the cornerstone of international monetary regimes. It has provided an important store of wealth to diverse investors, from individual to institutions, for centuries. It is an asset class and the foundation of a modern portfolio. In recent years, the world witnessed an aggressive growth in gold price. The role of gold in investment has drawn more attention since this transformational economic crisis began to unfold in 2008. This paper is an attempt to understand the price movement of gold. Can we find support for some popular opinions about gold on finance media? For instance: is gold a safe haven, a negative-beta asset, or an inflation hedge? How should we think about gold: a commodity or a currency? This paper provides some thoughts on these questions.

- by QTanalytics India
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- Finance, Economics, Time series Econometrics, Commerce and Management
- by mohammad mahdi banasaz
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- Time series Econometrics

Sustainable economic growth is desired to be achieved by governments targeting economic, social, and environmental benefits. The idea of circular economy model is to consider feedback effects from proper waste management instead of one-way effects typical with the classical linear model. Several sectors of society contribute to circular economy and its monetary and environmental outputs in a sustainable way. The aim of this paper is to analyze the dependencies and causalities of circular economy and economic developments in the EU. The research objectives include testing (i) whether research and development (R&D) expenditure, GDP per capita and generation of municipal waste per capita influence the recycling rate of municipal waste, and (ii) whether R&D expenditure, generation of municipal waste per capita and the recycling rate of municipal waste influence the GDP per capita. The relevant indicators are obtained from Eurostat. The research methods of fixed effects and Tobit approach are used to study the statistical relevance of the two models. The pairwise causality of variables is tested by Dumitrescu-Hurlin causality test. One result of the study is that technology development, by a decreasing life of products, leads to an increase of waste generation. Therefore, environmentally friendly technologies should be produced.

- by IRINA ALEXANDRA Georgescu and +1
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- Time series Econometrics

As previsões de carga média até dois meses à frente, com desagregação temporal semanal para o
primeiro mês, constituem informações fundamentais para o Programa Mensal de Operação Energética
- PMO formulado e executado pelo Operador Nacional do Sistema Elétrico – ONS, com a participação
dos agentes do setor elétrico. Visando agilizar e tornar reprodutível o processo de previsão de carga
para o PMO, foi concebido um programa computacional, denominado PrevCargaPMO, que busca
automatizar as etapas das previsões de carga semanais e mensais, para o PMO e suas revisões
semanais. Assim, o PrevCargaPMO fornece previsões de carga semanais, até seis semanas à frente, e
mensais, até 2 meses à frente, para os 4 subsistemas (Sudeste, Sul, Nordeste e Norte). Nas previsões
semanais, para a previsão da primeira semana são consideradas previsões de temperatura, histórico da
carga semanal verificada e variáveis de calendário (feriados, dias especiais, mês e horário de verão). Já
para as demais semanas do horizonte de previsão, as previsões de carga não contam com previsões de
temperatura. As previsões semanais são calculadas por meio de seis Máquinas de Vetor de Suporte -
SVM, uma para cada semana do horizonte de previsão. Adicionalmente, há uma SVM para previsão
da carga mensal até dois meses à frente, cujas variáveis explicativas incluem o histórico da carga
mensal verificada e as variáveis de calendário. Ao final, as previsões semanais e a previsão mensal
para o primeiro mês passam por uma etapa de compatibilização. O presente artigo tem por objetivo
descrever a metodologia e as principais funcionalidades implementadas no programa PrevCargaPMO
e apresentar alguns resultados obtidos para os quatro subsistemas (Sudeste, Sul, Nordeste e Norte) do
Sistema Interligado Nacional – SIN.

Consumer Price Index (CPI) is an important indicator used to determine inflation. The main objective of this research was to compare the forecasting ability of two time-series models using Zambia Monthly Consumer Price Index. We used monthly CPI data which were collected from January 2003 to December 2017. The models that were compared are the Autoregressive Integrated Moving average (ARIMA) model and Multicointegration (ECM) model. Results show that the ECM was the best fit model of CPI in Zambia since it showed smallest errors measures. Lastly, a forecast was done using the ECM and results show an average growth rate for food CPI at 6.63% and an average growth rate for nonfood CPI at 7.41%. Forecasting CPI is an important factor for any economy because it is essential in economic planning for the future. Hence, identifying a more accurate forecasting model is a major contribution to the development of Zambia.

- by Alick Banda
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- Economics, Statistics, Applied Statistics, Time series Econometrics

This paper analyzes relationship between external debt and economic growth. Data collections are mainly secondary over the period of 1980 to 2010. The study hypothesized negative relationship between external debt; debt servicing and economic growth. Collected data were regressed using OLS technique and Augmented Dickey Fuller to test for the stationarity of the variables. Findings indicate a negative relationship between external debt and economic growth while that of debt servicing conforms with the apriori expectation of positive relationship. Hence, it is therefore recommended that Nigeria has to narrow down its international trade in order to save its balance of payment (BOP) to meet debt servicing needs of the country. The policy makers should also create credibility including political will in order to spur investor confidence for both local and foreign investments.

- by Taiwo Muritala
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- Economics, Financial Economics, Time series Econometrics

Numerous time series models are available for forecasting economic output. Autoregressive models were initially applied to US gross national product (GNP), and have been extended to nonlinear structures, such as the self-exciting threshold autoregressive (SETAR) and Markov-switching autoregressive (MS-AR) models. We show that while these nonlinear models fit the in-sample data better than linear models, the outof-sample forecast performance of extremely simple methods, such as the unconditional mean is competitive compared with both previously published linear and nonlinear models for GNP time series. Motivated by Occam′s razor and the forecasting competitiveness of the unconditional mean, we seek parsimonious models which are based on simple assumptions, incorporate few parameters, and can generate accurate forecasts. We propose nonlinear and nonparametric models based on nearest neighbor and kernel regression by forming a state-space of time delayed GNP observations. The rationale of the proposed methodology lies in identification of local regions of state-space known as nearest neighbor sub-spaces, whereby we utilize future trajectories of the neighboring states and weight them using a double kernel estimator to generate density forecasts. The models proposed in this work incorporate only one or two parameters, and the model estimation framework relies on optimizing the performance of in-sample density forecasts. The proposed modeling approach does not require prior assumptions regarding the form of the error distribution, and can provide transition between regimes of growth and contraction. We compare the forecasts from proposed models with classical time series models for GNP and a range of benchmarks, and validate our results on two postwar GNP time series using different performance scores, such as the root mean squared error (RMSE), mean absolute error (MAE), and the continuous ranked probability score (CRPS). We find that proposed models consistently outperformed previously published models for point and density forecasts of GNP at varying horizons.

- by siddharth arora
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- Mathematics, Applied Mathematics, Computer Science, Econometrics

Time series forecasting models for tourist arrivals to the Philippines from the top 12 source countries are empirically developed in this paper. Together with a reliable procedure of modeling background noise, this study employed a modeling framework which takes into account influential events that impact on the level and direction of arrival series. From this framework, the study was able to establish twelve time series models for the monthly incoming tourism traffic from the top tourist sending countries to the Philippines for use in predicting future arrival scenerios.

- by Cesar C Rufino
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- Tourism Studies, Forecasting, Time series Econometrics, Tourism

In recent decades, there has been a growing literature dealing with the empirical estimation of the rate of profit and other Marxian variables in several countries. Nonetheless, there has been a paucity of econometric research about the impact of those Marxian variables on the growth rate in developing countries. This article seeks to evaluate the rate of profit and the rate of accumulation as determinants of the growth rate in Colombia during 1967–2019, using a generalized vector autoregressive model. We find that both variables are statistically significant and, in concordance with Marxian theory predictions, affect positively the growth rate. We also identify direct impacts of the growth rate over the profit rate and the accumulation rate as well as an economic relevant relationship between these latter variables. JEL Classification: B51, C22, O54

In this chapter I consider ways in which contemporary graphical causal models can be extended to model systems with complex temporal dynamics. I propose that the present limitations of many contemporary causal models in representing such dynamics are a legacy of their original application to simultaneous equations for long-term equilibrium relationships. Iwasaki and Simon (1994) provide a way of generalizing causal models to represent the dynamics of systems that are away from equilibrium. I clarify the temporal relationships among the variables in dynamic causal models, and illustrate how these relationships relate to those studied by time-series econometricians.

- by Naftali Weinberger
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- Econometrics, Philosophy of Science, Dynamical Systems, Time Series

Economists have investigated the relationship between output and export in order to explain economic growth for long years. Numerous studies have found very close correspondence between the growth of output and export. It is commonly known that Thirlwall's papers indicate very tight relationship between the growth of output and the ratio of the growth of exports to the income elasticity of demand for imports. This
paper aims to apply Thirlwall's balance-of-payments-constrained (BPC) model for the Turkish economy for 1968–2011 period. This research also evaluates the procedures of testing Thirlwall's principle by estimation of the income elasticity of demand for imports using the test of stationarity and cointegration methods. The findings are in accordance with the Harrod–Thirlwall growth model. The test results of Johansen cointegration
procedure and the comments on these results are presented as well.

- by Atilla Gökçe
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- Economics, Development Economics, International Economics, Econometrics

This paper has examined short run causality between government expenditure and GDP in India during 1951-2013 using a Toda-Yamamoto (1995) modified Granger causality approach under VAR environment. Exponentially detrended annual time series data on GDP and government expenditure at constant prices are used. Structural break point unit root tests are conducted besides the usual unit root tests to determine the order of integration of each variable. Tests for structural breaks reveal significant breaks in both time series around the period 2001-04. Government expenditure is found to significantly Granger-cause real GDP but the converse is insignificant implying that Wagner's law is inapplicable. The study thus suggests uni-directional causality from government expenditure to GDP. Moreover government expenditure in India has a long-run co-integrating relationship with real GDP and therefore short run causal relations may be anticipated. 1. Introduction and Objectives The association...

- by Ritwik Mazumder
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- Economics, Time series Econometrics

Latin America forests in the past decades has finally seen an easing in deforestation rates. This data, although hiding heterogeneous trends, suggests that this region may have embarked on the road to a forest transition (FT). Therefore, this paper empirically investigates the existence of a FT for Latin America through a specific Environmental Kuznets Curve (EKC) perspective. It relies on a panel of 21 countries and on novel satellite source characterized by a long-time coverage (1982–2015), hence particularly suitable to investigate the long-run dynamics between forest cover and economic development. Countries are clustered into two major groups according to the FT stage they are in. Results suggest the existence of a FT for Latin America along its economic development. Countries in early- and pre-transition stages have a U-shape relationship with a turning point at US$ 7,150. Conversely, countries in late- and post-transition stages show an opposite curve, albeit with a far high turning point, suggesting how the relationship after the first turning point will continue to be positive. The paper also conducts the analysis by comparing different forest cover sources showing how results may differ, hence representing a concern for any proper policy intervention. About this issue and the implication of the FT the paper reflects further in the conclusion.

The issue of the interaction between economic growth, inflation and exchange rate in Kenya, has been the subject of this paper. In order to empirically analyze the relationship between these three variables in the kenyan context (from 1960 to 2020), SVAR and ARDL (with bounds test cointegration) models have been used in this study.This paper has mainly led to the following results : (i) according to Granger test, there are no significant
causal relationships between economic growth, inflation and exchange rate in Kenya; (ii) with short-term
theoretical restrictions on the SVAR model, a significant appreciation of the kenyan local currency would
cause the kenyan inflation rate to increase slightly; (iii) the kenyan exchange rate and inflation rate would
react positively to endogenous and exogenous shocks but negatively to real sector ones; (iv) the impulse responses of Kenya’s economic growth would be negative in the event of exogenous shocks and positive in the event of endogenous ones; (v) with an ARDL model, in the short run, a depreciation of the kenyan local currency by 10% would lead to a 15.18% increase in the kenyan inflation rate; (vi) the significant long-term relationship (cointegration with bounds) between inflation, economic growth and exchange rate in Kenya has shown that, a 10% depreciation of the kenyan Shilling would lead to a 7.01% rise of this country’s inflation; (vii) in the long run, the kenyan economy would have an inflationary memory (inflation would also depends on its past values).

- by Kevin Nansha
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- Economics, Time series Econometrics

Through our project, we want to investigate the effect of policy
uncertainty on the unemployment level and we expect to find a positive causal effect. An interesting debate has risen recently since many scholars argued that the economic policy uncertainty affecting the Euro-zone slowed down the recovery from the current crisis. Thus it is important to study which are the main variables affecting European unemployment, learning thus how to design policies aiming at improve the labour market conditions.

Ethiopia's sesame export earn percentage share in the total export had been rapid declining over the last decades while it was the second commodity in currency grossing of the country. The objective of this study was to examine the determinant factors of Ethiopia's sesame exports performance, in the aspect of export trade, by the use of a more realistic model approach, a panel gravity model. It used short panel data that cover 11 countries of consistent Ethiopia's sesame importers for the period of 13 years from 2002 to 2014. The panel unit root test of Levin-Lin-Chu was used for each variable and applied the first difference transformation for the variables that had a unit root. The random effect model results suggested that real gross domestic product of importing countries; Ethiopian real gross domestic product, real exchange rate and weighted distance were found to be the determinant factors of Ethiopia's sesame exports performance. The estimated results revealed that as real gross domestic product of importing countries increase by 1%, the flows of Ethiopia's sesame exports performance increase by 1.63%. Based on the finding results, the researcher recommends that the policy maker must adopt the policies that reduce the cost of shipping through improving the infrastructure for shipments sector and contract a free trade agreement with distant countries. The government should encourage the private sector to diversify their products and improving the quality of its products to increase the competitiveness the Ethiopian products in foreign markets. Keywords: Sesame exports Ethiopia Gravity model Fixed effect model Random effect model a murt.stat@gmail.com https://orcid.org/0000-0002-1226-3145 b

- by Murad Mohammed
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- Economics, Econometrics, Statistics, Biostatistics

The study examined the relationship between exchange rates movements and stock market capitalisation in Ghana using Johansen cointegration technique and vector error correction model with quarterly time series data covering the period of 1990 to 2013. The study found a negative and significant relationship between exchange rates and stock market capitalisation both in the long-run and in the short run suggesting that a depreciation of the Ghana cedi against the US dollar is inimical to the performance of the Ghana Stock Exchange (GSE) Market. In order to increase stock market capitalisation, the study first recommends that the Central Bank of Ghana, the Ministry of Finance and the Ministry of Agriculture should adopt policies that will boost the real side of the economy such as increasing agricultural productivity to ensure increase in foreign exchange earnings in the country and hence preventing the cedi from depreciating. JEL Classification: E44, F23, F31, G14

We have analyzed the short term and long term linkages between the sectoral indexes of
Bombay Stock Exchange in India by using the daily data on nine sectoral indexes for the period
23rd August 2004 to 31st June 2010. After confirming the same order of integration of the study
variables from the unit root test incorporating endogenously determined structural breaks,
structural cointegration test has been carried out followed by VECM , Impulse response functions
and variance decomposition analysis. The cointegration analysis results indicate that most of the
sectoral indexes in India are cointegrated with at least one of the other indexes indicating that the
sectoral indexes posses’ useful information about the movements of other indexes. This is
confirmed by the Impulse response function analysis also. The comovements between the
sectoral indices indicate that the Bombay stock exchange is not weak form efficient and the
possibility of sectoral portfolio diversification is limited

This project shows the algorithm appropriate for short term trading strategies and techniques to leverage these strategies. The Durbin Watson statistic shows market inefficiency. Non-parametric models are used. The returns are not correlated but they show some amount of volatility clusterization which triggered the use of GARCH family to forecast returns, and the exponential GARCH is used to capture any symmetric effect in the data.

- by solomon caulker
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- Finance, Economics, Econometrics, Financial Econometrics

This study examined the determinants of food price inflation and its impact on the economy of PNG from 2001 to 2011 (10 years) using time series data. Secondary data obtained from credible sources such as BPNG, ADB, WB, and IMF were analysed using various econometric test such as the Johansen Test for Cointergration, Augmented Dickey Fuller Test and the Vector Error Correction Model. The econometric tests revealed that there is a long run relationship (causality) between the variables real exchange rate and wheat price which significantly influenced food price inflation in PNG. There is a negative correlation between money supply and interest rate which negatively impacted food price inflation in the country. In the short run, real exchange rate, money supply, real interest rate and wheat price do not significantly affect food inflation as their corresponding p-values are greater than five percent (5%) critical value. Alternatively, there is a long run relationship or causality betw...

- by Stanis Hulahau
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- Applied Mathematics, Economics, Development Economics, Statistics

This paper introduces the notion of common noncausal features and proposes tools to detect them in multivariate time series models. We argue that the existence of co-movements might not be detected using the conventional stationary vector autoregressive (VAR) model as the common dynamics are present in the noncausal (i.e. forward-looking) component of the series. In particular, we show that the presence of a reduced rank structure allows to identify purely causal and noncausal
VAR processes of order two and higher even in the Gaussian likelihood framework. Hence, usual test statistics and canonical correlation analysis can still be applied, where both lags and leads are used as instruments to determine whether the common features are present in either the backward-or forward-looking dynamics of the series. The proposed definitions of co-movements also valid for the mixed causal-noncausal VAR, with the exception that an approximate non-Gaussian maximum likelihood estimator is necessary for these cases. This means however that one loses the benefits of the simple tools proposed in this paper. An empirical analysis on European Brent and U.S. West Texas Intermediate oil prices illustrates the main findings. Whereas we fail to find any short run co-movements in a conventional causal VAR, they are detected in the growth rates of the series when considering a purely noncausal VAR.

- by Gianluca Cubadda and +1
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- Time series Econometrics, Time series analysis

This study is an empirical evaluation of the dynamic effect of intermittent television ad placements on the sales of a consumer product using three classes of distributed lag models. The study is also geared to analytically determine the duration of advertising effects and the dependability of the firm’s pulsing type of advertising strategy. Empirical results support the soundness of the company’s strategy. Maximum duration of advertising effect is estimated at six months, which is about the largest number of consecutive months the product was not seen on TV during the sample period.

The following working document summarizes our work on the clustering of financial time series. It was written for a workshop on information geometry and its application for image and signal processing. This workshop brought several experts in pure and applied mathematics together with applied researchers from medical imaging, radar signal processing and finance. The authors belong to the latter group. This document was written as a long introduction to further development of geometric tools in financial applications such as risk or portfolio analysis. Indeed, risk and portfolio analysis essentially rely on covariance matrices. Besides that the Gaussian assumption is known to be inaccurate , covariance matrices are difficult to estimate from empirical data. To filter noise from the empirical estimate, Mantegna proposed using hierarchical clustering. In this work, we first show that this procedure is statistically consistent. Then, we propose to use clustering with a much broader application than the filtering of empirical covariance matrices from the estimate correlation coefficients. To be able to do that, we need to obtain distances between the financial time series that incorporate all the available information in these cross-dependent random processes.

- by Gautier Marti and +2
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- Empirical Finance, Machine Learning, Computational Finance, Clustering and Classification Methods

We recreate the NIPA adjustments pioneered by Ruggles and Ruggles (1992) for the period 1947-2012 and reconrm their results: household net lending to other sectors is counter-cyclical and is a small fraction private rms' gross capital formation (GCF). To test the causal role of household Net Savings in terms of GCF and GDP growth, a VEC model is estimated. The VECM is cointegrated stationary for the three annual time series, but exogeneity testing shows household Net Savings is exogenous. We argue this is evidence of inter-secotral investment demand as driving feature of growth.

Abstract With a view to provide new evidence in favor of EKC hypothesis that claims a trade-off between growth and environmental quality at least in the short-run, we conduct a study for Singapore by analyzing the data on CO2 emissions, energy consumption (measured by two proxies) and per capita GDP for 1975–2011 by means of cointegration and causality techniques. The results indicate a significant rise in CO2 emissions as GDP rose over the years confirming a short-run trade-off between environment and growth. Further analysis on a possible turning point shows that continuous growth will be necessary for a long time before we experience any trickle-down effects on environmental pollution. The results of causality analysis indicate that CO2 emissions indeed have caused decline in Singapore's growth. It is therefore argued that strict regulatory regimes on environmental protection in the city–state must remain in force.

- by Francis Tan
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- Economics, Financial Econometrics, Tourism economics, Applied Econometrics

There is a relationship between the fiscal deficit and inflation, which was confirmed
empirically in several studies conducted in many countries. Sri Lanka has been encountering the
problem of inflation for the recent years. But in Sri Lanka, this proposition has not yet been studied
scientifically. Therefore, this study was going to fill this gap. The objective of this study was to test
the impact of fiscal deficit on inflation in Sri Lanka. For this study, the annual time series data were
used during the period of 1959 to 2013. The fiscal deficit, exchange rate, government expenditures
and import outflow were used as independent variables while the Colombo consumer price index
was considered as dependent variable which was proxy variable for inflation of Sri Lanka. In
addition, the multiple regressions model was used to test the impact of fiscal deficit on inflation.
Based on the regression results, the fiscal deficit preserved the positive relationship with inflation in
Sri Lanka at one percent significant level. Therefore, this study confirmed that the fiscal deficit
accelerates the inflation in Sri Lanka.

- by Ahamed Lebbe M Mohamed Aslam
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- Time series Econometrics

Nigeria has been faced with the macroeconomic problem of inflation for a long period of time. The problem slows down the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in Africa including Nigeria. Therefore, forecasting inflation rates in Nigeria becomes very important for the government to design economic strategies or effective monetary policies to combat any unexpected high inflation in the country. This study utilizes seasonal autoregressive integrated moving average model (SARIMA) to forecast inflation rates in Nigeria using monthly inflation data from January 1999 to December 2018. We discover that SARIMA (1, 1, 1) × (0, 0, 1)12 can represent very well in the data behavior and the forecasting of inflation rate in Nigeria. The research revealed that the inflation of Nigeria is non-stationary and based on the selected model, we forecast twelve (12) months inflation rates of Ni...

- by Oladapo Ifeoluwa
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- Economics, Econometrics, Statistics, Machine Learning

Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike's information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered "large" in size, AIC and FPE are recommended for the estimation the autoregressive lag length.

- by Marijan Vukadin
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- Time series Econometrics

1. Background and Introduction It is interesting to try to understand human beings' ways of economic thinking in modern societies. However, it is possible to realize this aim only if the approach of economic theory is based on the individual's or household perspective. Thus, the development of a microeconomic theory based on individual choices and preferences is essential for understanding of household saving behaviour. Although, the individual is the focus of the analysis, it is also necessary to acknowledge the fact that the household is the most important aspect of life for many individuals. Economic theory such as the Absolute Income Hypothesis by Keynes (1936), postulates that households' saving is the difference between households' income and consumption of goods and services. From the classical times, development economics has long recognized the importance of mobilization of household savings as a key component of domestic savings in developed and developing countries in order to achieve economic growth and development. It is purported that household savings, by facilitating the process of capital accumulation, ensures that economic growth and development are realized (Rostow, 1960). For instance, in the Harrod-Domar model, household savings and incremental capital –output ration jointly determine the economic growth of the economy (Afronia, 2011). However, this does not mean that raising household and domestic savings is enough to achieve economic growth and development. There was still need to investigate the determinants of household savings which provides the background motivation for this study with emphasis on Uganda. According to the World Bank (2010), a higher household savings as a component of gross domestic savings was an engine to growth in many countries in the past decades, since they financed higher rates of investment as well. Thus, policies to promote household savings have a central role to play in driving growth via investment in Sub-Saharan Africa. The question which arises is that " what determines household savings " ? This question was answered by this study with evidence from Uganda. Also, the fact that The Sub-Saharan African countries (Uganda inclusive) have for a long time been characterized by a worrisome problem of low savings rate motivated the study with specific focus on Uganda using a nationally representative UNHS (2009/2010) data set. 2. Objective of the Study The objective of the study was to investigate the determinants of household savings in Uganda. Abstract: The study examined the micro level determinants of household saving in Uganda using household level cross sectional data obtained from Uganda National Household Survey (2009/2010) conducted by Uganda Bureau of Statistics. Prior to the Ordinary Least Squares estimation, the study conducted preliminary analysis involving descriptive statistics and a correlation matrix. The results from the OLS estimation reveal that, Income was the main determinant explaining the cross-sectional variation of household savings in Uganda. The results show that household income, education of household head, spouse education, gender, age, and household location (living in urban areas) are factors positively and significantly influencing household saving. On the other hand, household size, marital status age square of household head and regional differences negatively and significantly influence household saving. Considering the income factor, one way to improve the saving level is by implementing policies that improve productivity and income of households. The government should also increase its funding of the education sector not only to primary (UPE) and secondary (USE) but also tertiary institutions but also to the adult education program that has been running for decades.

- by sawuya nakijoba
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- Health Economics, Applied Econometrics, Time series Econometrics, Macro Economics

How can we explain our consumption of cocoa? Is it the caffeine in this hot, stimulating drink that keeps us buying? But then again wouldn’t we substitute it for other hot, stimulating caffeine containing drinks like tea or coffee? Could sugar be a complementary product for cocoa? These are some of the interesting questions that are investigated and are attempted to be answered in this economic report.

- by ilker yoldas
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- Economic History, Economics, Econometrics, Applied Econometrics

The timing of individual states entering recession often differs from the United States as a whole. I identify state-level recessions in the Tenth Federal Reserve District and find that energy-producing states have more frequent (but... more

- by Jason Brown
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- Energy, Time series Econometrics, Regional Economics

This research aims to evaluate two econometric models to forecast imports and exports for the financial year (FY) 2020. For this purpose, we used the annual exports and imports data of Pakistan from FY2002 to FY2019. Thus, in this regard, we employed, and compared the results of two econometrics models such as Box Jenkins or Autoregressive Integrated Moving Average (ARIMA), and Auto-Regressive (AR) with seasonal dummies. For examining the precision of forecasting, we employed mean absolute error and root mean square error approaches. The findings of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) reveal that the ARIMA or Box Jenkins approach provides better accuracy of the forecast for the exports as compared to the AR model with dummies. However, Auto-Regressive (AR) model has demonstrated more precision for the imports as compared to the Box Jenkins model. Hence, the projected forecasting for the growth of export is 1.87 % for the FY2020 and projected forecasting for t...

This Instructor's Manual is designed to accompany the second edition of Walter Enders' Applied Econometric Time Series (AETS). As in the first edition, the text instructs by induction. The method is to take a simple example and build towards more general models and econometric procedures. A large number of examples are included in the body of each chapter. Many of the mathematical proofs are performed in the text and detailed examples of each estimation procedure are provided. The approach is one of learning-by-doing. As such, the mathematical questions and the suggested estimations at the end of each chapter are important. In addition, it is useful to have students perform the type of semester project described at the end of this manual.

- by MUHAMMAD ZUBAIR CHISHTI
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- Time series Econometrics

The main purpose of the research is to analyze the volatility nature of cryptocurrencies, and determine whether the volatility is increasing over time or decreasing, determine, whether they behave more like an asset or currency. Moreover, another important goal of research is to critically assess the possible implementations of cryptocurrencies in our lives and their potential to replace Fiat Monetary Systems. The GARCH (1 1) model was utilized to model the volatility of selected cryptocurrencies, fiat money and S&P500 index. The main findings of the research illustrate that the conditional volatility of cryptocurrencies is persistent except for Bitcoin and more severe compared to fiat currencies and S&P500 index. The results suggest that the examined cryptocurrencies do not fulfill the criteria of being the fiat replacement in terms of volatility and general currency requirements.

- by Sherzodbek Kirgizbaev and +1
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- Time series Econometrics, Volatility, Cryptocurrency

Ce cours est une introduction à la théorie des processus en temps discret, les modèles ARIMA, la méthode Box & Jenkins (1976) et la modélisation VAR.
Le but est d’introduire la notion de processus temporel et plus particulièrement la classe des processus ARMA qui sont particulièrement utiles pour décrire le comportement des séries temporelles univariées. En second lieu, nous s’intéressons à l’étude de plusieurs séries conjointement selon une modélisation VAR. Cette présentation suppose que l’on définisse au préalable un certain nombre de notions essentielles à l’analyse des séries temporelles, et en particulier la notion de stationnarité. L’étude des séries temporelles suppose, aussi, que l’on fasse au préalable un certain nombre de rappels en probabilité et en statistiques.

- by MONTASSAR Zayati
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- Financial Econometrics, Time series Econometrics

Agricultural development policies in India have aimed at reducing hunger, food insecurity, malnourishment and poverty at a rapid rate. The present work is designed with specific objectives to study the trend analysis of rice, wheat and total food grain in India for the period starting from 1950-2019. For stochastic trend model estimation, time series parametric regression models i.e. Linear model, Quadratic model, Exponential model, Logarithmic model, Auto Regressive Integrated Moving Average (ARIMA) and Auto Regressive Integrated Moving Average with explanatory variables (ARIMAX) were analyzed for estimating an appropriate econometric model to capture the trend of major food grain viz. rice, wheat, total food grain production and net availability of the country. Several goodness of fit criteria viz. Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and maximum R-squared values was worked out for finding best fitted models. Kolmogorov-Smirnov (K-S) test and Run-test were used to estimate the "Normality" and "Independence" of residuals of all data series respectively. By using the best fitted models, it was observed that the availability of rice (70.05 kg/year), wheat (70.73 kg/year) and total food grain (182.96 kg/year) will decrease in 2021 as comparatively to this year.

The introduction of R software into the statistical computing space has provided comprehensive language for managing and manipulating multidimensional data. Developing the capacity and skills of students and actuarial analysts is essential for actuarial practices. In this study, the use of R is proposed as a decision support tool for in the field of actuarial teaching and practice, as a complement to the existing excel and other existing platform. Count data was fitted using six regression models, out of which zero-inflated Poisson model is considered to be most suitable model for the count data based on model selection criteria; also procedure for reserving is demonstrated. It is expected that this would promote the use of R among academia and practitioners.

- by Dr. OLUMIDE S ADESINA and +1
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- Statistics, Time series Econometrics, Bayesian statistics & modelling, Applied Mathematics and Statistics

This study focused on investment decisions in comparing the performance of the single index model and mean variance optimization model in portfolio construction in Nigeria using the Nigeria Stock Exchange-20 Share Index from 2010-2017. The comparison is done by constructing portfolios using both the single index model and mean variance optimization model. The Sharpe ratio is used to determine which model is better, as indicated by the higher Sharpe ratio. The study adopted Visual Basic Programming code (VBA) and Microsoft Excel solver to establish the Mean Variance weights and Single Index figures. The study established that the mean variance optimization model is better at a Sharpe ratio of 0.7 when considering investments with long time horizon, whereas the single index model is better when considering investments with short time horizon at a Sharpe ratio of 0.6. The study also concludes that the mean variance optimization model is better if the investors are risk averse, while the single index model is better when considering investors who are risk lovers. The study recommends that risk averse investors are better of diversifying their portfolios to reduce the presence of idiosyncratic risks via MVO for a longer period investment, and for risk seeking investors the Single Index model will be a better option for investment less than 5 years period.