scholarly journals UNDERSTANDING INDONESIA’S MACROECONOMIC DATA: WHAT DO WE KNOW AND WHAT ARE THE IMPLICATIONS?

2018 ◽  
Vol 21 (2) ◽  
pp. 229-264 ◽  
Author(s):  
Susan Sunila Sharma
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Structural Breaks ◽  
Time Series Data ◽  
Unit Roots ◽  
Series Data ◽  
Econometric Modelling ◽  
Shock Response ◽  
Macroeconomic Time Series ◽  
Root Properties

Unit root properties of macroeconomic data are important for both econometric modelling specifications and policy making. The form of variables (whether they are a unit root process) helps determine the correct econometric modelling. Equally, the form of variables helps explain how they react to shocks (both internal and external). Macroeconomic time-series data are often at the forefront of shock analysis and econometric modelling. There is a growing emphasis on research on Indonesia using time-series data; yet, there is limited understanding of data characteristics and shock response of these data. Using an extensive dataset comprising 33 macroeconomic time-series variables, we provide an informative empirical analysis of unit root properties of data. We find that regardless of data frequencies the empirical evidence of unit roots is mixed, some series respond quickly to shocks others do take time, and almost every macroeconomic data suffers from structural breaks. We draw implications of these findings.

scholarly journals How to Identify Varying Lead–Lag Effects in Time Series Data: Implementation, Validation, and Application of the Generalized Causality Algorithm

Algorithms ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 95 ◽  
Author(s):  
Johannes Stübinger ◽  
Katharina Adler
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Structural Breaks ◽  
Time Series Data ◽  
Consumer Price Index ◽  
Real Data ◽  
Linear Mapping ◽  
Series Data ◽  
Lag Effects ◽  
Silver Metal

This paper develops the generalized causality algorithm and applies it to a multitude of data from the fields of economics and finance. Specifically, our parameter-free algorithm efficiently determines the optimal non-linear mapping and identifies varying lead–lag effects between two given time series. This procedure allows an elastic adjustment of the time axis to find similar but phase-shifted sequences—structural breaks in their relationship are also captured. A large-scale simulation study validates the outperformance in the vast majority of parameter constellations in terms of efficiency, robustness, and feasibility. Finally, the presented methodology is applied to real data from the areas of macroeconomics, finance, and metal. Highest similarity show the pairs of gross domestic product and consumer price index (macroeconomics), S&P 500 index and Deutscher Aktienindex (finance), as well as gold and silver (metal). In addition, the algorithm takes full use of its flexibility and identifies both various structural breaks and regime patterns over time, which are (partly) well documented in the literature.

scholarly journals Economic Growth, Inflation and Monetary Policy in Pakistan: Preliminary Empirical Estimates

2021 ◽  
Vol 4 (2) ◽  
pp. 321-333
Author(s):  
Hina Ali ◽  
Malka Liaquat ◽  
Noreen Safdar ◽  
Saeed ur Rahman
Keyword(s):  
Time Series ◽  
Monetary Policy ◽  
Unit Root ◽  
Time Series Data ◽  
Unit Root Test ◽  
Series Data ◽  
Economic Activities ◽  
Data Set ◽  
Explanatory Variables ◽  
Empirical Estimates

In economic policy, construction Inflation is a core variable to be considered that determines the economic activity. To make a suitable monetary policy, it is very essential to check the price level and later on, many other variables are considered to achieve the goal. This study aims to reveal the affiliation of inflation on the growth of economic activities in Pakistan. Time series data set for the period 1989-2020 was used to have the empirical estimates.  Augmented Dickey Fuller Unit Root Test is employed to check the unit root of the time series and Auto Regressive Distributive Lag techniques are used for empirical estimates. The present research uses Inflation as a dependent variable and Gross Domestic Product, Interest Rate, Money Supply, and Exchange Rate as the explanatory variables of the study. The findings of this analysis reveal that there's an antagonistic relation between Inflation and GDP.

Random order autoregressive time series model with structural break

2020 ◽  
Vol 15 (3) ◽  
pp. 225-237
Author(s):  
Saurabh Kumar ◽  
Jitendra Kumar ◽  
Vikas Kumar Sharma ◽  
Varun Agiwal
Keyword(s):  
Time Series ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Random Order ◽  
Real Data ◽  
Series Data ◽  
Model Parameters ◽  
Multiple Time ◽  
Multiple Time Points

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

Structural breaks and labor market disparities in the Canadian provinces

2015 ◽  
Vol 42 (2) ◽  
pp. 322-342 ◽  
Author(s):  
Firouz Fallahi ◽  
Gabriel Rodríguez
Keyword(s):  
Unemployment Rate ◽  
Unit Root ◽  
Structural Breaks ◽  
Time Series Data ◽  
Structural Break ◽  
Series Data ◽  
Stochastic Convergence ◽  
Content Type ◽  
Unemployment Rates ◽  
Canadian Provinces

Purpose – The purpose of this paper is to use quarterly time series data from Canada and the Canadian provinces to determine if the unemployment rates in the Canadian provinces are converging to the national rate of unemployment. Design/methodology/approach – First, the authors check for existence of stochastic convergence using recent unit root statistics, see Perron and Rodríguez (2003) and Rodríguez (2007). Second, the authors verify existence of convergence using methods proposed by Volgelsang (1998) and Bai and Perron (1998, 2003). All these methods allows for structural break(s) in the data. Findings – Results from different unit root tests, without and with structural breaks, confirm that stochastic convergence exists in all provinces. The other results show strong evidence that deterministic convergence exists and the unemployment rates of the Canadian provinces are converging to the unemployment rate of Canada. This conclusion is stronger when multiple breaks are allowed in the trend function using the approach of Bai and Perron (1998, 2003). Practical implications – Since the authors have verified the existence of stochastic convergence, any intervention in the labor markets of the Canadian provinces to control the provincial unemployment rate would have a temporary effect and these policies will not have a permanent influence on the unemployment rates. However, existence of β-convergence in the Canadian provinces shows that general policies toward lowering the national unemployment rate would decrease the provincial unemployment rates as well. Originality/value – To the best of the knowledge, the paper attempts to study the unemployment rate convergence in the Canadian provinces using the above-mentioned approaches. These approaches allow the authors to take into consideration the possibility of structural breaks in order to get results that are more accurate.

LONG MEMORY IN FINANCIAL TIME SERIES DATA WITH NON-GAUSSIAN DISTURBANCES

2003 ◽  
Vol 06 (02) ◽  
pp. 119-134 ◽  
Author(s):  
LUIS A. GIL-ALANA
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Unit Root ◽  
Time Series Data ◽  
Financial Time Series ◽  
Critical Values ◽  
Series Data ◽  
Finite Sample ◽  
Financial Time ◽  
Non Gaussian

In this article we propose the use of a version of the tests of Robinson [32] for testing unit and fractional roots in financial time series data. The tests have a standard null limit distribution and they are the most efficient ones in the context of Gaussian disturbances. We compute finite sample critical values based on non-Gaussian disturbances and the power properties of the tests are compared when using both, the asymptotic and the finite-sample (Gaussian and non-Gaussian) critical values. The tests are applied to the monthly structure of several stock market indexes and the results show that the if the underlying I(0) disturbances are white noise, the confidence intervals include the unit root; however, if they are autocorrelated, the unit root is rejected in favour of smaller degrees of integration. Using t-distributed critical values, the confidence intervals for the non-rejection values are generally narrower than with the asymptotic or than with the Gaussian finite-sample ones, suggesting that they may better describe the time series behaviour of the data examined.

scholarly journals Tax Elasticity and Progressivity for Turkish Tax System

2019 ◽  
Author(s):  
Fethiye Burcu Turkmen - Ceylan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
National Accounts ◽  
Tax Progressivity ◽  
Statistical Institute ◽  
Turkish Economy ◽  
First Results ◽  
Macroeconomic Time Series ◽  
Phd Thesis

I mainly used macroeconomic (time-series data) obtained from Central Bank of Republic of Turkey and General Directorate of Budget and Fiscal Control. The data is open to public and can be obtained free of charge. While the former institution provided GDP data derived from national accounts, the latter institution provided the tax revenue figures from 1924 onward. I also benefited from household budget survey data provided free of charge and open to public by Turkstat (official statistical institute). <div><br></div><div>My main method is time-series econometrics. I employed ARDL and FMOLS methods to derive figures which are used to calculate tax elasticity and to analyse tax progressivity. I adopted approach provided by Kakinaka et al (2006) to measure tax progressivity for Turkish economy. </div><div><br></div><div>The paper presents preliminary results of an ongoing project of the author. The first results were presented in her PhD thesis previously.</div><div><br></div><div><br></div>

scholarly journals Tinjauan terhadap Metode Ekonometrika Lanjutan

2003 ◽  
Vol 4 (1) ◽  
pp. 59-74
Author(s):  
Telisa Aulia Falianty
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Panel Data ◽  
Unit Root ◽  
Time Series Data ◽  
Random Effect ◽  
Unit Root Test ◽  
Cointegration Analysis ◽  
Series Data ◽  
Theory Of Evolution

Econometric models have been played an increasingly important role in empirical analysis in economics. This paper provides an overview on some advanced econometric methods that increasingly used in empirical studies.A panel data combines features of both time series and cross section data. Because of increasing availability of panel data in economic sciences, panel data regression models are being increasingly used by researcher. Related to panel data model, there are some methods that will be discussed here such as fixed effect and random effect. A new approach to panel data that developed by Im, Shin, and Pesaran (2002) for testing unit root in heterogenous panel is included in this overview.When we work with time series data, there are many problems that we must handle, most of them are unit root test, cointegration among non stationary variables, and autoregressive conditional heteroscedasticity. Provided these problems, author also review about ADF and Philips-Perron test. An approch to cointegration analysis developed by Pesaran (1999), ARCH and GARCH model are also interesting to be discussed here.Bayesian econometric, that less known than classical econometric, is includcd in this overview. The genctic algorithm, a relatively new method in econometric, has bcen increasingly employed the behavior of economic agents in macroeconomic models. The genetic algorithm is based on thc process of Darwin’s Theory of Evolution. By starting with a set of potential solutions and changing them during several iterations, the Genetic Algorithm hopes to converge on the most ‘fit’ solutions.

Time Series Analysis and Structural Change Detection

2010 ◽  
pp. 377-395
Author(s):  
Kwok Pan Pang
Keyword(s):  
Time Series ◽  
Structural Change ◽  
Time Series Analysis ◽  
Change Detection ◽  
Structural Breaks ◽  
Time Series Data ◽  
Series Data ◽  
Series Analysis ◽  
Us Economy ◽  
Over Time

Most research on time series analysis and forecasting is normally based on the assumption of no structural change, which implies that the mean and the variance of the parameter in the time series model are constant over time. However, when structural change occurs in the data, the time series analysis methods based on the assumption of no structural change will no longer be appropriate; and thus there emerges another approach to solving the problem of structural change. Almost all time series analysis or forecasting methods always assume that the structure is consistent and stable over time, and all available data will be used for the time series prediction and analysis. When any structural change occurs in the middle of time series data, any analysis result and forecasting drawn from full data set will be misleading. Structural change is quite common in the real world. In the study of a very large set of macroeconomic time series that represent the ‘fundamentals’ of the US economy, Stock and Watson (1996) has found evidence of structural instability in the majority of the series. Besides, ignoring structural change reduces the prediction accuracy. Persaran and Timmermann (2003), Hansen (2001) and Clement and Hendry (1998, 1999) showed that structural change is pervasive in time series data, ignoring structural breaks which often occur in time series significantly reduces the accuracy of the forecast, and results in misleading or wrong conclusions. This chapter mainly focuses on introducing the most common time series methods. The author highlights the problems when applying to most real situations with structural changes, briefly introduce some existing structural change methods, and demonstrate how to apply structural change detection in time series decomposition.

scholarly journals HOW TO ESTIMATE AUTOREGRESSIVE ROOTS NEAR UNITY

2001 ◽  
Vol 17 (1) ◽  
pp. 29-69 ◽  
Author(s):  
Peter C.B. Phillips ◽  
Hyungsik Roger Moon ◽  
Zhijie Xiao
Keyword(s):  
Time Series ◽  
Unit Root ◽  
Time Series Data ◽  
Diffusion Processes ◽  
Rates Of Convergence ◽  
Series Data ◽  
New Class ◽  
Power Rate ◽  
Integrated Time Series ◽  
Conventional Unit

A new model of near integration is formulated in which the local to unity parameter is identifiable and consistently estimable with time series data. The properties of the model are investigated, new functional laws for near integrated time series are obtained that lead to mixed diffusion processes, and consistent estimators of the localizing parameter are constructed. The model provides a more complete interface between I(0) and I(1) models than the traditional local to unity model and leads to autoregressive coefficient estimates with rates of convergence that vary continuously between the O(√n) rate of stationary autoregression, the O(n) rate of unit root regression, and the power rate of explosive autoregression. Models with deterministic trends are also considered, least squares trend regression is shown to be efficient, and consistent estimates of the localizing parameter are obtained for this case also. Conventional unit root tests are shown to be consistent against local alternatives in the new class.

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