Forecasting, Cointegration and Causality Analysis of Unemployment Using Time Series Models

Forecasting, Cointegration and Causality Analysis of Unemployment Using Time Series Models PDF Author: Muhammad Ullah
Publisher:
ISBN:
Category :
Languages : en
Pages : 23

Get Book Here

Book Description
One of the major issue for policy makers is handling with continues increase in the level of unemployment in Pakistan. Thus forecasting unemployment rate is imperative to policy makers. This study aims to explore the best forecasting model among ARIMA, ARFIMA and exponential smoothing for forecasting unemployment. Secondly this study analyzed unemployment using time series techniques, measured long & short run relationship with population growth, labor force participation rate and crop production, and also investigated the causality between unemployment and other variables. Time series data ranging from 1965 to 2014 is collected from Pakistan Economic Survey for analysis. This study evaluate the forecasting performance of three models by using the forecast accuracy criterion such mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil's U statistics. Double Exponential Smoothing model is chosen as a best forecasted model for unemployment rate on the basis of forecast accuracy criterion. Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test is used for checking stationarity in the variables. At level the variables were non stationary and become stationary at first difference. The results of Johnson cointegration and Vector Error Correction model (VECM) indicated that there exists long & short run cointegration relationship between unemployment rate and other variables. Granger Causality test shows bi-directional causality running from crop production toward population growth.

Forecasting, Cointegration and Causality Analysis of Unemployment Using Time Series Models

Forecasting, Cointegration and Causality Analysis of Unemployment Using Time Series Models PDF Author: Muhammad Ullah
Publisher:
ISBN:
Category :
Languages : en
Pages : 23

Get Book Here

Book Description
One of the major issue for policy makers is handling with continues increase in the level of unemployment in Pakistan. Thus forecasting unemployment rate is imperative to policy makers. This study aims to explore the best forecasting model among ARIMA, ARFIMA and exponential smoothing for forecasting unemployment. Secondly this study analyzed unemployment using time series techniques, measured long & short run relationship with population growth, labor force participation rate and crop production, and also investigated the causality between unemployment and other variables. Time series data ranging from 1965 to 2014 is collected from Pakistan Economic Survey for analysis. This study evaluate the forecasting performance of three models by using the forecast accuracy criterion such mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and Theil's U statistics. Double Exponential Smoothing model is chosen as a best forecasted model for unemployment rate on the basis of forecast accuracy criterion. Augmented Dickey Fuller (ADF) and Phillips-Perron (PP) test is used for checking stationarity in the variables. At level the variables were non stationary and become stationary at first difference. The results of Johnson cointegration and Vector Error Correction model (VECM) indicated that there exists long & short run cointegration relationship between unemployment rate and other variables. Granger Causality test shows bi-directional causality running from crop production toward population growth.

Cointegration, Causality, and Forecasting

Cointegration, Causality, and Forecasting PDF Author: Halbert White
Publisher: Oxford University Press, USA
ISBN: 9780198296836
Category : Business & Economics
Languages : en
Pages : 512

Get Book Here

Book Description
A collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with and/or studied with both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

Automatic Time Series Modelling and Forecasting

Automatic Time Series Modelling and Forecasting PDF Author: John Guerard
Publisher:
ISBN:
Category :
Languages : en
Pages : 30

Get Book Here

Book Description
We test and report on time series modelling and forecasting using several U.S. Leading Economic Indicators (LEI) as an input to forecasting real U.S. GDP and the unemployment rate. These time series have been addressed before, but our results are more statistically significant using more recently developed time series modelling techniques and software. Montgomery, Zarnowitz, Tsay, and Tiao (1998) modeled the U.S. unemployment rate as a function of the weekly unemployment claims time series, 1948 - 1992. In this replication case study, we apply the Hendry and Doornik automatic time series PC-Give (AutoMetrics) methodology to the well-studied macroeconomics series, U.S. real GDP and the unemployment rate. The Autometrics system substantially reduces regression sum of squares measures relative to traditional variations on the random walk with drift model. The LEI are a statistically significant input to real GDP. A similar conclusion is found for the impact of the LEI and weekly unemployment claims series leading the unemployment rate series. We tested the forecasting ability of best univariate and best bivariate models over 60- and 120-period rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA-AR and the adaptive learning forecast, ADL, produced the smallest root mean square errors and lowest mean absolute errors.

Unemployment Variation Over the Business Cycles

Unemployment Variation Over the Business Cycles PDF Author: Saeed Moshiri
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Get Book Here

Book Description
Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non-linear and, therefore, the use of linear models to explain their behavior and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non-linearity in the unemployment series. Only recently have there been some developments in applying non-linear models to estimate and forecast unemployment rates. A major concern of non-linear modeling is the model specification problem; it is very hard to test all possible non-linear specifications, and to select the most appropriate specification for a particular model. Artificial Neural Network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non-linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back-propagation model and a generalized regression neural network model to estimate and forecast postwar aggregate unemployment rates in the US, Canada, UK, France, and Japan. We compare the out-of-sample forecast results obtained by the ANN models with those obtained by several linear and non-linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases, better than, the other univariate econometrics time series models in our test.

Analysis of Integrated and Cointegrated Time Series with R

Analysis of Integrated and Cointegrated Time Series with R PDF Author: Bernhard Pfaff
Publisher: Springer Science & Business Media
ISBN: 0387759670
Category : Business & Economics
Languages : en
Pages : 193

Get Book Here

Book Description
This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.

Forecasting Economic Time Series

Forecasting Economic Time Series PDF Author: Michael Clements
Publisher: Cambridge University Press
ISBN: 9780521634809
Category : Business & Economics
Languages : en
Pages : 402

Get Book Here

Book Description
This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.

Applied Econometrics with R

Applied Econometrics with R PDF Author: Christian Kleiber
Publisher: Springer Science & Business Media
ISBN: 0387773185
Category : Business & Economics
Languages : en
Pages : 229

Get Book Here

Book Description
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Forecasting the Swedish Unemployment Rate

Forecasting the Swedish Unemployment Rate PDF Author: Per-Olov Edlund
Publisher:
ISBN:
Category : Unemployment
Languages : en
Pages : 54

Get Book Here

Book Description


New Developments in Time Series Econometrics

New Developments in Time Series Econometrics PDF Author: Jean-Marie Dufour
Publisher: Physica
ISBN: 9783642487439
Category : Business & Economics
Languages : en
Pages : 250

Get Book Here

Book Description
This book contains eleven articles which provide empirical applications as well as theoretical extensions of some of the most exciting recent developments in time-series econometrics. The papers are grouped around three broad themes: (I) the modeling of multivariate times series; (II) the analysis of structural change; (III) seasonality and fractional integration. Since these themes are closely inter-related, several other topics covered are also worth stressing: vector autoregressive (VAR) models, cointegration and error-correction models, nonparametric methods in time series, and fractionally integrated models. Researchers and students interested in macroeconomic and empirical finance will find in this collection a remarkably representative sample of recent work in this area.

Forecasting, Causality and Cointegration Analysis Using Vector Autoregressions

Forecasting, Causality and Cointegration Analysis Using Vector Autoregressions PDF Author: Wojciech Charemza
Publisher:
ISBN:
Category : Economics
Languages : en
Pages :

Get Book Here

Book Description