Analysis Of Economic Time Series Research Articles

Time series forecasting using amplitude-frequency analysis of STL components

The problem of economic time series analysis and forecasting using amplitude-frequency analysis of STL decomposition is considered. An amplitude-phase operator was chosen as an apparatus for extraction the series harmonic components, the advantages of which (compared to the Fourier transform) are: calculations speed, result accuracy, simplicity and interpretability of software implementation. The forecast quality was carried out using the MAPE metric. Significantly higher prediction quality was achieved compared to Facebook Prophet forecasting package.

Opioid Prescribing in Canada following the Legalization of Cannabis: A Clinical and Economic Time-Series Analysis.

On 17 October 2018 recreational cannabis became legal in Canada, thereby increasing access and reducing the stigma associated with its use for pain management. This study assessed total opioid prescribing volumes and expenditures prior to and following cannabis legalization. National monthly claims data for public and private payers were obtained from January 2016 to June 2019. The drugs evaluated consisted of morphine, codeine, fentanyl, hydrocodone, hydromorphone, meperidine, oxycodone, tramadol, and the non-opioids gabapentin and pregabalin. All opioid volumes were converted to a mean morphine equivalent dose (MED)/claim, which is analogous to a prescription from a physician. Gabapentin and pregabalin claims data were analyzed separately from the opioids. Time-series regression modelling was undertaken with dependent variables being mean MED/claim and total monthly spending. The slopes of the time-series curves were then compared pre- versus post-cannabis legalization. Over the 42-month period, the mean MED/claim declined within public plans (p < 0.001). However, the decline in MED/claim was 5.4 times greater in the period following legalization (22.3mg/claim post vs. 4.1mg/claim pre). Total monthly opioid spending was also reduced to a greater extent post legalization ($Can267,000 vs. $Can95,000 per month). The findings were similar for private drug plans; however, the absolute drop in opioid use was more pronounced (76.9 vs. 30.8mg/claim). Over the 42-month period, gabapentin and pregabalin usage also declined. Our findings support the hypothesis that easier access to cannabis for pain may reduce opioid use for both public and private drug plans.

Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method

PurposeSeasonal fluctuation interference often affects the relational analysis of economic time series. The main purpose of this paper is to propose a new grey relational model for relational analysis of seasonal time series and apply it to identify and eliminate the influence of seasonal fluctuation of retail sales of consumer goods in China.Design/methodology/approachFirst, the whole quarterly time series is divided into four groups by data grouping method. Each group only contains the time series data in the same quarter. Then, the new series of four-quarters are used to establish the grey correlation model and calculate its correlation coefficient. Finally, the correlation degree of factors in each group of data was calculated and sorted to determine its importance.FindingsThe data grouping method can effectively reflect the correlation between time series in different quarters and eliminate the influence of seasonal fluctuation.Practical implicationsIn this paper, the main factors influencing the quarterly fluctuations of retail sales of consumer goods in China are explored by using the grouped grey correlation model. The results show that the main factors are different from quarter to quarter: in the first quarter, the main factors are money supply, tax and per capita disposable income of rural residents. In the second quarter are money supply, fiscal expenditure and tax. In the third quarter are money supply, fiscal expenditure and per capita disposable income of rural residents. In the fourth quarter are money supply, fiscal expenditure and tax.Originality/valueThis paper successfully realizes the application of grey relational model in quarterly time series and extends the applicable scope of grey relational model.

Dynamic Model of Port Throughput's Influence on Regional Economy

Wang, R. and Tan, Q., 2019. Dynamic model of port throughput's influence on regional economy. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 811–816. Coconut Creek (Florida), ISSN 0749-0208.Port is the gathering point and hub of land-water transportation equipment and condition. Port throughput has a great influence on regional economy. This paper analyzes the dynamic model of port throughput affecting regional economy, and puts forward a dynamic model of port throughput affecting regional economy based on co-integration and error correction model. The dynamic model of port and regional economic development will be established by econometric analysis techniques such as co-integration and error correction model and Granger causality test. Co-integration analysis and error correction model are designed to solve the difficulties existing in the traditional econometric analysis technology for non-stationary economic time series analysis. After testing the cointegration relationship between variables, the long-term equilibrium relationship between variables is embodied by error correction model. The stability test of the regional economic growth evaluation model is carried out by using the correlation F test method to realize the recursive analysis of the influence of the port throughput on the regional economy. Finally, through the empirical analysis of the performance test, the study shows that coastal port construction and local economic construction promote each other, therefore, the development and construction of coastal ports should be fully combined with the hydrologic conditions of local waterways. And promote the development of coastal shipping according to local conditions, and should pay attention to the short-term fluctuation of the port, timely take measures, so as to ensure the steady development of coastal economy.

Size, correlations, and diversification: New evidence from an application of wavelet approach to the emerging Islamic mutual fund industry

Despite the rapid growth of Islamic finance in recent years, there has been relatively little work done on the emerging Islamic mutual fund industry within the broad field of Islamic finance. As far as the authors’ knowledge, this paper is the first attempt dedicated to understanding the correlation between different sizes of the young and rapidly growing Islamic mutual fund industry at different investment horizons. Major part of economic time series analysis is done in time or frequency domain separately. Wavelet analysis can combine these two fundamental approaches, so we can work in the time-frequency domain. Using wavelet coherence, we have gained valuable insights into the continuous dynamics of correlation between small, medium and large size Islamic mutual funds at different investment horizons for reaping the benefits of portfolio diversification in particular and keeping contagion risk at bay.

Mixed ℓ2 and ℓ1-norm regularization for adaptive detrending with ARMA modeling

In this paper, the problem of detrending a time series and/or estimating a wandering baseline is addressed. We propose a new methodology that adaptively minimizes different regularized cost functions by introducing an ARMA model of the underlying trend. Mixed ℓ1/ℓ2-norm penalty functions are taken into consideration and novel RLS and LMS solutions are derived for the model parameters estimation. The proposed methods are applied to typical trend estimation/removal problems that can be found in the analysis of economic time series or biomedical signal acquisition. Comparisons with standard noncausal filtering techniques are also presented.

The Balance of Payment of the KSA and Its Main Determinants Financial Economic Time Series Analysis from 1981 to 2007

This paper addresses the impact of different factors that are theoretically believed to have significant impact on the status of the balance of payment. The paper applies those theoretical concepts on the status of the balance of payment of Saudi Arabia from 1981 to 2007. The paper gives interesting implications in that regard for decision makers so they can address different implications on the status of the balance of payment of the country by incorporating endogenously different macro-variables addressed by the current paper while they take decisions regarding the improvement of the status of the balance of payment economically.

Common Feature Analysis of Economic Time Series: An Overview and Recent Developments

In this paper we overview the literature on common features analysis of economic time series. Starting from the seminal contributions by Engle and Kozicki (1993) and Vahid and Engle (1993), we present and discuss the various notions that have been proposed to detect and model common cyclical features in macroeconometrics. In particular, we analyze in details the link between common cyclical features and the reduced-rank regression model. We also illustrate similarities and differences between the common features methodology and other popular types of multivariate time series modelling. Finally, we discuss some recent developments in this area, such as the implications of common features for univariate time series models and the analysis of common autocorrelation in medium-large dimensional systems.

Common Feature Analysis of Economic Time Series: An Overview and Recent Developments

Common Feature Analysis of Economic Time Series: An Overview and Recent Developments

Multiscale analysis of economic time series by scale-dependent Lyapunov exponent

Economic time series usually exhibit complex behavior such as nonlinearity, fractal long-memory, and non-stationarity. Recently, considerable efforts have been made to detect chaos and fractal long-memory in finance. While evidence supporting fractal scaling in finance has been accumulating, it is now generally thought that financial time series may not be modeled by chaos or noisy chaos, since the estimated Lyapunov exponent (LE) is negative. A negative LE amounts to a negative Kolmogorov entropy, and thus implies simple regular dynamics of the economy. This is at odds with the general observation that the economy is highly complicated due to nonlinear and stochastic interactions among component systems and hierarchical regulations in the world economy. To resolve this dilemma, and to provide an effective means of characterizing fractal long-memory properties in non-stationary economic time series, we employ a multiscale complexity measure, the scale-dependent Lyapunov exponent (SDLE), to characterize economic time series. SDLE cannot only unambiguously distinguish low-dimensional chaos from noise, but also detect high-dimensional and intermittent chaos, as well as effectively deal with non-stationarity. With SDLE, we are able to show that the reported negative LE may correspond to large-scale convergence, but not imply the absence of small-scale divergence or noisy chaos in the world economy. Using US foreign exchange rate data as examples, we further show how SDLE can readily characterize fractal, persistent or anti-persistent long-range correlations in economic time series.

Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis

In this paper, we contribute to the literature on energy market co-movement by studying its dynamics in the time-frequency domain. The novelty of our approach lies in the application of wavelet tools to commodity market data. A major part of economic time series analysis is done in the time or frequency domain separately. Wavelet analysis combines these two fundamental approaches allowing study of the time series in the time-frequency domain. Using this framework, we propose a new, model-free way of estimating time-varying correlations. In the empirical analysis, we connect our approach to the dynamic conditional correlation approach of Engle (2002) on the main components of the energy sector. Namely, we use crude oil, gasoline, heating oil, and natural gas on a nearest-future basis over a period of approximately 16 and 1/2 years beginning on November 1, 1993 and ending on July 21, 2010. Using wavelet coherence, we uncover interesting dynamics of correlations between energy commodities in the time-frequency space.

Curriculum Vitae

Birth Date: September 4, 1934 Place of Birth: Swansea, Wales, UK Nationality: British Immigration Status: Permanent Resident Education: University of Nottingham, Ph.D. 1959; University of Nottingham, B.A. 1955 Honors and Awards: Major Research Interests: Research and Professional Experience: Published Interviews: Invited Named Lectures: Festschrift: “Cointegration, Causality, and Forecasting.” Editors Robert F. Engle and Halbert White, Oxford University Press, 1999. Collection of Papers: “Essays in Econometrics. Collected Papers of Clive W.J. Granger.” Two volumes edited by Eric Ghysels, Norman R. Swanson, and Mark W. Watson, Econometric Society Monographs, Cambridge University Press, 2001. PUBLICATIONS AND LITERATURE REFERENCES Books B.1. Spectral Analysis of Economic Time Series, in association with M. Hatanaka, Princeton University Press, October 1964. (French translation: “Analyse spectrale des séries temporelles en économie,” Dunod, Paris 1969.) Designated a ”Citation Classic” by the publishers of Citation Review, 1986. B.2....

Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major U.S. macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run volatility change as a recurrent structure change, while short-run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure-dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short-run regime switches and long-run structure changes in the U.S. macroeconomic data.

In memory of Clive Granger: an advisory board member of the journal

In memory of Clive Granger: an advisory board member of the journal

Old and New Spectral Techniques for Economic Time Series

This methodological paper reviews different spectral techniques well suitable to the analysis of economic time series. While econometric time series analysis is generally yielded in the time domain, these techniques propose a complementary approach based on the frequency domain. Spectral decomposition and time series reconstruction provide a precise quantitative and formal description of the main oscillatory components of a series: thus, it is possible to formally identify trends, low-frequency components, business cycles, seasonalities, etc. Since recent developments in spectral techniques allow to manage even with short noisy dataset, nonstationary processes, non purely periodic components these tools could be applied on economic datasets more widely than they nowadays are.

Measuring business cycles: A wavelet analysis of economic time series

Multiresolution wavelet analysis is a natural way to decompose an economic time series into trend, cycle, and noise. The method is illustrated with GDP data. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter.

Charles F. Roos, Harold T. Davis and the Quantitative Approach to Business Cycle Analysis at the Cowles Commission in the 1930s and early 1940s*

While much has been written about the history of the Cowles Commission for Research in Economics after its move from Colorado Springs to Chicago in 1939, its earlier history under the first two Cowles Commission research directors, Charles F. Roos and Harold T. Davis, is little known. This paper examines their quantitative approach to business analysis, notably in Roos' 1934 Cowles monograph on Dynamic Economics and Davis's 1941 books on The Analysis of Economic Time Series and The Theory of Econometrics.

Analysis of economic time series: effects of extremal observations on testing heteroscedastic components

AbstractMacroeconomic and financial time series are often tested for the presence of non‐linearity effects. Sometimes, small patches of extremal observations may wrongly influence non‐linearity tests. In this paper, a robust analysis of the Lagrange multiplier (LM) test for GARCH components is suggested. With Monte‐Carlo simulation we show that extreme observations might cause over‐estimation of the number of GARCH components, with the main contribution consisting by introducing the forward search method into the GARCH model family. Using robust estimators of regression coefficients and graphical displays of results, the effect of influential observations on estimates can be efficiently monitored. Analysing macroeconomic and financial time series we show that identifying the order of a GARCH model can be unduly influenced by a few isolated large values, and extremal observations affect p‐values and t‐statistics in an unexpected manner. Copyright © 2004 John Wiley &amp; Sons, Ltd.

Economic Time Series Forecasting: Box-Jenkins Methodology, Signal Processing and Neural Network Approach

This paper is devoted to the presentation of methods of economic time series analysis and modelling using the Box-Jenkins methodology, the signal processing approach and the feedforward neural network technique. Some results of our research on time series modelling with emphasis on potential improving forecast accuracy are presented here. The assessment of the particular models has been made using the root mean square error.

Generalized Method of Moments and Macroeconomics

We consider the contribution to the analysis of economic time series of the generalized method-of-moments estimator introduced by Hansen. We outline the theoretical contribution, conduct a small-scale literature survey, and discuss some ongoing theoretical research.