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Book part
Publication date: 18 January 2022

Francis X. Diebold and Glenn D. Rudebusch

Climate change is a massive multidimensional shift. Temperature shifts, in particular, have important implications for urbanization, agriculture, health, productivity, and…

Abstract

Climate change is a massive multidimensional shift. Temperature shifts, in particular, have important implications for urbanization, agriculture, health, productivity, and poverty, among other things. While much research has documented rising mean temperature levels, the authors also examine range-based measures of daily temperature volatility. Specifically, using data for select US cities over the past half-century, the authors compare the evolving time series dynamics of the average daily temperature (AVG) and the diurnal temperature range (DTR; the difference between the daily maximum and minimum temperatures). The authors characterize trend and seasonality in these two series using linear models with time-varying coefficients. These straightforward yet flexible approximations provide evidence of evolving DTR seasonality and stable AVG seasonality.

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Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

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Book part
Publication date: 1 September 2021

John L. Stanton and Stephen L. Baglione

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using…

Abstract

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.

Book part
Publication date: 13 March 2013

Youqin Pan, Terrance Pohlen and Saverio Manago

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict…

Abstract

Retail sales usually exhibit strong trend and seasonal patterns. Practitioners have typically used seasonal autoregressive integrated moving average (ARIMA) models to predict retail sales exhibiting these patterns. Due to economic instability, recent retail sales time-series data show a higher degree of variability and nonlinearity, which makes the ARIMA model less accurate. This chapter demonstrates the feasibility and potential of applying empirical mode decomposition (EMD) in forecasting aggregate retail sales. The hybrid forecasting method of integrating EMD and neural network (EMD-NN) models was applied to two real data sets from two different time periods. The one-period ahead forecasts for both time periods show that EMD-NN outperforms the classical NN model and seasonal ARIMA. In addition, the findings also indicate that EMD-NN can significantly improve forecasting performance during the periods in which macroeconomic conditions are more volatile.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

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Book part
Publication date: 6 February 2023

Asish Kumar Pal and Atanu Sengupta

It is recognised that environmental air pollution is one of the global problems and is a common problem for both developing as well as developed countries. In the era of…

Abstract

It is recognised that environmental air pollution is one of the global problems and is a common problem for both developing as well as developed countries. In the era of globalisation, it is the most important global environmental issue. In general, urban air quality is becoming vulnerable especially in the developing countries due to adopting various developmental schemes. Air pollution problem in Kolkata, capital city of West Bengal, is under serious for a long day. As per guidelines of World Health Organization, for residential areas, air pollution level in Kolkata is considerably higher than the standard enumerated. There are several types of air pollutants which are continuously exposing the air of Kolkata. West Bengal pollution control board (WBPCB) has been monitoring ambient air quality (AAQ) for the parameters viz. suspected particulate matters (SPM), respiratory particulate matters (RPM), sulphur dioxide (SO2), nitrogen dioxide (NO2) and lead (Pb) in Kolkata throughout the years. Present study has been designed to determine the vertical floor-wise air quality status of the city of Kolkata and the seasonal variation of the pollutants over the consecutive years from 2011 to 2017. It is demonstrated that the air pollution is the highest in the winter due to dry weather, second is festive season followed by winter due to heavy movement of vehicles and pedestrians for festival shoppings as well as pandel hoppings and then next is summer. But coming to the point of rainy season, this is the lowest due to wetted air or wind of monsoon. This chapter attempts to understand the long-run trend of air pollution as the periodical average value suggests.

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The Impact of Environmental Emissions and Aggregate Economic Activity on Industry: Theoretical and Empirical Perspectives
Type: Book
ISBN: 978-1-80382-577-9

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Book part
Publication date: 24 March 2006

Thomas B. Fomby and Dek Terrell

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive…

Abstract

The editors are pleased to offer the following papers to the reader in recognition and appreciation of the contributions to our literature made by Robert Engle and Sir Clive Granger, winners of the 2003 Nobel Prize in Economics. Please see the previous dedication page of this volume. The basic themes of this part of Volume 20 of Advances in Econometrics are time-varying betas of the capital asset pricing model, analysis of predictive densities of nonlinear models of stock returns, modeling multivariate dynamic correlations, flexible seasonal time series models, estimation of long-memory time series models, the application of the technique of boosting in volatility forecasting, the use of different time scales in Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) modeling, out-of-sample evaluation of the ‘Fed Model’ in stock price valuation, structural change as an alternative to long memory, the use of smooth transition autoregressions in stochastic volatility modeling, the analysis of the “balancedness” of regressions analyzing Taylor-type rules of the Fed Funds rate, a mixture-of-experts approach for the estimation of stochastic volatility, a modern assessment of Clive's first published paper on sunspot activity, and a new class of models of tail-dependence in time series subject to jumps. Of course, we are also pleased to include Rob's and Clive's remarks on their careers and their views on innovation in econometric theory and practice that were given at the Third Annual Advances in Econometrics Conference held at Louisiana State University, Baton Rouge, on November 5–7, 2004.

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Econometric Analysis of Financial and Economic Time Series
Type: Book
ISBN: 978-1-84950-388-4

Book part
Publication date: 1 January 2008

Arnold Zellner

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk…

Abstract

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

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Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

Book part
Publication date: 17 January 2009

Mark T. Leung, Rolando Quintana and An-Sing Chen

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of…

Abstract

Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-84855-548-8

Book part
Publication date: 13 March 2013

Virginia M. Miori, James Algeo, Brian Segulin and Dorothy Cimino Brown

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in…

Abstract

Evaluating pain and discomfort in animals is difficult at best. Veterinarians believe however, that they can establish a proxy for estimating levels of pain and discomfort in canines by observing variations in their activity levels. Sufficient research has been conducted to justify this assertion, but little has been conducted to analyze the volumes of activity data collected. We present the first of a series of analyses aimed at ultimately presenting an effective predictive tool for canine pain and discomfort levels. In this chapter, we perform analyses on a dataset of normal (control) dogs, containing almost 3 million records. The forecasting analyses incorporated multiple polynomial regression models with transcendental transformations and ARIMA models to provide effective determination and prediction of baseline normal canine activity levels.

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Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78190-331-5

Keywords

Book part
Publication date: 25 September 2020

Berna Kaçar and Huriye Gonca Diler

Introduction: Monetary policy resolutions issued by central banks play effective role in economy when accompanied with interest variable. In Keynesian approach to finance…

Abstract

Introduction: Monetary policy resolutions issued by central banks play effective role in economy when accompanied with interest variable. In Keynesian approach to finance, interest is treated as the main determinant underlying financial policy resolutions. Thus interest is a pivotal factor in monetary transmission mechanism. Tight monetary policy practices, essentially decreasing money supply, eventually lead to a slump in investments, total demand and national income due to the increase in real interest rates.

Objective: The aim of this study is to determine what type of effects do monetary policy practitioner in Turkey have on macroeconomic variables via the interest channel of monetary transmission mechanism.

Methodology: Based on this objective, variables that could help in unveiling CBT overnight interest rates, direct fixed capital investment (GSSO), real gross domestic product (RGDP), industry production index (SUE) and domestic producer price index (YUFE) variables and that could explain monetary functions of transmission mechanism’s interest channel were selected. For the variables constituting the research topic, collected data belong the period of 2003Q1–2018Q3.

Findings: In the study relation between the variables has been analyzed under two parts via harnessing Toda–Yamamoto casualty test. In the first part, results of Toda–Yamamoto causality test from RGDP, GSSO and interest rate (FO) variables have been presented. The results manifest that interest channel directly affects direct fixed capital investment and RGDP. Interest channel was found to be effective on these variables of the analysis. In the second part, Toda–Yamamoto causality test was harnessed for SUE, YUFE and FO variables. Interest channel did not provide a result that affected YUFE and SUE.

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Uncertainty and Challenges in Contemporary Economic Behaviour
Type: Book
ISBN: 978-1-80043-095-2

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Book part
Publication date: 24 March 2006

Zongwu Cai and Rong Chen

In this article, we propose a new class of flexible seasonal time series models to characterize the trend and seasonal variations. The proposed model consists of a common trend…

Abstract

In this article, we propose a new class of flexible seasonal time series models to characterize the trend and seasonal variations. The proposed model consists of a common trend function over periods and additive individual trend (seasonal effect) functions that are specific to each season within periods. A local linear approach is developed to estimate the trend and seasonal effect functions. The consistency and asymptotic normality of the proposed estimators, together with a consistent estimator of the asymptotic variance, are obtained under the α-mixing conditions and without specifying the error distribution. The proposed methodologies are illustrated with a simulated example and two economic and financial time series, which exhibit nonlinear and nonstationary behavior.

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Econometric Analysis of Financial and Economic Time Series
Type: Book
ISBN: 978-1-84950-388-4

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