3,925 research outputs found
Tools for non-linear time series forecasting in economics - an empirical comparison of regime switching vector autoregressive models and recurrent neural networks
No full textThe purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neu-ral network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly and annual forecast hori-zons. Both non-linear models perform significantly better than the VAR model. Keywords: Inflation forecasting, regime-switching vector autoregressive model, recurrent neural network
Co-evolving neural networks with evolutionary strategies: a new application to Divisia money
No full textThis work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis-à-vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.</p
Co-evolution vs. Neural Networks; An Evaluation of UK Risky Money
No full textThe performance of a "capital certain" Divisia index constructed using the same components included in the Bank of England"s MSI plus national savings; a "risky" Divisia index constructed by adding bonds, shares and unit trusts to the list of assets included in the first index; and a capital certain simple sum index for comparison is compared. nce suggests that co-evolutionary strategies are superior to neural networks in the majority of cases. The risky money index performs at least as well as the Bank of England Divisia index when combined with interest rate information. Notably, the provision of long term interest rates improves the out-of-sample forecasting performance of the Bank of England Divisia index in all cases examinedEvolutionary Strategies, Risk Adjusted Divisia, Inflation, Neural Networks
The SSC of the Generalised Jahangir’s Graph Jm,k and its Algebraic Characterizations
No full textIn this article, we present important combinatorial and algebraicproperties of spanning simplicial complex (SSC) of the generalised Jahangir’sgraph Jm,k. We describe the relation to find f−vectors associatedto Δs(Jm,k) and determine the Hilbert series for the SR-ring KΔs(Jm,k).In the end, we present the associated primes of the facet ideal IF(Δs(Jm,k))and the Cohen-Macaulay characterization of the SR-ring of Δs(Jm,k).AMS (MOS) Subject Classification Codes: Primary 13-P10, Secondary 13-F20, 13-C14, 13-H10.Corresponding Author: Agha KashifKey Words: Simplicial Complexes, f-vectors, Spanning Trees, Face Ring, Hilbert Series, CohenMacaulay
To <i>JM</i> on Its 75th Anniversary
No full text This article discusses how Journal of Marketing ( JM) has influenced marketing science and practice by publishing articles on substantive topics relevant to customers, managers, organizations, markets, and society. The journal's 75th anniversary coincides with the 50th anniversary of the Marketing Science Institute (MSI). Frequently, JM and MSI have collaborated to address important substantive marketing issues identified in MSI's Research Priorities. The author highlights seminal articles on brand equity; business-to-business marketing (including sales force management); connecting marketing information, metrics, and strategy; consumer behavior; innovation, new product development. and product management; marketing orientation and capabilities; and market research, methodology and services. She also draws attention to articles that have won the Sheth Foundation/ JM Award and the H. Paul Root Award. The article describes how JM‘s knowledge dissemination is amplified by powerful social network effects. Ideas in JM articles diffuse through the business community, influencing the mind-set of managers worldwide. </jats:p
Does money matter in inflation forecasting?.
No full textThis paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation
Financial innovation in Taiwan: an application of neural networks to the broad monetary aggregates
No full textIn this paper a weighted index measure of money using the ‘Divisia’ formulation is constructed for the Taiwan economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. This research extends an earlier study by Gazely and Binner by examining the theory that rapid financial innovation, particularly during the financial liberalization of the 1980s, has been responsible for the poor performance of conventional simple sum monetary aggregates. The Divisia index is adjusted in two ways to allow for the major financial innovations that Taiwan has experienced since the 1970s. The technique of neural networks is used to allow a completely flexible mapping of the variables and a greater variety of functional form than is currently achievable using conventional econometric techniques. Results suggest that superior tracking of inflation is possible for networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money appear to offer advantages over their simple sum counter parts as macroeconomic indicators.</p
Estimation with Inequality Constraints on Parameters and Truncation of the Sampling Distribution.
No full textTheoretical constraints on economic model parameters often are in the form of inequality restrictions. For example, many theoretical results are in the form of monotonicity or nonnegativity restrictions. Inequality constraints can truncate sampling distributions of parameter estimators, so that asymptotic normality no longer is possible. Sampling theoretic asymptotic inference is thereby greatly complicated or compromised. We use numerical methods to investigate the resulting sampling properties of inequality-constrained estimators produced by popular methods of imposing inequality constraints, with particular emphasis on the method of squaring, which is the most widely used method in the applied literature on estimating integrable neoclassical systems of demand equations. See Barnett and Binner (2004).Asymptotics, truncated sampling distribution, nonidentified sign, inequality constraints, bootstrap, jackknife.
JM-20, a Benzodiazepine-Dihydropyridine Hybrid Molecule, Inhibits the Formation of Alpha-Synuclein-Aggregated Species
No full text\ua9 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Studies showed that JM-20, a benzodiazepine-dihydropyridine hybrid molecule, protects against rotenone and 6-hydroxydopamine neurotoxicity. However, its protective effects against cytotoxicity induced by endogenous neurotoxins involved in Parkinson’s disease (PD) pathogenesis have never been investigated. In this study, we evaluated the ability of JM-20 to inhibit alpha-synuclein (aSyn) aggregation. We also evaluated the interactions of JM-20 with aSyn by molecular docking and molecular dynamics and assessed the protective effect of JM-20 against aminochrome cytotoxicity. We demonstrated that JM-20 induced the formation of heterogeneous amyloid fibrils, which were innocuous to primary cultures of mesencephalic cells. Moreover, JM-20 reduced the average size of aSyn positive inclusions in H4 cells transfected with SynT wild-type and synphilin-1-V5, but not in HEK cells transfected with synphilin-1-GFP. In silico studies showed the interaction between JM-20 and the aSyn-binding site. Additionally, we showed that JM-20 protects SH-SY5Y cells against aminochrome cytotoxicity. These results reinforce the potential of JM-20 as a neuroprotective compound for PD and suggest aSyn as a molecular target for JM-20
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