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We introduce Behavioral Learning Equilibria (BLE) into a multivariate linear framework and apply it to New Keynesian DSGE models. In a BLE, boundedly rational agents use simple, but optimal AR(1) forecasting rules whose parameters are consistent with the observed sample mean and autocorrelation of past data. We study the BLE concept in a standard 3-equation New Keynesian model and develop an estimation methodology for the canonical Smets and Wouters (2007) model. A horse race between Rational Expectations (REE), BLE, and constant gain learning models shows that the BLE model outperforms the REE benchmark and is competitive with constant gain learning models in terms of in-sample and outof-sample fitness. Sample-autocorrelation learning of optimal AR(1) beliefs provides the best fit when short-term survey data on inflation expectations are taken into account in the estimation. As a policy application, we show that optimal Taylor rules under AR(1) expectations inherit history dependence and require a lower degrees of interest rate smoothing than REE.

Aim Understanding the distribution of marine biodiversity is critical for evidence‐based identification of areas for protection and restoration. Taxonomic richness is a key, intuitive component of biodiversity and is often used to guide marine spatial planning and protection. In this study, we explore the relative merits of two spatial modelling approaches, stacked species distribution models (S‐SDMs) and macro‐ecological models (MEMs), for mapping the richness of benthic invertebrate taxa. Location New Zealand Exclusive Economic Zone. Methods Two hundred and seven individual layers from SDMs of benthic invertebrate genera were pooled from an existing database and stacked to create a single genera richness layer. The same occurrence data used to develop the SDMs, comprising over 120k occurrences, was used to fit MEMs using an ensemble modelling approach. Results The S‐SDM layer performed poorly when validated against a database of observed genera richness, while the MEM approach performed well. While there were some consistencies in the areas predicted as high richness, substantial differences between the methods were also apparent, with the MEM seemingly better able to discern nuanced, fine‐scale patterns in richness. Areas of high richness predicted by the MEM include parts of the Chatham Rise, a large component of the sub‐Antarctic region, continental‐shelf and coastal habitat in the south of the South Island, the north‐east coast of the North Island, around East Cape and the Kermadec, Lau‐Colville and Macquarie Ridges. Main Conclusions Spatial and catchability biases in the underlying occurrence data may contribute to the poor performance of the S‐SDM and suggest the approach may not be appropriate when using occurrence datasets with limited systematic sampling. The predictions from the MEM provide the best available information for the distribution of benthic invertebrate richness for New Zealand waters and thus offer important information for current and future marine spatial planning processes.

We provide a nonlinear characterization of the macroeconomic impact of microeconomic productivity shocks in terms of reduced-form nonparametric elasticities for efficient economies. We also show how microeconomic parameters are mapped to these reduced-form general equilibrium elasticities. In this sense, we extend the foundational theorem of Hulten (1978) beyond the first order to capture nonlinearities. Key features ignored by first-order approximations that play a crucial role are: structural microeconomic elasticities of substitution, network linkages, structural microeconomic returns to scale, and the extent of factor reallocation. In a business-cycle calibration with sectoral shocks, nonlinearities magnify negative shocks and attenuate positive shocks, resulting in an aggregate output distribution that is asymmetric (negative skewness), fattailed (excess kurtosis), and has a negative mean, even when shocks are symmetric and thin-tailed. Average output losses due to short-run sectoral shocks are an order of magnitude larger than the welfare cost of business cycles calculated by Lucas (1987). Nonlinearities can also cause shocks to critical sectors to have disproportionate macroeconomic effects, almost tripling the estimated impact of the 1970s oil shocks on world aggregate output. Finally, in a long-run growth context, nonlinearities, which underpin Baumol's cost disease via the increase over time in the sales shares of low-growth bottleneck sectors, account for a 20 percentage point reduction in aggregate TFP growth over the period 1948-2014 in the United States.

We develop a methodology that utilizes deep learning to simultaneously solve and estimate canonical continuous-time general equilibrium models in financial economics. We illustrate our method in two examples: (1) industrial dynamics of firms and (2) macroeconomic models with financial frictions. Through these applications, we illustrate the advantages of our method: generality, simultaneous solution and estimation, leveraging the state-of-art machine-learning techniques, and handling large state space. The method is versatile and can be applied to a vast variety of problems.

A companion to the bestseller, The Impact of Economic Policies on Poverty and Income Distribution, this title deals with theoretical challenges and cutting-edge macro-micro linkage models. The authors compare the predictive and analytical power of various macro-micro linkage techniques using the traditional RHG approach as a benchmark to evaluate standard policies, such as, a typical stabilization package and a typical structural reform policy.

The adoption as policy models by central banks of representative agent New Keynesian dynamic stochastic general equilibrium models has been widely criticised, including for their simplistic micro-foundations. At the Bank of England, the previous generation of policy models is seen in its 1999 medium-term macro model (MTMM). Instead of improving that model to correct its considerable flaws, many shared by other non-DSGE policy models such as the Federal Reserve’s FRB/US, it was replaced in 2004 by the DSGE-based BEQM. Though this clearly failed during and after the global financial crisis, it was replaced in 2011 by the DSGE COMPASS, complemented by a ‘suite of models’. We provide a general critique of DSGE models for explaining, forecasting and policy analyses at central banks, and suggest new directions for improving current empirical macroeconomic models based on empirical modelling broadly consistent with better theory, rather than seeking to impose simplistic and unrealistic theory.

Accurate numerical simulation of masonry-infilled reinforced concrete (RC) frames is essential for seismic performance assessment, particularly with modern lightweight infill systems like hollow clay bricks (HCB), gypsum blocks (GB), and autoclaved lightweight concrete (ALC) panels. This study presents an advanced single-strut macro-modeling framework, implemented in SAP2000, designed to overcome the limitations of conventional approaches in capturing the distinct nonlinear behavior of these systems. The core innovation involves the development of material-specific nonlinear axial hinges, meticulously calibrated against the full-scale experimental dataset from Cai et al. (J. Earthq. Eng. 23(9):1531-1559, 2019) to replicate crushing (HCB), shear-sliding (GB), and slip-hardening (ALC) mechanisms. The calibrated model demonstrates high predictive accuracy, with errors in lateral capacity and initial stiffness below 10% for all systems. Leveraging this validated model, a comprehensive comparative seismic performance analysis was conducted. The results quantify a critical performance trade-off: HCB infills increased initial stiffness by 471% but exhibited brittle failure, while ALC panels provided superior ductility (µ = 5.39) and stable energy dissipation up to 4.06% drift. The gypsum system confirmed its seismic inadequacy due to abrupt strength degradation. The study concludes that the proposed modeling strategy provides a reliable tool for performance-based design and analysis, enabling engineers to efficiently conduct parametric studies and evaluate the seismic viability of different infill systems at a fraction of the cost and time of full-scale testing.

Despite the convenience of facial recognition payment (FRP), many consumers hesitate to use FRP. Drawing on the antecedent-privacy concern-outcome (APCO) macro model, this study investigates antecedents of privacy concerns and privacy fatigue in the context of FRP and how privacy concerns and privacy fatigue influence user’s resistance intention of FRP. A mixed-methods is used to address these issues. A semi-structured interview is first used to identify the antecedents of privacy concerns and privacy fatigue for facial privacy information. According to the research results, we develop research hypotheses and build the research model. By analyzing survey data from 394 respondents using Amos, this study finds that privacy experience, privacy control, privacy policy effectiveness, peer influence, and reputation significantly influence users’ privacy concerns for facial privacy information; in addition, privacy experience, privacy control, and negative media exposure significantly influence users’ privacy fatigue. Moreover, privacy concerns and privacy fatigue are significantly related to users’ resistance intention to FRP.

Accurate prediction of the cyclic response of reinforced concrete (RC) shear walls is critical for performance assessment of buildings under wind and earthquakes. Over the past few decades, various macro-models have been developed, based on different formulations and simplifying assumptions, to facilitate large-scale modeling of RC walls. However, there is limited research on the accuracy of these models for walls with different characteristics. This study evaluates the accuracy and application range of five prevalent macro-models using experimental results from 39 wall specimens with a wide range of design variables. Analytical and experimental results are compared in terms of cyclic loaddeflection responses, failure modes, and a set of structural performance measures. The results indicate that while the evaluated macro-models can predict the behavior of shear walls reasonably well, there are important limitations that may restrict their application range. Strengths and weaknesses of each macro-model are identified to help engineers in selecting the most suitable analysis method based on characteristics of the wall. Keywords: cyclic response; fiber element; lumped plasticity; macromodel; nonlinear analysis; reinforced concrete (RC) walls; shear-flexure interaction.

Macroeconomics has been under scrutiny as a field since the financial crisis, which brought an abrupt end to the optimism of the Great Moderation. There is widespread acknowledgement that the prevailing dynamic stochastic general equilibrium (DSGE) models performed poorly, but little agreement on what alternative future paradigm should be pursued. This article is the elaboration of four blog posts that together present a clear message: current DSGE models are flawed, but they contain the right foundations and must be improved rather than discarded. Further, we need different types of macroeconomic models for different purposes. Specifically, there should be five kinds of general equilibrium models: a common core, plus foundational theory, policy, toy, and forecasting models. The different classes of models have a lot to learn from each other, but the goal of full integration has proven counterproductive. No model can be all things to all people.