Long-run Average Research Articles

Party affiliation predicts homeowners’ decisions to install solar PV, but partisan gap wanes with improved economics of solar

The perceived risk of climate change and the sense of urgency for an energy transition are both politically polarized, especially in the United States. Yet, we know relatively little about how political polarization affects consumer energy preferences and behaviors. Here, we use the case of residential solar photovoltaics (PV) in New York State to 1) measure the partisan gap in solar adoption rates and 2) test whether more favorable economics of solar PV mute the effect of political identity. Using household-level, longitudinal data that include nearly 63,000 completed residential PV projects, we find evidence of a partisan gap in PV adoption. Democratic homeowners are approximately 1.45 times as likely to adopt solar PV as Republican homeowners. Republicans' rate of adoption is the lowest of all measured groups, behind Independents, unaffiliated voters, and homeowners not registered to vote. Crucially, however, Republicans in our sample appear to be the most attuned to the changing economics and financing options of solar PV. Our estimates suggest that 1) as homeowners' electricity rate increases relative to its long-run average, the adoption gap between Democ-rats and Republicans narrows, 2) that Republican PV adopters obtain systems with higher expected economic value, and 3) Republicans take greater advantage of alternative financing models, like leases and power purchase agreements, especially when the upfront costs of solar are high. The results demonstrate that political identity affects consumers' participation in the energy transition, but local context, including the local economics of solar, may mitigate the effect of personal politics.

VOLATILITY ANALYSIS OF CRUDE OIL PRICES IN NIGERIA

This study investigates the symmetric and asymmetric characteristics as well as the persistence of shocks in the Nigerian crude oil returns, utilizing monthly and daily crude oil prices spanning from January 2006 to September 2022 and November 3, 2009, to November 4, 2022, respectively. Descriptive statistics, normality measures, time plots, and the Dickey-Fuller Generalized Least Squares unit root test were employed to analyze the series properties. Symmetric ARMA (1,1)-GARCH (2,1) and asymmetric ARMA (1,1)-TARCH (2,1) models for monthly and daily returns, with varying innovation densities, were utilized, alongside symmetric GARCH (1,1) and asymmetric TARCH (1,1) models. Model selection criteria including AIC, SIC, HQC, and log likelihood guided the order and error distribution selection. Results revealed non-normal distributions for both monthly and daily prices and returns, non-stationarity in prices, and weak stationarity in log returns with ARCH effects detected in both returns. Symmetric models exhibited volatility clustering, high shocks persistence, mean-reverting behaviour, and predictability in both returns. Asymmetric models identified asymmetry with leverage effects in both returns, indicating that negative shocks induce greater volatility than positive shocks of the same magnitude. Mean reversion and volatility half-life findings suggested that crude oil prices tend to revert to their long-run averages. The study recommended promoting market information flow and aggressive trading to enhance market depth and mitigate the volatile nature of the Nigerian crude oil market.

Poorer White Matter Microstructure Predicts Slower and More Variable Reaction Time Performance: Evidence for a Neural Noise Hypothesis in a Large Lifespan Cohort.

Most prior research has focused on characterizing averages in cognition, brain characteristics, or behavior, and attempting to predict differences in these averages among individuals. However, this overwhelming focus on mean levels may leave us with an incomplete picture of what drives individual differences in behavioral phenotypes by ignoring the variability of behavior around an individual's mean. In particular, enhanced white matter (WM) structural microstructure has been hypothesized to support consistent behavioral performance by decreasing Gaussian noise in signal transfer. Conversely, lower indices of WM microstructure are associated with greater within-subject variance in the ability to deploy performance-related resources, especially in clinical populations. We tested a mechanistic account of the "neural noise" hypothesis in a large adult lifespan cohort (Cambridge Centre for Ageing and Neuroscience) with over 2500 adults (ages 18-102; 1508 female; 1173 male; 2681 behavioral sessions; 708 MRI scans) using WM fractional anisotropy to predict mean levels and variability in reaction time performance on a simple behavioral task using a dynamic structural equation model. By modeling robust and reliable individual differences in within-person variability, we found support for a neural noise hypothesis (Kail, 1997), with lower fractional anisotropy predicted individual differences in separable components of behavioral performance estimated using dynamic structural equation model, including slower mean responses and increased variability. These effects remained when including age, suggesting consistent effects of WM microstructure across the adult lifespan unique from concurrent effects of aging. Crucially, we show that variability can be reliably separated from mean performance using advanced modeling tools, enabling tests of distinct hypotheses for each component of performance.SIGNIFICANCE STATEMENT Human cognitive performance is defined not just by the long-run average, but trial-to-trial variability around that average. However, investigations of cognitive abilities and changes during aging have largely ignored this variability component of behavior. We provide evidence that white matter (WM) microstructure predicts individual differences in mean performance and variability in a sample spanning the adult lifespan (18-102). Unlike prior studies of cognitive performance and variability, we modeled variability directly and distinct from mean performance using a dynamic structural equation model, which allows us to decouple variability from mean performance and other complex features of performance (e.g., autoregression). The effects of WM were robust above the effect of age, highlighting the role of WM in promoting fast and consistent performance.

Adaptive Regulation of Block-Oriented Nonlinear Systems Using Binary Sensors With Applications to Automotive Engine Control

In this article, adaptive regulation of block-oriented nonlinear systems, i.e., Hammerstein and Wiener systems, with binary-valued measurements of the regulation errors is considered. Compared with the classical framework for stochastic adaptive control, the new feature here is that only binary-valued observations of regulation errors are available to the controller. An adaptive regulator based on the stochastic approximation algorithm is proposed and it is proved that the regulator is optimal in the sense that it minimizes the long-run average of the squared regulation errors almost surely. Numerical examples as well as real applications of the proposed algorithms to automotive engine control are given.

A note on the existence of optimal stationary policies for average Markov decision processes with countable states

In many practical stochastic dynamic optimization problems with countable states, the optimal policy possesses certain structural properties. For example, the (s,S) policy in inventory control, the well-known cμ-rule and the recently discovered c/μ-rule (Xia et al. (2022)) in scheduling of queues. A presumption of such results is that an optimal stationary policy exists. There are many research works regarding to the existence of optimal stationary policies of Markov decision processes with countable state spaces (see, e.g., Bertsekas (2012); Hernández-Lerma and Lasserre (1996); Puterman (1994); Sennott (1999)). However, these conditions are usually not easy to verify in such optimization problems. In this paper, we study the optimization of long-run average of continuous-time Markov decision processes with countable state spaces. We provide an intuitive approach to prove the existence of an optimal stationary policy. The approach is simply based on compactness of the policy space, with a special designed metric, and the continuity of the long-run average in the space. Our method is capable to handle cost functions unbounded from both above and below, which makes a complementary contribution to the literature work where the cost function is unbounded from only one side. Examples are provided to illustrate the application of our main results.

Revisiting Baumol’s growth disease in Japan

This study analyzes the cause of secular stagnation in Japan after the economic bubble and its association with industrial structural change, specifically Baumol’s growth disease (BGD). Our analysis updates Nishi and focuses on the period between 1995 and 2018 using the latest version of the JIP (Japan Industrial Productivity) database. Then, we ask “Is Japan suffering from BGD?” Our approach includes decomposition of the aggregate labor productivity growth rate, fixed-share growth rate (FSGR) measurement, and a transition probability matrix. The decomposition reveals that the within-sector effect (WSE) is the main source of aggregate labor productivity. The reallocation growth effect (RGE), which measures BGD, was negative for both the short- and long-run averages of the post-1995 period. This period is different from the previous one, as the WSE declined significantly, and the considerable decline in the WSE can be seen as a major factor in Japan’s stagnation over the past 30 years. During this stagnation, BGD silently undermined the Japanese economy, albeit as a secondary effect. Furthermore, our FSGR and transition probability matrix show that persists and is infectious in Japan. Sectors that undergo BGD tend to persist in this state, and even when one sector is in a non-BGD state, it is more likely to develop BGD.

Revisiting the PPP puzzle: Nominal exchange rate rigidity and region of inaction

The paper revisits the purchasing power parity (PPP) puzzle and investigates potential sources of rigidity in the real exchange rate convergence. We propose two hypotheses on the slow convergence estimates in previous studies, and re-estimate the convergence rates to test the hypotheses. First, the slow convergence estimation is potentially from the forex market intervention by the central banks which claim to freely float their exchanges. Second, the real exchange rates may not show any convergence if they are already converged and within the “region of inaction.” We identify the regions of inaction (1) by creating bands around averages of real exchange rates, assuming that the PPP forms around the long-run averages, and (2) by using a Bayesian Dynamic Linear Model (DLM), assuming that real exchange rates converge to the different levels over time. We use a standard fixed effect estimation as well as a mean group panel estimation to handle aggregation biases in standard panel estimation. The results confirm the propositions and make the puzzle less puzzling; we find approximately 30%–65% shorter half-life estimates than the standard fixed effects estimates in previous studies.

New Definition of Default—Recalibration of Credit Risk Models Using Bayesian Approach

After the financial crisis, the European Banking Authority (EBA) has established tighter standards around the definition of default (Capital Requirements Regulation CRR Article 178, EBA/GL/2017/16) to increase the degree of comparability and consistency in credit risk measurement and capital frameworks across banks and financial institutions. Requirements of the new definition of default (DoD) concern how banks recognize credit defaults for prudential purposes and include quantitative impact analysis and new rules of materiality. In this approach, the number and timing of defaults affect the validity of currently used risk models and processes. The recommendation presented in this paper is to address current gaps by considering a Bayesian approach for PD recalibration based on insights derived from both simulated and empirical data (e.g., a priori and a posteriori distributions). A Bayesian approach was used in two steps: to calculate the Long Run Average (LRA) on both simulated and empirical data and for the final model calibration to the posterior LRA. The Bayesian approach result for the PD LRA was slightly lower than the one calculated based on classical logistic regression. It also decreased for the historically observed LRA that included the most recent empirical data. The Bayesian methodology was used to make the LRA more objective, but it also helps to better align the LRA not only with the empirical data but also with the most recent ones.

IFRS 9 compliant economic adjustment of expected credit loss modeling

This paper presents an International Financial Reporting Standard 9 (IFRS 9) compliant solution related to expected credit loss modeling. Commonly, credit default swap (CDS) spreads are considered as market indicators of future debt performance. However, we demonstrate empirically that nondefault risks explain a relevant part of the CDS spread, and we assess the average weight-of-default component for each point in the CDS spread term structure. Thus, to be used for probability of default estimations, CDS spreads must be adjusted for the nondefault component to guarantee the neutral character of expected credit loss estimations, as required by IFRS 9. Our study introduces an innovative methodology for extracting the pure default component and probability of default calibration. To enable economic adjustment of probabilities of default we analyze the relationship between a long-run average of the across-the-sample mean CDS spread of the homogeneous cohort of issuers and the spread implied by the long-run average of the observed default rates. Our easy-to-implement solution is applied to a sample of investment-grade and high-yield corporate debt issuers. We exploit differences in the economic performance of North American and eurozone obligors. The proposed framework allows us to understand complex interactions between the forward-looking impairment provisions and economic capital requirements in relation to credit losses.

Excess liquidity premia of single-name CDS vs iTraxx/CDX spreads: 2007-2017

PurposeThe aim of this research is twofold. First, we study average levels of liquidity for long-run through-the-cycle periods, which potentially allow eliminating procyclicality from risk parameters used for expected credit-loss calculations. Second, we investigate to what extent the relative illiquidity of individual credit default swap (CDS) contracts affects their spreads in comparison with the respective CDS indices.Design/methodology/approachBased on the iTraxx Europe CDS index covering European firms and the CDX North America CDS index covering US firms, as well as on individual CDS transactions involving the reference entities constituting these two benchmark indices, we investigate the excess liquidity premia in spreads of the single-name CDS contracts over the spreads of the iTraxx and CDX indices over 2007-2017.FindingsFirst, single-name CDS excess liquidity premia depend on CDS contract maturity. Second, the long-run average spread of a benchmark index may stay as low as three-fourths of the respective long-run average of the mean of the single-name CDS spreads, meaning that the excess liquidity premium may be as high as one-fourth of the firm-specific CDS spread. Third, the term structure of the excess liquidity differs between the Europe and North America geographies. Fourth, on average, the excess liquidity premia in the single-name CDS spreads over the respective CDS indices diminish with increasing maturities of CDS contracts.Originality/valueNo previous research addresses differences between the liquidity component in a benchmark CDS index spreads and the mean spread averaged across the constituents of the index. Our work fills this gap.

Detecting bias due to input modelling in computer simulation

This is the first paper to approach the problem of bias in the output of a stochastic simulation due to using input distributions whose parameters were estimated from real-world data. We consider, in particular, the bias in simulation-based estimators of the expected value (long-run average) of the real-world system performance; this bias will be present even if one employs unbiased estimators of the input distribution parameters due to the (typically) nonlinear relationship between these parameters and the output response. To date this bias has been assumed to be negligible because it decreases rapidly as the quantity of real-world input data increases. While true asymptotically, this property does not imply that the bias is actually small when, as is always the case, data are finite. We present a delta-method approach to bias estimation that evaluates the nonlinearity of the expected-value performance surface as a function of the input-model parameters. Since this response surface is unknown, we propose an innovative experimental design to fit a response-surface model that facilitates a test for detecting a bias of a relevant size with specified power. We evaluate the method using controlled experiments, and demonstrate it through a realistic case study concerning a healthcare call centre.

Modeling Volatility of Exchange Rates Returns in the Nigerian Foreign Exchange Market in the Presence of Non-Gaussian Errors

This study investigates volatility behaviour of exchange rates returns of Naira against CFA, Euro, Great British Pounds, US Dollar, West African Unit of Account (WAUA) and Japanese Yen in Nigeria using historical volatility approach as well as symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (GARCH) models in the presence of non-Gaussian errors. The study utilizes daily quotations of these exchange rates from 12/11/2001 to 04/13/2018 making a total of 4008 observations each. Historical (annualized) volatility approach as well as symmetric GARCH (1,1) and asymmetric EGARCH (1,1) models were used to model the exchange rates return series. Results showed that CFA and USD have the highest and least annualized volatilities (market risk) respectively among the six exchange rates returns as measured by historical approach. The symmetric GARCH (1,1) model showed volatility clustering with evidence of shock persistence in the six exchange rates return series. The asymmetric EGARCH (1,1) model found evidence of asymmetry and leverage effects in the Nigerian foreign exchange market indicating that negative shocks (bad news) generate more volatility than positive shocks (good news) of similar magnitudes. All the estimated models were found to be stationary and mean reverting indicating the predictability and stability of the conditional variances of the foreign exchange rates returns. This result suggests that no matter how high or low the foreign exchange rates shall move in the exchange market, they shall eventually return to their long-run averages. Stationary and mean reverting stocks provide good and long term investment opportunities for investors.

On the Optimal Control Problem for a Linear Stochastic System with an Unstable State Matrix Unbounded at Infinity

We consider a control problem over an infinite time horizon with a linear stochastic system with an unstable asymptotically unbounded state matrix. We extend the notion of anti-stability of a matrix to the case of non-exponential anti-stability, and introduce an antistability rate function as a characteristic of the rate of growth for the norm of the corresponding fundamental matrix. We show that the linear stable feedback control law is optimal with respect to the criterion of the adjusted extended long-run average. The designed criterion explicitly includes information about the rate of anti-stability and the parameters of the disturbances. We also analyze optimality conditions.

Extreme dependence and risk spillovers across north american equity markets

This study analyzes risk spillovers across North American equity markets over 1995–2016. Downside and upside Conditional Value-at-Risk (CoVaR) are estimated after modeling the dynamic dependence structure for each equity market pair using generalized autoregressive score (GAS) copulas. US-CAN and CAN-MX dynamic correlations trend upwards over the sample period while the US-CAN correlation fluctuates around a higher long-run average. Conditional tail dependence is symmetric and significantly higher following the Global Financial Crisis (GFC) in all cases, implying greater co-movement under extreme economic conditions. Downside and upside risk spillovers are significant and asymmetric along two dimensions for each equity market pair, where downside risk spillovers are more severe and the degree of asymmetry by conditioning direction is rank ordered by relative equity market sizes. Aside from the US-CAN pair, downside and upside risk spillovers are significantly larger following the GFC as compared to the pre-crisis period. Observed asymmetric and time-varying behavior is consistent across high/low risk and high/low risk spillover sub-periods.

Semismooth Potentials of Stochastic Systems With Degenerate Diffusions

We prove that for a number of optimal control problems, including finite horizon, long-run average, and optimal stopping, with one-dimensional degenerate diffusion processes, the potential function (solution to Poisson equation) and, hence, the value function (solution to HJB equation) are semismooth at the degenerate points (i.e., the left- and right-hand side derivatives exist but may not be equal). This allows applying the Ito–Tanaka formula in the direct-comparison-based optimization approach, and previous results on semismooth value functions depend heavily on this property. This result will facilitate further research in stochastic optimal control.

Approximation of optimal ergodic dividend strategies using controlled Markov chains

This study develops a numerical method to find optimal ergodic (long-run average) dividend strategies in a regime-switching model. The surplus process is modelled by a regime-switching process subject to liability constraints. The regime-switching process is modelled by a finite-time continuous-time Markov chain. Using the dynamic programming principle, the optimal long-term average dividend payment is a solution to the coupled system of Hamilton–Jacobi–Bellman equations. Under suitable conditions, the optimal value of the long-term average dividend payment can be determined by using an invariant measure. However, due to the regime switching, getting the invariant measure is very difficult. The objective is to design a numerical algorithm to approximate the optimal ergodic dividend payment strategy. By using the Markov chain approximation techniques, the authors construct a discrete-time controlled Markov chain for the approximation, and prove the convergence of the approximating sequences. A numerical example is presented to demonstrate the applicability of the algorithm.

Jobless Recovery: A Time Series Look at the United States

We use vector auto regression models controlling for log differences in gross domestic product (GDP), the unemployment rate, and changes in the unemployment rate to show that following a recession, the rate of decrease in the unemployment rate significantly slowed over time. Controlling for GDP growth rates, we find two structural breaks indicating weaker recoveries in the unemployment rate over time, i.e. recoveries that are increasingly jobless. The first break is in 1959, and the second is in 1984 coinciding with the usual timing of the Great Moderation. Using the 7.85% unemployment rate at the end of 2012 and assuming average annual recovery growth of 2%, the structural breaks imply an additional two full years are necessary to return to a historical long-run average of 5.5%. We empirically explore possible causes proposed in the literature including industry composition, participation rates and social benefits. Demographic shifts in the labor force and changing industry composition appear to be the strongest contributors to jobless recovery.

Improving U.S. Stock Return Forecasts: A “Fair-Value” CAPE Approach

The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. A primary reason has been that traditional CAPE regressions assume the CAPE ratio reverts mechanically to its long-run average, regardless of the macroeconomic environment. In this article, the authors propose an enhancement to standard CAPE-based forecasts that conditions mean reversion in the CAPE ratio on real (not nominal) bond yields, expected inflation rates, and financial volatility in a VAR model. The forecasted results are promising for both real and nominal returns, with out-of-sample forecast errors approximately 50% lower than traditional approaches. The differences are economically meaningful and statistically significant over the forecast period examined. At present, low real bond yields imply low real earnings yields and an above-average “fair-value” CAPE ratio. Nevertheless, with Shiller’s CAPE ratio now well above its fair value, the model proposed by the author predicts nominal U.S. stock returns centered near 5% over the next decade.

Improving U.S. Stock Return Forecasts: A 'Fair-Value' Cape Approach

The accuracy of U.S. stock return forecasts based on the cyclically-adjusted P/E (CAPE) ratio has deteriorated since 1985. The issue is not the CAPE ratio, but CAPE regressions that assume it reverts mechanically to its long-run average. Our approach conditions mean reversion in the CAPE ratio on real (not nominal) bond yields, reducing out-of-sample forecast errors by as much as 50%. At present, low real bond yields imply low real earnings yields and an above-average “fair-value” CAPE ratio. Nevertheless, with Shiller’s CAPE ratio now well above its fair value, our model predicts muted U.S. stock returns over the next decade. We believe that our framework should be adopted by the investment profession when forecasting stock returns for strategic asset allocation.

Searching for the existence of entrepreneurial ecosystems: a regional cross-section growth regression approach

In this paper, we propose a method by which the entrepreneurial ecosystem, if present, reveals itself in the data. We first follow the literature and define the entrepreneurial ecosystem as a multidimensional set of interacting factors that moderate the effect of entrepreneurial activity on economic growth. The quality of such an ecosystem, by its multidimensionality, is impossible to measure directly. But so defined, we argue that variation in entrepreneurial ecosystem quality should result in variation in the estimated marginal effect of entrepreneurial activity on economic growth. Testing for such variation is possible using a combination of a multilevel growth regression and latent class analysis. We motivate and validate our approach in simulated data before illustrating its applicability in a data set covering 107 European NUTS1-2 regions across 16 EU member states. For this dataset, we cannot reject the hypothesis of a homogeneous contribution of entrepreneurship to regional growth. That is, in this dataset, we find no evidence of statistically significant heterogeneity in the estimated slope coefficients for entrepreneurial activity across regions. There are several possible explanations for this negative result. The two we deem most likely are first that the NUTS1-2 level may not be disaggregated enough to coincide with the relevant boundaries of the entrepreneurial ecosystem. We suspect our method would reveal significant differences across smaller geographical units, but the data unfortunately do not yet allow us to empirically test this hypothesis in a multi-country regional analysis. The second possible explanation is that the growth rates from 2006 to 2014 coincided with the global financial and the European crisis, and during this time, the effect of entrepreneurship on (long-run average) growth overall has been obscured. Our simulations also suggested a third reason. If measurement error is large (in the order of 33 or 0.015% point annual GDP-growth), the effects may also have been obscured.