Forecasting Pre-World War I Inflation: The Fisher Effect Revisited

We consider the puzzling behavior of interest rates and inflation in the United States and the United Kingdom between 1879 and 1913. A deflationary regime prior to 1896 was followed by an inflationary one from 1896 until the beginning of World War I; the average inflation rate was 3.8 percentage points higher in the second period than in the first. Yet nominal interest rates were no higher after 1896 than they had been before. This nonadjustment of nominal interest rates would be consistent with rational expectations if inflation were not forecastable, and indeed univariate tests show little sign of serial correlation in inflation. However, inflation was forecastable on the basis of lagged gold production. Investors' expectations of inflation should have risen by at least three percentage points in the United States between 1890 and 1910. We consider in an information processing context alternative ways of accounting for this failure of interest rates to adjust, for example the possible beliefs that increases in gold production might be transitory. We conclude that the failure of investors to exhibit foresight with regard to the shift in the trend inflation rate after 1896 is not persuasive evidence that investors were negligent or naive in processing information.

The Economic Consequences of Noise Traders

The claim that financial markets are efficient is backed by an implicit argument that misinformed "noise traders" can have little influence on asset prices in equilibrium. If noise traders' beliefs are sufficiently different from those of rational agents to significantly affect prices, then noise traders will buy high and sell low. They will then lose money relative to rational investors and eventually be eliminated from the market. We present a simple overlapping-generations model of the stock market in which noise traders with erroneous and stochastic beliefs (a) significantly affect prices and (b) earn higher returns than do rational investors. Noise traders earn high returns because they bear a large amount of the market risk which the presence of noise traders creates in the assets that they hold: their presence raises expected returns because sophisticated investors dislike bearing the risk that noise traders may be irrationally pessimistic and push asset prices down in the future. The model we present has many properties that correspond to the "Keynesian" view of financial markets. (i) Stock prices are more volatile than can be justified on the basis of news about underlying fundamentals. (ii) A rational investor concerned about the short run may be better off guessing the guesses of others than choosing an appropriate P portfolio. (iii) Asset prices diverge frequently but not permanently from average values, giving rise to patterns of mean reversion in stock and bond prices similar to those found directly by Fama and French (1987) for the stock market and to the failures of the expectations hypothesis of the term structure. (iv) Since investors in assets bear not only fundamental but also noise trader risk, the average prices of assets will be below fundamental values; one striking example of substantial divergence between market and fundamental values is the persistent discount on closed-end mutual funds, and a second example is Mehra and Prescott's (1986) finding that American equities sell for much less than the consumption capital asset pricing model would predict. (v) The more the market is dominated by short-term traders as opposed to long-term investors, the poorer is its performance as a social capital allocation mechanism. (vi) Dividend policy and capital structure can matter for the value of the firm even abstracting from tax considerations. And (vii) making assets illiquid and thus no longer subject to the whims of the market -- as is done when a firm goes private -- may enhance their value.

Phase Correction using Deep Learning for Satellite-to-Ground CV-QKD

Coherent measurement of quantum signals used for continuous-variable (CV) quantum key distribution (QKD) across satellite-to-ground channels requires compensation of phase wavefront distortions caused by atmospheric turbulence. One compensation technique involves multiplexing classical reference pulses (RPs) and the quantum signal, with direct phase measurements on the RPs then used to modulate a real local oscillator (RLO) on the ground - a solution that also removes some known attacks on CV-QKD. However, this is a cumbersome task in practice - requiring substantial complexity in equipment requirements and deployment. As an alternative to this traditional practice, here we introduce a new method for estimating phase corrections for an RLO by using only intensity measurements from RPs as input to a convolutional neural network, mitigating completely the necessity to measure phase wavefronts directly. Conventional wisdom dictates such an approach would likely be fruitless. However, we show that the phase correction accuracy needed to provide for non-zero secure key rates through satellite-to-ground channels is achieved by our intensity-only measurements. Our work shows, for the first time, how artificial intelligence algorithms can replace phase-measuring equipment in the context of CV-QKD delivered from space, thereby delivering an alternate deployment paradigm for this global quantum-communication application

Magnetic-Field Amplification in the Thin X-ray Rims of SN1006

Several young supernova remnants (SNRs), including SN1006, emit synchrotron X-rays in narrow filaments, hereafter thin rims, along their periphery. The widths of these rims imply 50 to 100 $\mu$G fields in the region immediately behind the shock, far larger than expected for the interstellar medium compressed by unmodified shocks, assuming electron radiative losses limit rim widths. However, magnetic-field damping could also produce thin rims. Here we review the literature on rim width calculations, summarizing the case for magnetic-field amplification. We extend these calculations to include an arbitrary power-law dependence of the diffusion coefficient on energy, $D \propto E^{\mu}$. Loss-limited rim widths should shrink with increasing photon energy, while magnetic-damping models predict widths almost independent of photon energy. We use these results to analyze Chandra observations of SN 1006, in particular the southwest limb. We parameterize the full widths at half maximum (FWHM) in terms of energy as FWHM $\propto E^{m_E}_{\gamma}$. Filament widths in SN1006 decrease with energy; $m_E \sim -0.3$ to $-0.8$, implying magnetic field amplification by factors of 10 to 50, above the factor of 4 expected in strong unmodified shocks. For SN 1006, the rapid shrinkage rules out magnetic damping models. It also favors short mean free paths (small diffusion coefficients) and strong dependence of $D$ on energy ($\mu \ge 1$).Comment: Accepted by ApJ, 49 pages, 10 figure

The State Correction Officer as Keeper and Counselor: An Empirical Investigation of the Role

This paper addresses two essential research needs in criminal justice literature: (1) the need for an assessment of the content of the role of block officer; and (2) the need for an empirical test of the presumed irreconcilable goals of custody and treatment as these are embedded in the role of state correction officer. A Task Inventory approach was adapted and a random sample of 100 correction officers in four heterogeneous state institutions were interviewed. Results of the study reveal that custodial staff spend at least sixty-percent of their on-job time performing duties not classified as security in nature. Results of the study challenge many of the existing stereotypes of correction officers in the literature. *Th

The dynamic Arctic

Research campaigns over the last decade have yielded a growing stream of data that highlight the dynamic nature of Arctic cryosphere and climate change over a range of time scales. As a consequence, rather than seeing the Arctic as a near static environment in which large scale changes occur slowly, we now view the Arctic as a system that is typified by frequent, large and abrupt changes. The traditional focus on end members in the system - glacial versus interglacial periods - has been replaced by a new interest in understanding the patterns and causes of such dynamic change. Instead of interpreting changes almost exclusively as near linear responses to external forcing (e.g. orbitally-forced climate change), research is now concentrated on the importance of strong feedback mechanisms that in our palaeo-archives often border on chaotic behaviour. The last decade of research has revealed the importance of on-off switching of ice streams, strong feedbacks between sea level and ice sheets, spatial and temporal changes in ice shelves and perennial sea ice, as well as alterations in ice sheet dynamics caused by shifting centres of mass in multi-dome ice sheets. Recent advances in dating techniques and modelling have improved our understanding of leads and lags that exist in different Arctic systems, on their interactions and the driving mechanisms of change. Future Arctic research challenges include further emphases on rapid transitions and untangling the feedback mechanisms as well as the time scales they operate on