We present a new methodology that uses professional forecasts to estimate the effects of fiscal policy. We use short-term forecasts to better identify exogenous shocks to government spending by controlling for anticipatory information already in the public domain. We use longer-term forecasts to net out expectations from the future path of other variables, which improves accuracy and efficiency by focusing on more precise measures of the impact of shocks. We show that this improves the statistical fit relative to both local projection methods and vector autoregression-based analyses that do not control for the entire future path of expectations.

It has repeatedly been shown that properly constructed monetary aggregates based on index number theory (such as Divisia money) vastly outperform traditional measures of money (i.e. simple sum money) in empirical models. However, opponents of Divisia frequently claim that Divisia is “too complex” for little gain. And indeed, at first glance it looks as if simple sum and Divisia sum exhibit similar dynamics. In this paper, we want to build deeper understanding of how and when Divisia and simple sum differ empirically using monthly US data from 1990M1 to 2007M12. In particular, we look at how they respond differently to monetary policy shocks, which seems to be the most essential aspect of those differences from the perspective of the policy maker. We use a very rich, fairly agnostic setup that allows us to identify many potential nonlinearities, building on a smoothed local projections approach with automatic selection of the relevant interaction terms. We find, that—while the direction of change is often similar—the precise dynamics differ sharply. In particular in times of economic uncertainty, when the proper assessment of monetary policy is most relevant, those existing differences are drastically augmented.

This study estimates the response of macroeconomic variables to stock market fluctuations in Japan and the United States. It emphasizes the economy's reaction to stock market bubbles and crashes. To do this, I propose a new way to identify bubbles and crashes by testing price-to-fundamental ratios using the newly developed trend-filtering approach. Regardless of the measures used, both countries' macroeconomy tends to respond positively to the positive shock of stock price. Asymmetric effects of the stock market are observed. Japan's macroeconomic variables, especially investment and industrial production, are more sensitive to market crashes, while those of the United States are more sensitive to stock bubbles. Finally, I provide evidence that market sentiment can affect the economy either directly or indirectly through the stock market.

We compare spectral and wavelet estimators of the response amplitude operator (RAO) of a linear system, with various input signals and added noise scenarios. The comparison is based on a model of a heaving buoy wave energy device (HBWED), which oscillates vertically as a single mode of vibration linear system. HBWEDs and other single degree of freedom wave energy devices such as oscillating wave surge convertors (OWSC) are currently deployed in the ocean, making such devices important systems to both model and analyse in some detail. The results of the comparison relate to any linear system. It was found that the wavelet estimator of the RAO offers no advantage over the spectral estimators if both input and response time series data are noise free and long time series are available. If there is noise on only the response time series, only the wavelet estimator or the spectral estimator that uses the cross-spectrum of the input and response signals in the numerator should be used. For the case of noise on only the input time series, only the spectral estimator that uses the cross-spectrum in the denominator gives a sensible estimate of the RAO. If both the input and response signals are corrupted with noise, a modification to both the input and response spectrum estimates can provide a good estimator of the RAO. A combination of wavelet and spectral methods is introduced as an alternative RAO estimator. The conclusions apply for autoregressive emulators of sea surface elevation, impulse, and pseudorandom binary sequences (PRBS) inputs. However, a wavelet estimator is needed in the special case of a chirp input where the signal has a continuously varying frequency.

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

Impulse response functions (IRFs) were obtained from two-pulse detection thresholds using isoluminant stimuli that produced increments or decrements in S-cone excitation. The pulses were chromatically modulated at constant luminance (based on 18 Hz heterochromatic flicker photometry). Chromatic stimuli were presented as a Gaussian patch (±1 SD = 2.3°) in one of four quadrants around a central fixation cross on a CRT screen. Each of the two pulses (6.67 ms) was separated by an inter-stimulus interval (ISI) from 20 to 360 ms. Chromaticity of the pulses was changed from the equal-energy white of the background to a bluish or yellowish color along individually determined tritan lines (based on color matching under strong S-cone adaptation from a 420 nm background superimposed in Maxwellian view). Chromatic detection thresholds were determined by a four-alternative forced-choice method with staircases for each ISI interleaved in each session. Measurements were repeated in at least four sessions for each observer. IRFs were calculated by varying four parameters of an exponentially-damped sinewave. Both S-cone increment and decrement IRFs are characterized by a single excitatory phase and a much longer time course compared with IRFs derived for luminance modulation using the same apparatus and observers. S-cone increment IRFs are faster than S-cone decrement IRFs; the time to peak amplitude of S-cone increment and decrement IRFs is 50–70 and 100–120 ms, respectively. These results were used to derive the temporal contrast sensitivity for human observers of putative ON- and OFF-channels carrying signals from S-cones.

This paper studies limiting properties of discretely sampled Poisson shot noise processes. Versions of the law of large numbers and central limit theorem are derived under very general conditions. Examples demonstrating the utility of the results are included.

It is argued that standard impulse response analysis based on vector autoregressive models has a number of shortcomings. Although the impulse responses are estimated quantities, measures for sampling variability such as confidence intervals sometimes are not provided. If confidence intervals are given, they often are based on bootstrap methods with dubious theoretical properties. These problems are illustrated using two German monetary systems. Proposals are made for improving current practice. Special emphasis is placed on systems with cointegrated variables.

We studied how intrinsic membrane properties affect the gain and temporal pattern of response in bipolar cells dissociated from retinae of tiger salamanders. Currents specified by a pseudorandom binary sequence, an m-sequence, superimposed on various means, were injected into the cells. From the resultant membrane voltage response for each mean current, impulse responses were estimated. From each impulse response, transfer function, gain, and time constant were calculated. The bipolar cells acted as quasilinear adaptive filters whose gain and response speed are determined by the mean input current. Near resting potential, gain, and time constant were maximum. Dynamics were slow and low-pass, characterized by an approximately exponential impulse response. With depolarization, gains were reduced sharply, responses were much faster, and dynamics became band-pass, as indicated by an undershoot in the impulse response. For any given mean current, the shape of the impulse response did not depend on the amplitude of the m-sequence currents. Thus, bipolar cells behaved in a quasilinear fashion. The adaptive behavior was eliminated by blocking a potassium current, which implicates the role of a voltage-gated potassium conductance. Computer simulations on a model neuron including a delayed-rectifier reconstructed the observed behavior, and provided insight into other, less readily observable, parameters. Thus, bipolar cells, even when isolated, possess mechanisms which regulate, with unsuspected elaborateness, the sensitivities and dynamics of their responsiveness. Implications for adaptation and neuronal processing are discussed.