The Role of High-Tech Capital Formation for Swedish Productivity Growth

While using new data and standard growth-accounting techniques, this paper takes a closer look at the Swedish productivity revival in the second half of the 1990s. In particular, I find large total factor productivity growth in high-tech producing sectors and capital deepening associated with high-tech equipment elsewhere. In addition, for high-tech producers, high-tech capital deepening has as a rule contributed negatively to labor productivity growth - a result above all driven by large increases in hours worked in this sector. I also find that in the business sector, the contribution from high-tech capital deepening to labor productivity growth increased from about 1 percent 1994 to 9 percent 1999.

Is Rising Returns to Scale a Figment of Poor Data?

While using detailed firm-level data from the private business sector, this study identifies two empirical puzzles: (i) returns-to-scale (RTS) parameter estimates rise at higher levels of data aggregation, and (ii) estimates from the firm level suggest decreasing returns to scale. The analysis shows that, although consistent with rising estimates, the Basu-Fernald (1997) aggregation-bias effect does not drive this result. Rather, rising and too low returns-to-scale estimates probably reflect a mixture of random errors in factor inputs. It turns out, in fact, that a 7.5-10 percent error in labor (hours worked) can explain both puzzles.Business cycles; Data aggregation; External economies; Factor hoarding; Firm-level data; Monte Carlo simulation; Random errors; Returns to scale

Qualitative Survey Responses and Production over the Business Cycle

An examination of Swedish manufacturing data on real output and qualitative business tendency survey (BTS) responses from 1968 through 1998 reveals that survey-based attitude data typically improve the fit of simple autoprojective models of manufacturing output growth. It also turns out that traditional autoregressive distributed lag (ADL) models based on business survey data can provide more accurate one-quarter-ahead forecasts of output growth than naive alternatives. Another finding is that when BTS variables concerning ex post (ex ante) output growth are included in the empirical specifications, then no other ex post (ex ante) business survey variables seems to include any additional information about output growth

External Economies at the Firm Level: Evidence from Swedish Manufacturing

Using the method of Caballero and Lyons (1990, 1992), I examine detailed Swedish manufacturing firm-level data on output and factor inputs from 1979 through 1994. Panel regressions show that an increase in aggregate output and inputs appears to raise individual firms production beyond private marginal returns, a result consistent with external economies. However, while considering potential specification difficulties, this paper shows that a model in which random shifts in technology drive the business cycle statistically outperforms the Caballero-Lyons model. This finding suggests that high-frequency random shifts in technology are more important for movements in firms productivity than are external economies

Optical flow estimation on image sequences with differently exposed frames

Optical flow (OF) methods are used to estimate dense motion information between consecutive frames in image sequences. In addition to the specific OF estimation method itself, the quality of the input image sequence is of crucial importance to the quality of the resulting flow estimates. For instance, lack of texture in image frames caused by saturation of the camera sensor during exposure can significantly deteriorate the performance. An approach to avoid this negative effect is to use different camera settings when capturing the individual frames. We provide a framework for OF estimation on such sequences that contain differently exposed frames. Information from multiple frames are combined into a total cost functional such that the lack of an active data term for saturated image areas is avoided. Experimental results demonstrate that using alternate camera settings to capture the full dynamic range of an underlying scene can clearly improve the quality of flow estimates. When saturation of image data is significant, the proposed methods show superior performance in terms of lower endpoint errors of the flow vectors compared to a set of baseline methods. Furthermore, we provide some qualitative examples of how and when our method should be used

Variational Optical Flow Estimation for Images with Spectral and Photometric Sensor Diversity

Motion estimation of objects in image sequences is an essential computer vision task. To this end, optical flow methods compute pixel-level motion, with the purpose of providing low-level input to higher-level algorithms and applications. Robust flow estimation is crucial for the success of applications, which in turn depends on the quality of the captured image data. This work explores the use of sensor diversity in the image data within a framework for variational optical flow. In particular, a custom image sensor setup intended for vehicle applications is tested. Experimental results demonstrate the improved flow estimation performance when IR sensitivity or flash illumination is added to the system

On robust optical flow estimation on image sequences with differently exposed frames using primal-dual optimization

Optical flow methods are used to estimate pixelwise motion information based on consecutive frames in image sequences. The image sequences traditionally contain frames that are similarly exposed. However, many real-world scenes contain high dynamic range content that cannot be captured well with a single exposure setting. Such scenes result in certain image regions being over- or underexposed, which can negatively impact the quality of motion estimates in those regions. Motivated by this, we propose to capture high dynamic range scenes using different exposure settings every other frame. A framework for OF estimation on such image sequences is presented, that can straightforwardly integrate techniques from the state-of-the-art in conventional OF methods. Different aspects of robustness of OF methods are discussed, including estimation of large displacements and robustness to natural illumination changes that occur between the frames, and we demonstrate experimentally how to handle such challenging flow estimation scenarios. The flow estimation is formulated as an optimization problem whose solution is obtained using an efficient primal–dual method

Price and investment dynamics: Theory and plant level data

We construct a model of a firm competing for market share in a customer market and making investments in physical capital. The firm is financially constrained and there are implementation lags in investment. Our model predicts that product prices should depend on costs and competitors' prices, but respond weakly to demand shocks. Also, prices should be strongly related to investment. Estimating price and investment equations on panel data for Swedish manufacturing plants we find results which are qualitatively in line with these predictions, though the relation between investment and prices is stronger than predicted by our model

Frequency dependence of speckle in continuous-wave ultrasound with implications for blood perfusion measurements.

Speckle in continuous wave (CW) Doppler has previously been found to cause large variations in detected Doppler power in blood perfusion measurements, where a large number of blood vessels are present in the sample volume. This artifact can be suppressed by using a number of simultaneously transmitted frequencies and averaging the detected signals. To optimize the strategy, statistical properties of speckle in CW ultrasound need to be known. This paper presents analysis of the frequency separation necessary to obtain independent values of the received power for CW ultrasound using a simplified mathematical model for insonation of a static, lossless, statistically homogeneous, weakly scattering medium. Specifically, the autocovariance function for received power is derived, which functionally is the square of the (deterministic) autocorrelation function of the effective sample volumes produced by the transducer pair for varying frequencies, at least if a delta correlated medium is assumed. A marginal broadening of the modeled autocovariance functions is expected for insonation of blood. The theory is applicable to any transducer aperture, but has been experimentally verified here with 5-MHz, 6.35-mm circular transducers using an agar phantom containing small, randomly dispersed glass particles. A similar experimental verification of a transducer used in multiple-frequency blood perfusion measurements shows that the model proposed in this paper is plausible for explaining the decorrelation between different channels in such a measurement