1,721,075 research outputs found
The forward looking information content of equity and bond markets for aggregate investments
No full textThe literature on aggregate investment has recently shifted attention away from the stock market in favor of the bond market as a consequence of the disappointing empirical results of stock market’s Q and the ability of credit spreads to forecast investment and output growth. In this paper we examine the different information content of Tobin’s Q and corporate bond spread for aggregate investments in the US by means of wavelet analysis. The evidence shows that equity and bond markets’ information contents are complementary each other rather than alternative. In particular, a progressive shift in the respective contributions of stock market’s Q and the relative price of corporate bonds for aggregate investments emerges when moving from higher to lower scales, the contribution of stock market’s Q being predominant at higher scales, whereas that of the relative price of corporate bonds has a tendency to increase as the time scale decreases
Data reduction by the Haar function: A case study of the Phillips Curve
Full text linkThe unorthodox estimation procedure, which Phillips (1958) adopted in his original paper, is examined using the Haar wavelet filter. The application of the Haar wavelet transform to Phillips' original data shows that Phillips' six pairs of mean coordinates display a striking similarity with the Haar scaling coefficients that represent averages with a period greater than 16 years. This is consistent with Desai's (1975) intuition on the interpretation of the Phillips Curve. We show that the choice of sorting observations by ascending values of the unemployment rate is crucial for reaching the goal of estimating the eye-catching nonlinear hyperbolic shape of the wage-unemployment relationship that would be otherwise linear. Interestingly, the Haar filter can account not only for the facts characterizing the Phillips' relationship up to the early 1960s but also for two important facts mostly debated among policymakers: the downward shift of the Phillips Curve and its flattening over time
Interest rate spreads and output: A time scale decomposition analysis using wavelets
No full textThe information content of several interest rate spreads for future output growth is analyzed using wavelet analysis. The ‘‘scale-by-scale’’ regression analysis shows that
standard indicators of the stance of monetary policy, such as the shape of the yield curve,
the real federal funds rate, and the credit spread have different information content for
future output at different time frames.
This is consistent with the idea that allowing
for different time scales of variation in the data can provide a deeper understanding of the complex dynamics between real and financial variables, certainly richer than those
obtainable using standard aggregate regression methods
INSTRUMENTAL VARIABLES AND WAVELET ANALYSIS
No full textThe application of wavelet analysis provides an orthogonal decomposition of a time series by time scale,
thereby facilitating the decomposition of a data series into the sum of a structural component and a random
error component. The structural components revealed by the wavelet analysis yield nearly ideal
instrumental variables for variables observed with error and for co-endogenous variables in simultaneous
equation models. Wavelets also provide an efficient way to explore the path of the structural component of
the series to be analyzed and can be used to detect some specification errors. The methodology described in
this paper is applied to the errors in variables problem and simultaneous equations case using some
simulation exercises and to the analysis of a version of the Phillips curve with interesting results
Time Scale Analysis of Interest Rate Spreads and Output Using Wavelets
No full textThis paper adds to the literature on the information content of different spreads for real activity by explicitly taking into account the time scale relationship between a variety of monetary and financial indicators (real interest rate, term and credit spreads) and output growth. By means of wavelet-based exploratory data analysis we obtain richer results relative to the aggregate analysis by identifying the dominant scales of variation in the data and the scales and location at which structural breaks have occurred. Moreover, using the “double residuals” regression analysis on a scale-by-scale basis, we find that changes in the spread in several markets have different information content for output at different time frames. This is consistent with the idea that allowing for different time scales of variation in the data can provide a fruitful understanding of the complex dynamics of economic relationships between variables with non-stationary or transient components, certainly richer than those obtained using standard time domain methods
Does Productivity Affect Unemployment? A Time-Frequency Analysis for the US
No full textThe effect of increased productivity on unemployment has long been disputed both theoretically and empirically. Although economists mostly agree on the long run positive effects of labor productivity, there is still much disagreement
over the issue as to whether productivity growth is good or bad for employment in the short run. Does productivity growth increase or reduce unemployment?
This paper try to answer this question by using the property of wavelet analysis to decompose economic time series into their time scale components, each associated to a specific frequency range. We decompose the relevant US time series data in different time scale components and consider
co-movements of productivity
and unemployment over different time horizons.
In a nutshell, we conclude that, according to US post-war data, productivity creates unemployment in the short and
medium terms, but employment in the long run
The decomposition of the inflation-unemployment relationship by time scale using wavelets
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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