This thesis examines the difference between the extracted cyclical components of some macroeconomic time series using four popular detrending methods HP, BK, CF and FOD. We use different approaches to compare their differences. A standard examination of the cyclical component is applied. We also take a frequency domain approach and examine the sample spectra for each cycle. Moreover, impulse responses and the correlation between the cyclical components extracted by each detrending method are studied. We conclude that for quarterly data HP, BK and CF produce similar cycles. However, when considering annual data the HP diverges from the other filters. The FOD extracts cycles that are not similar to those of the other three examined filters.

No description available.

During this project three distinct methods to improve the data processing of Controlled Source Audio Magnetotellurics (CSAMT) data are implemented and their advantages and disadvantages are discussed. The methods in question are: Detrending the time series in the time domain, instead of detrending in the frequencydomain. Implementation of a coherency test to pinpoint data segments of low quality andremove these data from the calculations. Implementing a method to detect and remove transients from the time series toreduce background noise in the frequency spectra. Both the detrending in time domain and the transient removal shows potential in improvingdata quality even if the improvements are small(both in the (1-10% range). Due totechnical limitations no coherency test was implemented. Overall the processes discussedin the report did improve the data quality and may serve as groundwork for further improvementsto come. / Projektet behandlar tre stycken metoder för att förbättra signalkvaliten hos Controlled Source Audio Magnetotellurics (CSAMT) data, dessa implementeras och deras för- och nackdelar diskuteras. Metoderna som hanteras är: Avlägsnandet av trender från tidsserier i tidsdomänen istället för i frekvensdomänen. Implementationen av ett koherenstest för att identifiera ”dåliga” datasegment ochavlägsna dessa från vidare beräkningar. Implementationen av en metod för att både hitta och avlägsna transienter (dataspikar) från tidsserien för att minska bakgrundsbruset i frekvensspektrat. Både avlägsnandet av trender samt transienter visar positiv inverkan på datakvaliteten,även om skillnaderna är relativt små (båda på ungefär 1-10%). På grund av begränsningarfrån mätdatan kunde inget meningsfullt koherenstest utformas. Överlag har processernasom diskuteras i rapporten förbättrat datakvaliten och kan ses som ett grundarbete förfortsatta förbättringar inom området.

No description available.

Pinus mugo Turra (sensu lato) is a prostrate shrub growing above the alpine timberline in the mountain ranges of Southern, Central and Eastern Europe. It is well adapted to the harsh alpine environment creating different mechanisms for survival. The research was carried out in Krkonoše Mountains on base of Pinus mugo individuals compared with Picea abies trees. Samples from shrubs were gathered using serial sectioning from four sites in different elevations located on Sněžka Mountain and Smogornia Ridge. Further analysis of the sampled material was made with the help of different detrending methods to see which method will return the best growth response to climate parameters. RCS detrending and detrending via simple averaging reflected the best climate signal contained in dwarf pine chronologies. The advantage of these methods is based on their ability to reflect growth conditions of the particular site. Upper sites showed significant correlations with temperatures of the growing season, while on lower sites the signal was quite unclear. The amount of precipitation plays the significant role on shrub growth during the vegetation period (especially July month) and early spring when water is very important for growth initiation. Upper Pinus mugo sites showed high sensitivity to droughts, especially...

We use a simulation model to generate tree-ring like data with systematic growth forcings and subject it to two methods of standardization: Regional Curve Standardization (RCS) and Negative Exponential Curve Standardization (NECS). The coherency between very low frequency forcings (hundreds of years) and the chronologies was higher when RCS was used to detrend the component series. There was no difference between standardization methods at decadal or annual time scales. We found that the detectability of systematic forcings was heavily dependent on amplitude and wavelength of the input signal as well as the number of trees simulated. These results imply that for very long tree-ring chronologies where the analyst is interested in low-frequency variability, RCS is a better method for detrending series if the requirements for that method can be met. However, in the majority of situations NECS is an acceptable detrending method. Most critically, we found that multi-centennial signals can be recovered using both methods.

Heteroscedasticity (or time-dependent volatility) in economic and financial time series has been recognized for decades. Still, heteroscedasticity is surprisingly often neglected by practitioners and researchers. This may lead to inefficient procedures. Much of the work in this thesis is about finding more effective ways to deal with heteroscedasticity in economic and financial data. Paper I suggest a filter that, unlike the Box-Cox transformation, does not assume that the heteroscedasticity is a power of the expected level of the series. This is achieved by dividing the time series by a moving average of its standard deviations smoothed by a Hodrick-Prescott filter. It is shown that the filter does not colour white noise. An appropriate removal of heteroscedasticity allows more effective analyses of heteroscedastic time series. A few examples are presented in Paper II, III and IV of this thesis. Removing the heteroscedasticity using the proposed filter enables efficient estimation of the underlying probability distribution of economic growth. It is shown that the mixed Normal - Asymmetric Laplace (NAL) distributional fit is superior to the alternatives. This distribution represents a Schumpeterian model of growth, the driving mechanism of which is Poisson (Aghion and Howitt, 1992) distributed innovations. This distribution is flexible and has not been used before in this context. Another way of circumventing strong heteroscedasticity in the Dow Jones stock index is to divide the data into volatility groups using the procedure described in Paper III. For each such group, the most accurate probability distribution is searched for and is used in density forecasting. Interestingly, the NAL distribution fits best also here. This could hint at a new analogy between the financial sphere and the real economy, further investigated in Paper IV. These series are typically heteroscedastic, making standard detrending procedures, such as Hodrick-Prescott or Baxter-King, inadequate. Prior to this comovement study, the univariate and bivariate frequency domain results from these filters are compared to the filter proposed in Paper I. The effect of often neglected heteroscedasticity may thus be studied.

The aim of this paper is to investigate various versions of the Phillips curve and their inflation forecasting ability for Euro Area. We consider autoregressive distributed lag models and use two types of trend estimation -- successive (the trend is estimated before the remaining parameters are) and join, using exponential smoothing. The versions of the Phillips curve are evaluated by rolling and recursive window methods, various selection criteria for lag variables and different combination of the inflation indicators. To evaluate the forecasted values, we calculate the RMSE in three 7-year periods: 1993-1999 (run up Euro area), 2000-2006 (stable inflation period) and 2007-2013 (financial crisis). According to all our modifications, we find some models which achieve satisfying results in terms of the RMSE, albeit not for all forecasting periods. We notice that some models are satisfactory only in the stable period however not in the periods with low inflation and vice versa.