An approach to nonirreducible MIMO FIR channel equalization

It is known that a nonirreducible multiple-input– multiple-output finite-impulse-response channel driven by colored signals that are mutually uncorrelated and of sufficiently diverse power spectra can be identified blindly by exploiting only the second-order statistics of the measured data. In this brief, we propose an approach to dealing with the equalization of a nonirreducible channel, provided that the estimate of the channel matrix is available. Both zero-forcing and minimum-mean-square-error equalizers are developed to perform the channel equalization. The effectiveness of the approach and equalizers is demonstrated by simulation examples.

A new adaptive blind channel identification algorithm

This paper addresses the blind identification of single-input multiple-output (SIMO) finite-impulse-response (FIR) systems. We first propose a new adaptive algorithm for the blind identification of SIMO FIR systems. Then, its convergence property is analyzed systematically. It is shown that under some mild conditions, the proposed algorithm is guaranteed to converge in the mean to the true channel impulse responses in both noisy and noiseless cases. Simulations are carried out to demonstrate the theoretical results.

An iterative equalization method for nonirreducible MIMO FIR channels

This paper deals with the equalization of a nonirreducible multiple-input multiple-output (MIMO) finite-impulse-response (FIR) channel provided that the estimate of the channel matrix is available. An iterative method is developed to perform the channel equalization. The effectiveness of the proposed equalization method is demonstrated by simulation examples.

Estimation of basis frequencies for time-varying SIMO channels : a second-order method

The complex exponential basis expansion model (CE-BEM) provides an accurate description for the time-varying (TV) channels encountered in mobile communications. Many blind channel identification and equalization approaches based on the CE-BEM require precise knowledge of the basis frequencies of TV channels. Existing methods for basis frequency estimation usually resort to the higher-order statistics of channel outputs and impose strict constraints on the source signal. In this paper, we propose a novel method to estimate the basis frequencies for blind identification and equalization of time-varying single-input multiple-output (SIMO) finite-impulse-response (FIR) channels. The proposed method exploits only the second-order statistics of channel outputs and does not require strong conditions on the source signal. As a result, it exhibits superior performance to the existing basis frequency estimation methods. The validity of our method is demonstrated by numerical simulations.

Impacts of monetary policies on housing affordability in Australia

Housing affordability has become a major policy issue in many countries across the world since the rapid inflation of house prices. This paper empirically investigates how monetary policies affect housing affordability in Australia from 1998 to 2009. Three primary variables associated with the housing sector and monetary policy, which are money supply, interest rates and house prices, are studied for all eight capital cities in Australia in this research. Shocks of such variables are identified by a structural vector autoregression (SVAR) model with restrictions that are consistent with economic theoretical framework. Based upon the analysis using the structural decomposition of impulse response on quarterly data, it can be discovered that the monetary policy plays an active role in housing affordability via adjustments of money supply and interest rates during the observed period in Australia. The empirical results from this research may be used for decision makers to determine money supply and interest rates from the perspective of housing affordability.

Demystifying the supply of capital and labour in a nation’s economic chain

Public capital has been considered to be the wheels of economic activity in a nation or region. The reverse effect, the contribution of economic growth to public capital, is also worth analysis. The non-structural vector auto-regression (VAR) approach is performed for the Australian economy using yearly data for the 1960-2008 period. The optimal lag is investigated to build the VAR model that is then tested for stability. The impulse response function is further employed to examine the response of one economic variable to the innovation of others and to determine the lagged terms for the maximum absolute value of the other variables’ responses. The results will provide historical evidence for the federal and regional governments of Australia to estimate the effects of these production variables, in particular, the effect of infrastructure spending on the gross domestic product.

A factor-augmented VAR approach : the effect of a rise in the US personal income tax rate on the US and Canada

In this paper a factor-augmented vector autoregressive (FAVAR) model is estimated to characterize the dynamic effects of shocks in the personal income tax rate in the United States on United States and Canadian economies. The representation and the estimate of the FAVAR model is based on Stock and Watson (2005) and the shocks are recovered applying the identification scheme proposed by Bernanke et al. (2005); this method allows impulse response functions to be generated for all the variables in the dataset and provides a description of the domestic and international transmission mechanisms of United States movements in the personal income tax rate. A distinguishing feature of our model is the disaggregation of traded goods sector where imports and exports are disaggregated into 12 and 13 industries, respectively. This provides extra information on the domestic and international transmission mechanism across the two countries. The results show that the FAVAR approach generates a reasonable characterisation of the effects of United States movements in the US personal income tax rate on the United States economy and its transmission to the Canadian economy.

CM-based blind equalization of time-varying SIMO-FIR channel with single pulsation estimation

It is known that the constant modulus (CM) property of the source signal can be exploited to blindly equalize time-invariant single-inputmultiple-output (SIMO) and finite-impulse-response (FIR) channels. However, the time-invariance assumption about the channel cannot be satisfied in several practical applications, e.g., mobile communication. In this paper, we show that, under some mild conditions, the CM criterion can be extended to the blind equalization of a time-varying channel that is described by the complex exponential basis expansion model (CE-BEM). Although several existing blind equalization methods that are based on the CE-BEM have to employ higher order statistics to estimate all nonzero channel pulsations, the CM-based method only needs to estimate one pulsation using second-order statistics, which yields better estimation results. It also relaxes the restriction on the source signal and is applicable to some classes of signals with which the existing methods cannot deal.

House price bubble estimations in Australia's capital cities with market fundamentals

This paper investigates the existence of house price bubbles in Australia's eight capital cities in recent years by using quantitative analyses including Johansen cointegration test, Granger causality test, impulse response and Chow forecast test. While interactions between house prices and market fundamentals are discussed in long-run and causal estimations, shocks from the market fundamentals to house prices are investigated in generalized impulse response analyses. Findings from estimating house price bubbles for eight capital cities suggest that there was an obvious house price bubble in Perth, while a slight house price bubble occurred in Sydney. In contrast, house prices in Adelaide and Darwin can be explained very well by market fundamentals, while house prices in Melbourne, Brisbane, Hobart and Canberra were undervalued in the study period.

Investigating the housing market fluctuation under an expansionary monetary policy in Australia

This research empirically investigates the impact of monetary policy on the housing market in Australia from 1996 to 2009. Three primary variables associated with the housing sector and monetary policy, including interest rates, money supply and house prices, are estimated by a structural vector autoregression (VAR) model. Depending upon the analysis using the impulse response function, it can be identified that monetary policy significantly affects the housing market in Australia by the adjustments in interest rates and money supply. The empirical results from this study may be useful for policy makers to enact appropriate policies in relation to the infrastructure planning.

Ripple effects of house prices : considering spatial correlations in geography and demography

Purpose: Studies into ripple effects have previously focused on the interconnections between house price movements across cities over space and time. These interconnections were widely investigated in previous research using vector autoregression models. However, the effects generated from spatial information could not be captured by conventional vector autoregression models. This research aimed to incorporate spatial lags into a vector autoregression model to illustrate spatial-temporal interconnections between house price movements across the Australian capital cities. Design/methodology/approach: Geographic and demographic correlations were captured by assessing geographic distances and demographic structures between each pair of cities, respectively. Development scales of the housing market were also used to adjust spatial weights. Impulse response functions based on the estimated SpVAR model were further carried out to illustrate the ripple effects. Findings: The results confirmed spatial correlations exist in housing price dynamics in the Australian capital cities. The spatial correlations are dependent more on the geographic rather than the demographic information. Originality/value: This research investigated the spatial heterogeneity and autocorrelations of regional house prices within the context of demographic and geographic information. A spatial vector autoregression model was developed based on the demographic and geographic distance. The temporal and spatial effects on house prices in Australian capital cities were then depicted.

A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities

Purpose - A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process. Design/methodology/approach - Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices. Findings - The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin. Originality/value - This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.