This paper documents the function and use of the Gretl function package VCwrapper.pdf that implements the VC method for estimating time-varying coefficients in linear models as described in Schlicht (2021). It builds on the VCC program by Schlicht (2021a), is easy to use and highly configurable....

We give some Monte Carlo results on the performance of two robust alternatives to least squares regression estimation - least absolute residuals and the one-step "sine" estimator. We show how to scale the residuals for the sine estimator to achieve constant efficiency at the Gaussian across...

I establish the equivalence between the two-way fixed effects (TWFE) estimator and an estimator obtained from a pooled ordinary least squares regression that includes unit-specific time averages and time-period specific cross-sectional averages, which I call the two-way Mundlak (TWM) regression....

At the present time there is no well accepted test for determining whether or not robust regression parameter estimates are significantly different than least squares estimates. Thus. we propose and demonstrate the efficacy of two Wald-like statistical tests for the above purposes using...

This research aims to show the direct and indirect effects of size on the performance of the municipalities of Santa Catarina, due to the academic discussion about the size of the municipality affecting its performance. All municipalities in the state of Santa Catarina, from 2005 to 2016, were...

We provide a method to estimate resource shares - the fraction of total household expenditure allocated to each household member - using OLS estimation of Engel curves. The method is a linear reframing of the nonlinear model of Dunbar, Lewbel and Pendakur (2013), extended to allow single-parent...

Ordinary Least Squares (OLS) estimation of monetary policy rules produces potentially inconsistent estimates of policy parameters. The reason is that central banks react to variables, such as in ation and the output gap, which are endogenous to monetary policy shocks. Endogeneity implies a...

The essence of this study is to investigate the influence of the board gender diversity on firms' accounting and market-based performance using a sample of Standard & Poor's 500 companies belonging to the information technology sector over 12 years. Using the pooled ordinary least squares (OLS)...

This study explored contribution of foreign direct investment (FDI) inflows to the sustainable development of the Nigerian and Ghanaian economies. The investigation was prompted by the apparent evidence of rising FDI inflows in the last two decades, which has failed to improve both nations’...

We use unique survey data on working time reduction during the first wave of the COVID-19 crisis in the spring of 2020 to estimate the number of working hours lost in Germany due to closed schools and child care facilities. Our results indicate that overall, a loss of not more than 1.1 percent...

Official Chinese GDP growth rates have been remarkably smooth over the past decade, in contrast with alternative Chinese economic data. To better identify Chinese business cycles, we construct a sparse partial least squares (PLS) factor from a wide array of Chinese higher-frequency data,...

We examine evidence on omitted-ability bias in estimates of the economic return to schooling, using proxies for unobserved ability. We consider measurement error in these ability proxies and the potential endogeneity of both experience and schooling, and examine wages at labor market entry and...

The paper investigated the nexus between Ghana's single spine pay policy and unemployment. An exploratory sequential mixed design method was employed to collect data from 413 business owners and managers, which comprised manufacturing companies, service industries, wholesalers as well as small...

The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz (2001) is widely used for pricing American options. The LSM estimator contains undesirable look-ahead bias, and the conventional technique of removing it necessitates doubling simulations. We present the...

This paper formulates a weighting function from conventional least squares (LS) and combines it with estimation theory to provide the statistical estimate, expected value, and variance of any point on the polynomial constructed for fitting a set of existing data. This solves the problem of the...

Within a New Zealand business cycle context, we assess whether Hamilton's (H84) OLS regression methodology produces stylised business cycle facts which are materially different from HP1600 measures, and whether using the H84 predictor and other forecast extensions improves the HP filter's...

We explore Okun's coefficient in several Latin American countries for the period from 1995-2017 and compare the results obtained using ordinary least squares (OLS) and generalised maximum entropy (GME) estimators. There are several advantages in considering the GME estimator over traditional...

The purpose of this article to propose a comparative study in estimation methods under regression analysis between adaptive, OLS, M and S estimation.. An example is used here to fulfill the objective. In this example S estimation is more suitable. At the end of the discussion we find that if the...

Economic diversification has been the glamour of successive administrations in Nigeria, especially amidst the dwindling oil-revenue in recent years, which has resulted from the fluctuations in world crude oil prices. This study aims at investigating the impact of diversifying the economy on the...

Individuals may be poor even if their household is not poor, because the intra-household distribution of resources may be unequal. Dunbar, Lewbel and Pendakur (2013) develop a model wherein the resource share of each person in a collective household - defined as their share of total household...

The ordinary least squares (OLS) estimator for spatial autoregressions may be consistent as pointed out by Lee (2002), provided that each spatial unit is influenced aggregately by a significant portion of the total units. This paper presents a unified asymptotic distribution result of the...

In this paper, we test the efficiency of least-squares Monte Carlo method to estimate capital requirements in life insurance. We choose a simplified Gaussian evaluation framework where closed-form formulas are available and allow us to obtain solid benchmarks. Extensive numerical experiments...

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these...

Stock factor returns exhibit greater predictability from the weighted least squares (WLS) estimator of autoregressions with time-varying volatility. The predictability transmits into superior mean-variance optimal portfolio performance that is hardly achieved by other strategies that utilize...

The purpose of this study is to identify how open innovation variables influence the competitive capability of manufacturing small and medium enterprises (SMEs) in a regional territory in Southern Brazil. The research method is a survey of 67 SMEs in Southern Brazil that provided data for an...

Linear instrumental variable estimators, such as two-stage least squares (TSLS), are commonly interpreted as estimating positively weighted averages of causal effects, referred to as local average treatment effects (LATEs). We examine whether the LATE interpretation actually applies to the types...

Multicointegration is traditionally defined as a particular long run relationship among variables in a parametric vector autoregressive model that introduces links between these variables and partial sums of the equilibrium errors. This paper departs from the parametric model, using a...

We adopt the Jackknife Model Averaging (JMA) technique to conduct a meta-regression analysis of 925 renminbi (RMB) misalignment estimates generated by 69 studies. The JMA method accounts for model selection and sampling uncertainties, and allows for non-nested model specifications and...